sequencing-methods-review

3
Sequencing Methods Review
A review of publications featuring Illumina®
Technology
DNASE-SEQ
PAR-CLIP-SEQ
MEDIP-SEQ
GRO-SEQ
CHIA-PET
HITS-CLIP
HI-C/3-CTRAP-SEQ
RC-SEQ
FRAG-SEQ
MBDCAP-SEQ
PARE-SEQ/GMUT
ICLIP
DIGITAL
MERIP-SEQ ATAC-SEQ
CIP-TAP
BS-SEQ
CHIP-SEQ
RIBO-SEQ
MAINE-SEQ
CLASH-SEQ
5-C
4-C
TC-SEQ
NET-SEQ
UMI
CAP-SEQ
FAIRE-SEQ
DUPLEX-SEQ
SMMIP
OXBS-SEQ
RIP-SEQ
TIF-SEQ/PEAT
IN-SEQ TAB-SEQ
MDA
SHAPE-SEQ
PARS-SEQ
MALBAC
CHIRP-SEQ
RNA-SEQ
RRBS-SEQICE
OS-SEQ

2
TABLE OF CONTENTS
Table of Contents 2
Introduction 4
RNA Transcription 5
Chromatin Isolation by RNA Purification (ChIRP-Seq) 7
Global Run-on Sequencing (GRO-Seq) 9
Ribosome Profiling Sequencing (Ribo-Seq)/ARTseq™
12
RNA Immunoprecipitation Sequencing (RIP-Seq) 15
High-Throughput Sequencing of CLIP cDNA library (HITS-CLIP) or 17
Crosslinking and Immunoprecipitation Sequencing (CLIP-Seq) 17
Photoactivatable Ribonucleoside–Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP) 19
Individual Nucleotide Resolution CLIP (iCLIP) 22
Native Elongating Transcript Sequencing (NET-Seq) 24
Targeted Purification of Polysomal mRNA (TRAP-Seq) 25
Crosslinking, Ligation, and Sequencing of Hybrids (CLASH-Seq) 26
Parallel Analysis of RNA Ends Sequencing (PARE-Seq) or 27
Genome-Wide Mapping of Uncapped Transcripts (GMUCT) 27
Transcript Isoform Sequencing (TIF-Seq) or 29
Paired-End Analysis of TSSs (PEAT) 29
RNA Structure 30
Selective 2’-Hydroxyl Acylation Analyzed by Primer Extension Sequencing (SHAPE-Seq) 31
Parallel Analysis of RNA Structure (PARS-Seq) 32
Fragmentation Sequencing (FRAG-Seq) 33
CXXC Affinity Purification Sequencing (CAP-Seq) 34
Alkaline Phosphatase, Calf Intestine-Tobacco Acid Pyrophosphatase Sequencing (CIP-TAP) 36
Inosine Chemical Erasing Sequencing (ICE) 38
m6A-Specific Methylated RNA Immunoprecipitation Sequencing (MeRIP-Seq) 39
Low-Level RNA Detection 40
Digital RNA Sequencing 42
Whole-Transcript Amplification for Single Cells (Quartz-Seq) 43
Designed Primer–Based RNA Sequencing (DP-Seq) 44
Switch Mechanism at the 5’ End of RNA Templates (Smart-Seq) 45
Switch Mechanism at the 5’ End of RNA Templates Version 2 (Smart-Seq2) 47
Unique Molecular Identifiers (UMI) 49
Cell Expression by Linear Amplification Sequencing (CEL-Seq) 51
Single-Cell Tagged Reverse Transcription Sequencing (STRT-Seq) 52
Low-Level DNA Detection 53
Single-Molecule Molecular Inversion Probes (smMIP) 55
Multiple Displacement Amplification (MDA) 56
Multiple Annealing and Looping–Based Amplification Cycles (MALBAC) 59
Oligonucleotide-Selective Sequencing (OS-Seq) 61
Duplex Sequencing (Duplex-Seq) 62
DNA Methylation 63
Bisulfite Sequencing (BS-Seq) 65
Post-Bisulfite Adapter Tagging (PBAT) 70
Tagmentation-Based Whole Genome Bisulfite Sequencing (T-WGBS) 72
Oxidative Bisulfite Sequencing (oxBS-Seq) 73

3
Tet-Assisted Bisulfite Sequencing (TAB-Seq) 74
Methylated DNA Immunoprecipitation Sequencing (MeDIP-Seq) 76
Methylation-Capture (MethylCap) Sequencing or 79
Methyl-Binding-Domain–Capture (MBDCap) Sequencing 79
Reduced-Representation Bisulfite Sequencing (RRBS-Seq) 81
DNA-Protein Interactions 83
DNase l Hypersensitive Sites Sequencing (DNase-Seq) 85
MNase-Assisted Isolation of Nucleosomes Sequencing (MAINE-Seq) 88
Chromatin Immunoprecipitation Sequencing (ChIP-Seq) 91
Formaldehyde-Assisted Isolation of Regulatory Elements (FAIRE-Seq) 94
Assay for Transposase-Accessible Chromatin Sequencing (ATAC-Seq) 96
Chromatin Interaction Analysis by Paired-End Tag Sequencing (ChIA-PET) 97
Chromatin Conformation Capture (Hi-C/3C-Seq) 99
Circular Chromatin Conformation Capture (4-C or 4C-Seq) 101
Chromatin Conformation Capture Carbon Copy (5-C) 104
Sequence Rearrangements 105
Retrotransposon Capture Sequencing (RC-Seq) 107
Transposon Sequencing (Tn-Seq) or Insertion Sequencing (INSeq) 109
Translocation-Capture Sequencing (TC-Seq) 111
Bibliography 113
Appendix 131
DNA/RNA Purification Kits 131
DNA Sequencing 133
RNA Sequencing 168
Arrays 190
PCR and Enzyme Solutions 194
Instruments 198

4
INTRODUCTION
This collection of next-generation sequencing (NGS) sample preparation protocols was compiled from the scientific literature to demonstrate the
wide range of scientific questions that can be addressed by Illumina’s sequencing by synthesis technology. It is both a tribute to the creativity of
the users and the versatility of the technology. We hope it will inspire researchers to use these methods or to develop new ones to address new
scientific challenges.
These methods were developed by users, so readers should refer to the original publications for detailed descriptions and protocols.
Have we missed anything? Please contact us if you are aware of a protocol that should be listed.

5
RNA TRANSCRIPTION
The regulation of RNA transcription and processing directly affects protein synthesis. Proteins, in turn, mediate cellular functions to establish the
phenotype of the cell. Dysregulated RNAs are the cause for some diseases and cancers1,2
. Sequencing RNA provides information about both the
abundance and sequence of the RNA molecules. Careful analysis of the results, along with adaptation of the sample preparation protocols, can
provide remarkable insight into all the various aspects of RNA processing and control of transcription. Examples of these measures include:
post-translational modifications, RNA splicing, RNA bound to RNA binding proteins (RBP), RNA expressed at various stages, unique RNA
isoforms, RNA degradation, and regulation of other RNA species3,4
. Studies of RNA transcription and translation are leading to a better
understanding of the implications of RNA production, processing, and regulation for cellular phenotype.
1 Kloosterman W. P. and Plasterk R. H. (2006) The diverse functions of microRNAs in animal development and disease. Dev Cell 11: 441-450
2 Castello A., Fischer B., Hentze M. W. and Preiss T. (2013) RNA-binding proteins in Mendelian disease. Trends Genet 29: 318-327
3 McGettigan P. A. (2013) Transcriptomics in the RNA-seq era. Curr Opin Chem Biol 17: 4-11
4 Feng H., Qin Z. and Zhang X. (2013) Opportunities and methods for studying alternative splicing in cancer with RNA-Seq. Cancer Lett 340: 179-191
5 Davis H. P. and Squire L. R. (1984) Protein synthesis and memory: a review. Psychol Bull 96: 518-559
6 Holt C. E. and Schuman E. M. (2013) The central dogma decentralized: new perspectives on RNA function and local translation in neurons. Neuron 80: 648-657
Scientists have discovered a link between long term memory and protein synthesis in brain5,6
.

6
Reviews
Castello A., Fischer B., Hentze M. W. and Preiss T. (2013) RNA-binding proteins in Mendelian disease. Trends Genet 29: 318-327
Feng H., Qin Z. and Zhang X. (2013) Opportunities and methods for studying alternative splicing in cancer with RNA-Seq.
Cancer Lett 340: 179-191
Holt C. E. and Schuman E. M. (2013) The central dogma decentralized: new perspectives on RNA function and local translation in neurons.
Neuron 80: 648-657
Law G. L., Korth M. J., Benecke A. G. and Katze M. G. (2013) Systems virology: host-directed approaches ]to viral pathogenesis and drug
targeting. Nat Rev Microbiol 11: 455-466
Licatalosi D. D. and Darnell R. B. (2010) RNA processing and its regulation: global insights into biological networks. Nat Rev Genet11: 75-87

7
CHROMATIN ISOLATION BY RNA PURIFICATION (CHIRP-SEQ)
Chromatin isolation by RNA purification (ChIRP-Seq) is a protocol to detect the locations on the genome where non-coding RNAs (ncRNAs), such
as long non-coding RNAs (lncRNAs), and their proteins are bound7
. In this method, samples are first crosslinked and sonicated. Biotinylated tiling
oligos are hybridized to the RNAs of interest, and the complexes are captured with streptavidin magnetic beads. After treatment with RNase H the
DNA is extracted and sequenced. With deep sequencing the lncRNA/protein interaction site can be determined at single-base resolution.
Pros Cons
• Binding sites can be found anywhere on the genome
• New binding sites can be discovered
• Specific RNAs of interest can be selected
• Nonspecific oligo interactions can lead to misinterpretation
of binding sites
• Chromatin can be disrupted during the preparation stage
• The sequence of the RNA of interest must be known
References
Li Z., Chao T. C., Chang K. Y., Lin N., Patil V. S., et al. (2014) The long noncoding RNA THRIL regulates TNFalpha expression through its
interaction with hnRNPL. Proc Natl Acad Sci U S A 111: 1002-1007
The non-protein–coding parts of the mammalian genome encode thousands of large intergenic non-coding RNAs (lincRNAs). To identify
lincRNAs associated with activation of the innate immune response, this study applied custom microarrays and Illumina RNA sequencing
for THP1 macrophages. A panel of 159 lincRNAs was found to be differentially expressed following innate activation. Further analysis of the
RNA-Seq data revealed that linc1992 was required for expression of many immune-response genes, including cytokines and regulators of
TNF-alpha expression.
Illumina Technology: HiSeq 2000®
7 Chu C., Qu K., Zhong F. L., Artandi S. E. and Chang H. Y. (2011) Genomic maps of long noncoding RNA occupancy reveal principles of RNA-chromatin interactions. Mol Cell 44: 667-678

8
Li W., Notani D., Ma Q., Tanasa B., Nunez E., et al. (2013) Functional roles of enhancer RNAs for oestrogen-dependent transcriptional
activation. Nature 498: 516-520
Enhancers are regions of DNA with regulatory function. Through binding of transcription factors and cis-interactions with promoters, target
gene expression may be increased. In addition, both lncRNAs and bidirectional ncRNAs may be transcribed on enhancers and are referred
to as enhancer RNAs (eRNAs). This study examined eRNA expression in breast cancer cells using a combination of sequencing protocols
on HiSeq 2000 (ChIRP-seq, GRO-seq, ChIP-Seq, 3C, 3D-DSL) to discover a global increase in eRNA transcription on enhancers adjacent to
E2-upregulated coding genes. These data suggest that eRNAs may play an important role in transcriptional regulation.
Illumina Technology: HiSeq 2000
Chu C., Qu K., Zhong F. L., Artandi S. E. and Chang H. Y. (2011) Genomic maps of long noncoding RNA occupancy reveal principles of
RNA-chromatin interactions. Mol Cell 44: 667-678
Associated Kits
ScriptSeq™
Complete Kit
TruSeq®
RNA Sample Prep Kit
TruSeq®
Small RNA Sample Prep Kit

9
GLOBAL RUN-ON SEQUENCING (GRO-SEQ)
Br-UTP
N
O
O
OH OH
NH
O
O
O
O
O
PO
O
O
PHO
O
O
P
Br
Global run-on sequencing (GRO-Seq) maps binding sites of transcriptionally active RNA polymerase II . In this method, active RNA polymerase
II8
is allowed to run on in the presence of Br-UTP. RNAs are hydrolyzed and purified using beads coated with Brd-UTP antibody. The eluted RNA
undergoes cap removal and end repair prior to reverse transcription to cDNA. Deep sequencing of the cDNA provides sequences of RNAs that are
actively transcribed by RNA polymerase II.
Pros Cons
• Maps position of transcriptionally-engaged RNA polymerases
• Determines relative activity of transcription sites
• Detects sense and antisense transcription
• Detects transcription anywhere on the genome
• No prior knowledge of transcription sites is needed
• The protocol is limited to cell cultures and other artificial systems
due to the requirement for incubation in the presence of
labeled nucleotides
• Artifacts may be introduced during the preparation of the nuclei9
• New initiation events may occur during the run-on step
• Physical impediments may block the polymerases
References
Heinz S., Romanoski C. E., Benner C., Allison K. A., Kaikkonen M. U., et al. (2013) Effect of natural genetic variation on enhancer selection
and function. Nature 503: 487-492
Previous work in epigenetics has proposed a model where lineage-determining transcription factors (LDTF) collaboratively compete with
nucleosomes to bind DNA in a cell type–specific manner. In order to determine the sequence variants that guide transcription factor binding,
the authors of this paper tested this model in vivo by comparing the SNPs that disrupted transcription factor binding sites in two inbred
mouse strains. The authors used GRO-seq in combination with ChIP-seq and RNA-Seq to determine expression and transcription factor
binding. The SNPs of the two strains were then classified based on their ability to perturb transcription factor binding and the authors found
substantial evidence to support the model.
Illumina Technology: TruSeq RNA Sample Prep Kit, HiSeq 2000
8 Core L. J., Waterfall J. J. and Lis J. T. (2008) Nascent RNA sequencing reveals widespread pausing and divergent initiation at human promoters. Science 322: 1845-1848
9 Adelman K. and Lis J. T. (2012) Promoter-proximal pausing of RNA polymerase II: emerging roles in metazoans. Nat Rev Genet 13: 720-731

10
Jin F., Li Y., Dixon J. R., Selvaraj S., Ye Z., et al. (2013) A high-resolution map of the three-dimensional chromatin interactome in human cells.
Nature 503: 290-294
Cis-acting regulatory elements in the genome interact with their target gene promoter by transcription factors, bringing the two locations
close together in the 3D conformation of the chromatin. In this study the chromosome conformation is examined by a genome-wide
analysis method (Hi-C) using the Illumina HiSeq 2000 system. The authors determined over one million long-range chromatin interactions
in humanfibroblasts. In addition, they characterized the dynamics of promoter-enhancer contacts after TNF-alpha signaling and discovered
pre-existing chromatin looping with the TNF-alpha–responsive enhancers, suggesting the three-dimensional chromatin conformation may be
stable over time.
Kaikkonen M. U., Spann N. J., Heinz S., Romanoski C. E., Allison K. A., et al. (2013) Remodeling of the enhancer landscape during
macrophage activation is coupled to enhancer transcription. Mol Cell 51: 310-325
Enhancers have been shown to specifically bind lineage-determining transcription factors in a cell-type–specific manner. Toll-like receptor 4
(TLR4) signaling primarily regulates macrophage gene expression through a pre-existing enhancer landscape. In this study the authors used
GRO-seq and ChIP-seq to discover that enhancer transcription precedes local mono- and dimethylation of histone H3 lysine 4 (H3K4).
Illumina Technology: Genome AnalyzerIIx
®
Kim Y. J., Greer C. B., Cecchini K. R., Harris L. N., Tuck D. P., et al. (2013) HDAC inhibitors induce transcriptional repression of high copy
number genes in breast cancer through elongation blockade. Oncogene 32: 2828-2835
Histone deacetylase inhibitors (HDACI) are a promising class of cancer-repressing drugs. This study investigated the molecular mechanism
of HDACI by using GRO-seq in combination with expression analysis. The authors show that HDACI preferentially represses transcription
of highly expressed genes which, in cancers, are typically misregulated oncogenes supporting further development of HDACI as a general
cancer inhibitor.
Illumina Technology: Genome AnalyzerIIx, Human Gene Expression—BeadArray; 35 bp reads
Li W., Notani D., Ma Q., Tanasa B., Nunez E., et al. (2013) Functional roles of enhancer RNAs for oestrogen-dependent transcriptional activa-
tion. Nature 498: 516-520
Enhancers are regions of DNA with regulatory function. Through binding of transcription factors and cis-interactions with promoters, target
gene expression may be increased. In addition, both lncRNAs and bidirectional ncRNAs may be transcribed on enhancers and are referred
to as enhancer RNAs (eRNAs). This study examined eRNA expression in breast cancer cells using a combination of sequencing protocols
on HiSeq 2000 (ChIRP-seq, GRO-seq, ChIP-Seq, 3C, 3D-DSL) to discover a global increase in eRNA transcription on enhancers adjacent to
E2-upregulated coding genes. These data suggest that eRNAs may play an important role in transcriptional regulation.

11
Saunders A., Core L. J., Sutcliffe C., Lis J. T. and Ashe H. L. (2013) Extensive polymerase pausing during Drosophila axis patterning enables
high-level and pliable transcription. Genes Dev 27: 1146-1158
Drosophila embryogenesis has been intensively studied for the expression patterns of genes corresponding to differentiation of embryonal
tissue. In this study, gene regulation was examined using GRO-seq to map the details of RNA polymerase distribution over the genome
during early embryogenesis. The authors found that certain groups of genes were more highly paused than others, and that bone
morphogenetic protein (BMP) target gene expression requires the pause-inducing negative elongation factor complex (NELF).
Ji X., Zhou Y., Pandit S., Huang J., Li H., et al. (2013) SR proteins collaborate with 7SK and promoter-associated nascent RNA to release
paused polymerase. Cell 153: 855-868
Lam M. T., Cho H., Lesch H. P., Gosselin D., Heinz S., et al. (2013) Rev-Erbs repress macrophage gene expression by inhibiting enhancer-
directed transcription. Nature 498: 511-515
Li P., Spann N. J., Kaikkonen M. U., Lu M., Oh da Y., et al. (2013) NCoR repression of LXRs restricts macrophage biosynthesis of insulin-
sensitizing omega 3 fatty acids. Cell 155: 200-214
Chopra V. S., Hendrix D. A., Core L. J., Tsui C., Lis J. T., et al. (2011) The Polycomb Group Mutant esc Leads to Augmented Levels of Paused
Pol II in the Drosophila Embryo. Mol Cell 42: 837-844
Hah N., Danko C. G., Core L., Waterfall J. J., Siepel A., et al. (2011) A rapid, extensive, and transient transcriptional response to estrogen
signaling in breast cancer cells. Cell 145: 622-634
Larschan E., Bishop E. P., Kharchenko P. V., Core L. J., Lis J. T., et al. (2011) X chromosome dosage compensation via enhanced transcriptional
elongation in Drosophila. Nature 471: 115-118
Wang D., Garcia-Bassets I., Benner C., Li W., Su X., et al. (2011) Reprogramming transcription by distinct classes of enhancers functionally
defined by eRNA. Nature 474: 390-394
Core L. J., Waterfall J. J. and Lis J. T. (2008) Nascent RNA sequencing reveals widespread pausing and divergent initiation at human promoters.
Science 322: 1845-1848
Associated Kits
ScriptSeq™
Complete Kit
TruSeq RNA Sample Prep Kit
TruSeq Small RNA Sample Prep Kit
TruSeq Stranded mRNA®
and Total RNA®
Sample Preparation Kit
TruSeq Targeted RNA®
Expression Kit

12
RIBOSOME PROFILING SEQUENCING (RIBO-SEQ)/ARTSEQ™
RNase digestion RNA extractionRibosome rRNA depletion cDNAReverse transcriptionRNA extraction
RNA
rRNA
RNA
rRNA
RNA
Active mRNA Translation Sequencing (ARTseq), also called ribosome profiling (Ribo-Seq), isolates RNA that is being processed by the ribosome in
order to monitor the translation process10
. In this method ribosome-bound RNA first undergoes digestion. The RNA is then extracted and the rRNA
is depleted. Extracted RNA is reverse-transcribed to cDNA. Deep sequencing of the cDNA provides the sequences of RNAs bound by ribosomes
during translation. This method has been refined to improve the quality and quantitative nature of the results. Careful attention should be paid to:
(1) generation of cell extracts in which ribosomes have been faithfully halted along the mRNA they are translating in vivo; (2) nuclease digestion of
RNAs that are not protected by the ribosome followed by recovery of the ribosome-protected mRNA fragments; (3) quantitative conversion of the
protected RNA fragments into a DNA library that can be analyzed by deep sequencing11
. The addition of harringtonine (an alkaloid that inhibits
protein biosynthesis) causes ribosomes to accumulate precisely at initiation codons and assists in their detection.
Pros Cons
• Reveals a snapshot with the precise location of ribosomes on
the RNA
• Ribosome profiling more closely reflects the rate of protein
synthesis than mRNA levels
• No prior knowledge of the RNA or ORFs is required
• The whole genome is surveyed
• Can be used to identify protein-coding regions
• Initiation from multiple sites within a single transcript makes it
challenging to define all ORFs
• Does not provide the kinetics of translational elongation
References
Becker A. H., Oh E., Weissman J. S., Kramer G. and Bukau B. (2013) Selective ribosome profiling as a tool for studying the interaction of
chaperones and targeting factors with nascent polypeptide chains and ribosomes. Nat Protoc 8: 2212-2239
A plethora of factors is involved in the maturation of newly synthesized proteins, including chaperones, membrane targeting factors, and
enzymes. This paper presents an assay for selective ribosome profiling (SeRP) to determine the interaction of factors with ribosome-nascent
chain complexes (RNCs). The protocol is based on Illumina sequencing of ribosome-bound mRNA fragments combined with selection for
RNCs associated with the factor of interest.
10 Ingolia N. T., Ghaemmaghami S., Newman J. R. and Weissman J. S. (2009) Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324: 218-223
11 IIngolia N. T., Lareau L. F. and Weissman J. S. (2011) Ribosome Profiling of Mouse Embryonic Stem Cells Reveals the Complexity and Dynamics of Mammalian Proteomes. Cell 147: 789-802

13
Lee M. T., Bonneau A. R., Takacs C. M., Bazzini A. A., DiVito K. R., et al. (2013) Nanog, Pou5f1 and SoxB1 activate zygotic gene expression
during the maternal-to-zygotic transition. Nature 503: 360-364
In the developmental transition from egg to zygote, the fertilized egg must clear maternal mRNAs and initiate the zygote development
program—the zygotic genome activation (ZGA). In this paper, the ZGA was studied in zebrafish using Illumina sequencing to determine the
factors that activate the zygotic program. Using a combination of ribosome profiling and mRNA sequencing, the authors identified several
hundred genes directly activated by maternal factors, constituting the first wave of zygotic transcription.
Illumina Technology: HiSeq 2000/2500
Stumpf C. R., Moreno M. V., Olshen A. B., Taylor B. S. and Ruggero D. (2013) The translational landscape of the Mammalian cell cycle. Mol
Cell 52: 574-582
The regulation of gene expression accounts for the differences seen between different cell types and tissues that share the same genomic
information. Regulation may vary over time, and the mechanism and extent is still poorly understood. This study applied Illumina HiSeq
technology to sequence total mRNA and total ribosome-occupied mRNA throughout the cell cycle of synchronized HeLa cells to study
the translational regulation by ribosome occupancy. The authors identified a large number of mRNAs that undergo significant changes in
translation between phases of the cell cycle, and they found 112 mRNAs that were translationally regulated exclusively between specific
phases of the cell cycle. The authors suggest translational regulation is a particularly well-suited mechanism for controlling dynamic
processes, such as the cell cycle.
Illumina Technology: HiSeq 2000/2500
Wang T., Cui Y., Jin J., Guo J., Wang G., et al. (2013) Translating mRNAs strongly correlate to proteins in a multivariate manner and their
translation ratios are phenotype specific. Nucleic Acids Res 41: 4743-4754
It is well known that the abundance of total mRNAs correlates poorly to protein levels. This study set out to analyze the relative abundances
of mRNAs, ribosome-nascent chain complex (RNC)-mRNAs, and proteins on a genome-wide scale. A human lung cancer cell line and
normal bronchial epithelial cells were analyzed with RNA-seq and the protein abundance measured. The authors created a multivariate linear
model showing strong correlation of RNA and protein abundance by integrating the mRNA length as a key factor.
Illumina Technology: Genome AnalyzerIIx and HiSeq 2000
Liu B., Han Y. and Qian S. B. (2013) Cotranslational response to proteotoxic stress by elongation pausing of ribosomes. Mol Cell 49: 453-463
Liu X., Jiang H., Gu Z. and Roberts J. W. (2013) High-resolution view of bacteriophage lambda gene expression by ribosome profiling.
Proc Natl Acad Sci U S A 110: 11928-11933
Cho J., Chang H., Kwon S. C., Kim B., Kim Y., et al. (2012) LIN28A is a suppressor of ER-associated translation in embryonic stem cells.
Cell 151: 765-777

14
Fritsch C., Herrmann A., Nothnagel M., Szafranski K., Huse K., et al. (2012) Genome-wide search for novel human uORFs and N-terminal
protein extensions using ribosomal footprinting. Genome Res 22: 2208-2218
Gerashchenko M. V., Lobanov A. V. and Gladyshev V. N. (2012) Genome-wide ribosome profiling reveals complex translational regulation in
response to oxidative stress. Proc Natl Acad Sci U S A 109: 17394-17399
Han Y., David A., Liu B., Magadan J. G., Bennink J. R., et al. (2012) Monitoring cotranslational protein folding in mammalian cells at codon
resolution. Proc Natl Acad Sci U S A 109: 12467-12472
Hsieh A. C., Liu Y., Edlind M. P., Ingolia N. T., Janes M. R., et al. (2012) The translational landscape of mTOR signalling steers cancer initiation
and metastasis. Nature 485: 55-61
Lee S., Liu B., Lee S., Huang S. X., Shen B., et al. (2012) Global mapping of translation initiation sites in mammalian cells at single-nucleotide
resolution. Proc Natl Acad Sci U S A 109: E2424-2432
Li G. W., Oh E. and Weissman J. S. (2012) The anti-Shine-Dalgarno sequence drives translational pausing and codon choice in bacteria.
Nature 484: 538-541
Stadler M., Artiles K., Pak J. and Fire A. (2012) Contributions of mRNA abundance, ribosome loading, and post- or peri-translational effects to
temporal repression of C. elegans heterochronic miRNA targets. Genome Res 22: 2418-2426
Darnell J. C., Van Driesche S. J., Zhang C., Hung K. Y., Mele A., et al. (2011) FMRP Stalls Ribosomal Translocation on mRNAs Linked to
Synaptic Function and Autism. Cell 146: 247-261
Ingolia N. T., Lareau L. F. and Weissman J. S. (2011) Ribosome Profiling of Mouse Embryonic Stem Cells Reveals the Complexity and Dynamics
of Mammalian Proteomes. Cell 147: 789-802
Oh E., Becker A. H., Sandikci A., Huber D., Chaba R., et al. (2011) Selective ribosome profiling reveals the cotranslational chaperone action of
trigger factor in vivo. Cell 147: 1295-1308
Han Y., David A., Liu B., Magadan J. G., Bennink J. R., et al. (2012) Monitoring cotranslational protein folding in mammalian cells at codon
resolution. Proc Natl Acad Sci U S A 109: 12467-12472
Ingolia N. T. (2010) Genome-wide translational profiling by ribosome footprinting. Methods Enzymol 470: 119-142
Associated Kits
ARTseq™
Ribosome Profiling Kit
Ribo-Zero®
Kit

15
RNA IMMUNOPRECIPITATION SEQUENCING (RIP-SEQ)
RNase digestion RNA extraction cDNAReverse transcriptionImmunoprecipitate
RNA-protein complex
RNA-protein complex
RNA immunoprecipitation sequencing (RIP-Seq) maps the sites where proteins are bound to the RNA within RNA-protein complexes12
. In this
method, RNA-protein complexes are immunoprecipitated with antibodies targeted to the protein of interest. After RNase digestion, RNA covered
by protein is extracted and reverse-transcribed to cDNA. The locations can then be mapped back to the genome. Deep sequencing of cDNA
provides single-base resolution of bound RNA.
Pros Cons
• Maps specific protein-RNA complexes, such as polycomb-
associated RNAs
• Low background and higher resolution of binding site due to
RNase digestion
• No prior knowledge of the RNA is required
• Genome-wide RNA screen
• Requires antibodies to the targeted proteins
• Nonspecific antibodies will precipitate nonspecific complexes
• Lack of crosslinking or stabilization of the complexes may lead to
false negatives
• RNase digestion must be carefully controlled
References
Kanematsu S., Tanimoto K., Suzuki Y. and Sugano S. (2014) Screening for possible miRNA-mRNA associations in a colon cancer cell line.
Gene 533: 520-531
MicroRNAs (miRNAs) are small ncRNAs mediating the regulation of gene expression in various biological contexts, including carcinogenesis.
This study examined the putative associations between miRNAs and mRNAs via Argonaute1 (Ago1) or Ago2 immunoprecipitation in a colon
cancer cell line. The mRNA sequencing and RIP-seq was performed on an Illumina Genome AnalyzerIIx
system. From this analysis the authors
found specific associations of Ago1 with genes having constitutive cellular functions, whereas putative miRNA-mRNA associations detected
with Ago2 IP appeared to be related to signal transduction genes.
Udan-Johns M., Bengoechea R., Bell S., Shao J., Diamond M. I., et al. (2014) Prion-like nuclear aggregation of TDP-43 during heat shock is
regulated by HSP40/70 chaperones. Hum Mol Genet 23: 157-170
Aberrant aggregation of the protein TDP-43 is a key feature of the pathology of amyotrophic lateral sclerosis (ALS). Studying the mechanism
of TDP-43 aggregation, this paper presents an analysis of gene expression and RNA-binding partners in human and mouse cell lines. The
aggregation of TDP-43 was observed during heat shock and potential interaction partners were identified. The authors suggest TDP-43
shares properties with physiologic prions from yeast, requiring chaperone proteins for aggregation.
12 Zhao J., Ohsumi T. K., Kung J. T., Ogawa Y., Grau D. J., et al. (2010) Genome-wide identification of polycomb-associated RNAs by RIP-seq. Mol Cell 40: 939-953

16
Wang X., Lu Z., Gomez A., Hon G. C., Yue Y., et al. (2014) N6-methyladenosine-dependent regulation of messenger RNA
stability. Nature 505: 117-120
N6
-methyladenosine (m6A) is the most prevalent internal (non-cap) modification present in the messenger RNA of all higher eukaryotes. To
understand the role of m6A modification in mammalian cells, the authors of this study applied Illumina sequencing to characterize the YTH
domain family 2 (YTHDF2) reader protein regulation of mRNA degradation. The authors performed m6A-seq (MeRIP-Seq), RIP-seq,
mRNA-Seq, photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP), and ribosome profiling for HeLa
cells on an Illumina HiSeq system with 100 bp single-end reads. They demonstrated that m6A is selectively recognized by YTHDF2, affecting
the translation status and lifetime of mRNA.
Illumina Technology: HiSeq 2000; 100 bp single-end reads
Di Ruscio A., Ebralidze A. K., Benoukraf T., Amabile G., Goff L. A., et al. (2013) DNMT1-interacting RNAs block gene-specific
DNA methylation. Nature 503: 371-376
DNA methylation is one of the many epigenetic factors that influence the regulation of gene expression. In this paper, the authors show that a
novel RNA from the CEBPA gene locus is critical in regulating the local DNA methylation profile, and thus co-influences gene regulation. Using
RIP-seq and RNA-Seq on Illumina platforms, the authors showed that this novel RNA binds DNA (cytosine-5)-methyltransferase 1 (DNMT1)
and prevents methylation of the CEBPA gene locus.
Illumina Technology: Genome AnalyzerIIx and HiSeq 2000
Meyer K. D., Saletore Y., Zumbo P., Elemento O., Mason C. E., et al. (2012) Comprehensive analysis of mRNA methylation reveals enrichment in
3’ UTRs and near stop codons. Cell 149: 1635-1646
Cernilogar F. M., Onorati M. C., Kothe G. O., Burroughs A. M., Parsi K. M., et al. (2011) Chromatin-associated RNA interference components
contribute to transcriptional regulation in Drosophila. Nature 480: 391-395
Salton M., Elkon R., Borodina T., Davydov A., Yaspo M. L., et al. (2011) Matrin 3 binds and stabilizes mRNA. PLoS One 6: e23882
Zhao J., Ohsumi T. K., Kung J. T., Ogawa Y., Grau D. J., et al. (2010) Genome-wide identification of polycomb-associated RNAs by RIP-seq.
Mol Cell 40: 939-953
Associated Kits
ARTseq™
Ribo-Zero Kit
TruSeq Stranded mRNA and Total RNA Sample Preparation Kit
TruSeq Targeted RNA Expression Kit

17
HIGH-THROUGHPUT SEQUENCING OF CLIP CDNA LIBRARY (HITS-CLIP) OR
CROSSLINKING AND IMMUNOPRECIPITATION SEQUENCING (CLIP-SEQ)
RNA-protein complex RNA extractionRNase T1 digestionUV 254 nm cDNAReverse transcriptionProteinase K
High-throughput sequencing of CLIP cDNA library (HITS-CLIP) or crosslinking and immunoprecipitation sequencing (CLIP-Seq) maps protein-RNA
binding sites in vivo13
. This approach is similar to RIP-Seq, but uses crosslinking to stabilize the protein-RNA complexes. In this method, RNA-pro-
tein complexes are UV crosslinked and immunoprecipitated. The protein-RNA complexes are treated with RNase followed by Proteinase K. RNA is
extracted and reverse-transcribed to cDNA. Deep sequencing of cDNA provides single-base resolution mapping of protein binding to RNAs.
Pros Cons
• Crosslinking stabilizes the protein-target binding
• UV crosslinking can be carried out in vivo
• Low background and higher resolution of binding site due to
RNase digestion
• Antibodies not specific to the target may precipitate
nonspecific complexes
• UV crosslinking is not very efficient and requires very close
protein-RNA interactions
• Artifacts may be introduced during the crosslinking process
References
Poulos M. G., Batra R., Li M., Yuan Y., Zhang C., et al. (2013) Progressive impairment of muscle regeneration in muscleblind-like 3 isoform
knockout mice. Hum Mol Genet 22: 3547-3558
The human muscleblind-like (MBNL) genes encode alternative splicing factors essential for development of multiple tissues. In the
neuromuscular disease myotonic dystrophy, C(C)UG repeats in RNA inhibit MBNL activity. This paper reports a study of the Mbnl3 protein
isoform in a mouse model to determine the function of Mbnl3 in muscle regeneration and muscle function. The authors used an Illumina
Genome Analyzer system for RNA-Seq and HITS-CLIP to determine Mbnl3-RNA interaction.
13
Chi SW, Zang JB, Mele A, Darnell RB; (2009) Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature 460: 479-86

18
Xu D., Shen W., Guo R., Xue Y., Peng W., et al. (2013) Top3beta is an RNA topoisomerase that works with fragile X syndrome protein to
promote synapse formation. Nat Neurosci 16: 1238-1247
Topoisomerases are crucial for solving DNA topological problems, but they have not previously been linked to RNA metabolism. In this study
the human topoisomerase 3beta (Top3B), which is known to regulate the translation of mRNAs, was found to bind multiple mRNAs encoded
by genes with neuronal functions linked to schizophrenia and autism.
Charizanis K., Lee K. Y., Batra R., Goodwin M., Zhang C., et al. (2012) Muscleblind-like 2-mediated alternative splicing in the developing brain
and dysregulation in myotonic dystrophy. Neuron 75: 437-450
Chi S. W., Hannon G. J. and Darnell R. B. (2012) An alternative mode of microRNA target recognition. Nat Struct Mol Biol 19: 321-327
Riley K. J., Rabinowitz G. S., Yario T. A., Luna J. M., Darnell R. B., et al. (2012) EBV and human microRNAs co-target oncogenic and apoptotic
viral and human genes during latency. EMBO J 31: 2207-2221
Vourekas A., Zheng Q., Alexiou P., Maragkakis M., Kirino Y., et al. (2012) Mili and Miwi target RNA repertoire reveals piRNA biogenesis and
function of Miwi in spermiogenesis. Nat Struct Mol Biol 19: 773-781
Darnell J. C., Van Driesche S. J., Zhang C., Hung K. Y., Mele A., et al. (2011) FMRP Stalls Ribosomal Translocation on mRNAs Linked to
Synaptic Function and Autism. Cell 146: 247-261
Polymenidou M., Lagier-Tourenne C., Hutt K. R., Huelga S. C., Moran J., et al. (2011) Long pre-mRNA depletion and RNA missplicing contribute
to neuronal vulnerability from loss of TDP-43. Nat Neurosci 14: 459-468
Zhang C. and Darnell R. B. (2011) Mapping in vivo protein-RNA interactions at single-nucleotide resolution from HITS-CLIP data.
Nat Biotechnol 29: 607-614
McKenna L. B., Schug J., Vourekas A., McKenna J. B., Bramswig N. C., et al. (2010) MicroRNAs control intestinal epithelial differentiation,
architecture, and barrier function. Gastroenterology 139: 1654-1664, 1664 e1651
Yano M., Hayakawa-Yano Y., Mele A. and Darnell R. B. (2010) Nova2 regulates neuronal migration through an RNA switch in disabled-1
signaling. Neuron 66: 848-858
Zhang C., Frias M. A., Mele A., Ruggiu M., Eom T., et al. (2010) Integrative modeling defines the Nova splicing-regulatory network and its
combinatorial controls. Science 329: 439-443
Chi S. W., Zang J. B., Mele A. and Darnell R. B. (2009) Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps.
Nature 460: 479-486
Associated Kits
ARTseq™
Ribo-Zero Kit

19
PHOTOACTIVATABLE RIBONUCLEOSIDE–ENHANCED CROSSLINKING AND
IMMUNOPRECIPITATION (PAR-CLIP)
RNA-protein complex RNA
extraction
RNase T1 digestionUV 365 nm cDNAReverse transcriptionIncorporate 4-thiouridine
(4SU) into transcripts of
cultured cells
Proteinase K
Photoactivatable ribonucleoside–enhanced crosslinking and immunoprecipitation (PAR-CLIP) maps RNA-binding proteins (RBPs)14
. This approach
is similar to HITS-CLIP and CLIP-Seq, but uses much more efficient crosslinking to stabilize the protein-RNA complexes. The requirement to
introduce a photoactivatable ribonucleoside limits this approach to cell culture and in vitro systems. In this method, 4-thiouridine (4-SU) and
6-thioguanosine (6-SG) are incorporated into transcripts of cultured cells. UV irradiation crosslinks 4-SU/6-SG–labeled transcripts to interacting
RBPs. The targeted complexes are immunoprecipitated and digested with RNase T1, followed by Proteinase K, before RNA extraction. The RNA is
reverse-transcribed to cDNA and sequenced. Deep sequencing of cDNA accurately maps RBPs interacting with labeled transcripts.
Pros Cons
• Highly accurate mapping of RNA-protein interactions
• Labeling with 4-SU/6-SG improves crosslinking efficiency
• Antibodies not specific to target may precipitate
• Limited to cell culture and in vitro systems
References
Kaneko S., Bonasio R., Saldana-Meyer R., Yoshida T., Son J., et al. (2014) Interactions between JARID2 and Noncoding RNAs Regulate
PRC2 Recruitment to Chromatin. Mol Cell 53: 290-300
JARID2 is an accessory component of Polycomb repressive complex-2 (PRC2) required for the differentiation of embryonic stem cells (ESCs).
In this study the molecular role of JARID2 in gene silencing was elucidated using RIP, ChIP, and PAR-CLIP combined with sequencing on
an Illumina HiSeq 2000 system. The authors found that Meg3 and other lncRNAs from the Dlk1-Dio3 locus interact with PRC2 via JARID2.
These findings suggest a more general mechanism by which lncRNAs contribute to PRC2 recruitment.
14 Hafner M., Landgraf P., Ludwig J., Rice A., Ojo T., et al. (2008) Identification of microRNAs and other small regulatory RNAs using cDNA library sequencing. Methods 44: 3-12
Photoactivatable ribonucleosides
4-thiouridine (4SU)
N
HO
O
OH OH
NH
O
S
5-iodouridine (5IU)
N
OH
O
OH OH
NH
O
I
O
4-bromo uridine (5BrU)
N
OH
O
OH OH
NH
O
Br
O
6-Thioguanosine (6SG)
N
OH
O
OH OH
N NH2
NH
N
S

20
Liu Y., Hu W., Murakawa Y., Yin J., Wang G., et al. (2013) Cold-induced RNA-binding proteins regulate circadian gene
expression by controlling alternative polyadenylation. Sci Rep 3: 2054
In an effort to understand the concert of gene regulation by the circadian rhythm, the authors of this study used a mouse model with a fixed
light/dark cycle, to determine genes regulated by variations in body temperature. The authors applied RNA-Seq and PAR-CLIP sequencing
on an Illumina Genome Analyzer system to determine Cirbp and Rbm3 as important regulators for the temperature entrained circadian
gene expression. They discovered that these two proteins regulate the peripheral clocks by controlling the oscillation of alternative
polyadenylation sites.
Illumina Technology: Genome Analyzer®
; 76 bp single-end reads
Stoll G., Pietilainen O. P., Linder B., Suvisaari J., Brosi C., et al. (2013) Deletion of TOP3beta, a component of FMRP-containing mRNPs,
contributes to neurodevelopmental disorders. Nat Neurosci 16: 1228-1237
Genetic studies, including studies of mRNA-binding proteins, have brought new light to the connection of mRNA metabolism to disease. In
this study the authors found the deletion of the topoisomerase 3ß (TOP3ß) gene was associated with neurodevelopmental disorders in the
Northern Finnish population. Combining genotyping with immunoprecipitation of mRNA-bound proteins (PAR-CLIP), the authors found that
the recruitment of TOP3ß to cytosolic messenger ribonucleoproteins (mRNPs) was coupled to the co-recruitment of FMRP, the disease gene
involved in fragile X syndrome mental disorders.
Illumina Technology: Human Gene Expression—BeadArray, Human610-Quad (Infinium GT®
), HumanHap300 (Duo/Duo+) (Infinium GT),
HumanCNV370-Duo (Infinium GT)
Whisnant A. W., Bogerd H. P., Flores O., Ho P., Powers J. G., et al. (2013) In-depth analysis of the interaction of HIV-1 with
cellular microRNA biogenesis and effector mechanisms. MBio 4: e000193
The question of how HIV-1 interfaces with cellular miRNA biogenesis and effector mechanisms has been highly controversial. In this paper,
the authors used the Illumina HiSeq 2000 platform for deep sequencing of small RNAs in two different infected cell lines and two types of
primary human cells. They unequivocally demonstrated that HIV-1 does not encode any viral miRNAs.
Majoros W. H., Lekprasert P., Mukherjee N., Skalsky R. L., Corcoran D. L., et al. (2013) MicroRNA target site identification by integrating
sequence and binding information. Nat Methods 10: 630-633
Mandal P. K., Ewing A. D., Hancks D. C. and Kazazian H. H., Jr. (2013) Enrichment of processed pseudogene transcripts in
L1-ribonucleoprotein particles. Hum Mol Genet 22: 3730-3748
Hafner M., Lianoglou S., Tuschl T. and Betel D. (2012) Genome-wide identification of miRNA targets by PAR-CLIP. Methods 58: 94-105
Sievers C., Schlumpf T., Sawarkar R., Comoglio F. and Paro R. (2012) Mixture models and wavelet transforms reveal high confidence
RNA-protein interaction sites in MOV10 PAR-CLIP data. Nucleic Acids Res 40: e160

21
Skalsky R. L., Corcoran D. L., Gottwein E., Frank C. L., Kang D., et al. (2012) The viral and cellular microRNA targetome in lymphoblastoid cell
lines. PLoS Pathog 8: e1002484
Uniacke J., Holterman C. E., Lachance G., Franovic A., Jacob M. D., et al. (2012) An oxygen-regulated switch in the protein synthesis
machinery. Nature 486: 126-129
Gottwein E., Corcoran D. L., Mukherjee N., Skalsky R. L., Hafner M., et al. (2011) Viral microRNA targetome of KSHV-infected primary effusion
lymphoma cell lines. Cell Host Microbe 10: 515-526
Jungkamp A. C., Stoeckius M., Mecenas D., Grun D., Mastrobuoni G., et al. (2011) In vivo and transcriptome-wide identification of RNA binding
protein target sites. Mol Cell 44: 828-840
Kishore S., Jaskiewicz L., Burger L., Hausser J., Khorshid M., et al. (2011) A quantitative analysis of CLIP methods for identifying binding sites of
RNA-binding proteins. Nat Methods 8: 559-564
Lebedeva S., Jens M., Theil K., Schwanhausser B., Selbach M., et al. (2011) Transcriptome-wide analysis of regulatory interactions of the RNA-
binding protein HuR. Mol Cell 43: 340-352
Mukherjee N., Corcoran D. L., Nusbaum J. D., Reid D. W., Georgiev S., et al. (2011) Integrative regulatory mapping indicates that the RNA-
binding protein HuR couples pre-mRNA processing and mRNA stability. Mol Cell 43: 327-339
Hafner M., Landthaler M., Burger L., Khorshid M., Hausser J., et al. (2010) Transcriptome-wide identification of RNA-binding protein and
microRNA target sites by PAR-CLIP. Cell 141: 129-141
Hafner M., Landthaler M., Burger L., Khorshid M., Hausser J., et al. (2010) PAR-CliP--a method to identify transcriptome-wide the binding sites
of RNA binding proteins. J Vis Exp
Associated Kits
ARTseq™
Ribo-Zero Kit
TruSeq Stranded mRNA and Total RNA Sample Prep Kit

22
INDIVIDUAL NUCLEOTIDE RESOLUTION CLIP (ICLIP)
RNA-protein complex cDNAImmunoprecipitate
cross-linked RNA-protein
complex
Cross-linked peptides
remain after proteinase K
digestion
Proteinase K
cDNA truncates
at binding site
cleavable
adapter
Circularize Linearize and PCR
Individual nucleotide resolution CLIP (iCLIP) maps protein-RNA interactions similar to HITS-CLIP and PAR-CLIP15
. This approach includes ad-
ditional steps to digest the proteins after crosslinking and to map the crosslink sites with reverse transcriptase. In this method specific crosslinked
RNA-protein complexes are immunoprecipitated. The complexes are then treated with proteinase K, as the protein crosslinked at the binding
site remains undigested. Upon reverse transcription, cDNA truncates at the binding site and is circularized. These circularized fragments are then
linearized and PCR-amplified. Deep sequencing of these amplified fragments provides nucleotide resolution of protein-binding site.
Pros Cons
• Nucleotide resolution of protein-binding site
• Avoids the use of nucleases
• Amplification allows the detection of rare events
• Antibodies not specific to target will precipitate
• Non-linear PCR amplification can lead to biases
affecting reproducibility
• Artifacts may be introduced in the circularization step
References
Broughton J. P. and Pasquinelli A. E. (2013) Identifying Argonaute binding sites in Caenorhabditis elegans using iCLIP.
Methods 63: 119-125
The identification of endogenous targets remains an important challenge in understanding miRNA function. New approaches include
iCLIP-sequencing, using Illumina sequencing, for high-throughput detection of miRNA targets. In this study the iCLIP protocol was adapted
for use in Caenorhabditis elegans to identify endogenous sites targeted by the worm Argonaute protein primarily responsible for
miRNA function.
Zarnack K., Konig J., Tajnik M., Martincorena I., Eustermann S., et al. (2013) Direct competition between hnRNP C and U2AF65 protects the
transcriptome from the exonization of Alu elements. Cell 152: 453-466
Alu elements are a certain type of repeat scattered all over the human genome. Interestingly, Alu elements may be found within gene regions
and contain cryptic splice sites. This study investigated the mechanism by which the Alu splice sites are prevented from disrupting normal
gene splicing and expression. By using CLIP with Illumina sequencing, the authors profiled mRNAs bound by protein and showed that
heterogeneous nuclear riboprotein (hnRNP) C competes with the splicing factor at many genuine and cryptic splice sites. These results
suggest hnRNP C acts as a genome-wide protection against transcription disruption by Alu elements.
15 Konig J., Zarnack K., Rot G., Curk T., Kayikci M., et al. (2010) iCLIP reveals the function of hnRNP particles in splicing at individual nucleotide resolution. Nat Struct Mol Biol 17: 909-915

23
Zund D., Gruber A. R., Zavolan M. and Muhlemann O. (2013) Translation-dependent displacement of UPF1 from coding sequences causes
its enrichment in 3’ UTRs. Nat Struct Mol Biol 20: 936-943
UPF1 is a factor involved in nonsense-mediated mRNA decay (NMD). The target binding sites and timing of the binding to target mRNAs has
been investigated. In this report the binding sites of UPF1 were studied using transcriptome-wide mapping by CLIP-seq on an Illumina HiSeq
2000 system. The authors show how UPF1 binds RNA before translation and is displaced by translating ribosomes. This observation
suggests that the triggering of NMD occurs after the binding of UPF1, presumably through aberrant translation termination.
Rogelj B., Easton L. E., Bogu G. K., Stanton L. W., Rot G., et al. (2012) Widespread binding of FUS along nascent RNA regulates alternative
splicing in the brain. Sci Rep 2: 603
Tollervey J. R., Curk T., Rogelj B., Briese M., Cereda M., et al. (2011) Characterizing the RNA targets and position-dependent splicing regulation
by TDP-43. Nat Neurosci 14: 452-458
Konig J., Zarnack K., Rot G., Curk T., Kayikci M., et al. (2010) iCLIP reveals the function of hnRNP particles in splicing at individual nucleotide
resolution. Nat Struct Mol Biol 17: 909-915
Associated Kits
ARTseq™
Ribo-Zero Kit

24
NATIVE ELONGATING TRANSCRIPT SEQUENCING (NET-SEQ)
cDNAReverse transcriptionRNA extraction
RNA
Transcriptome complex Immunoprecipitate complex
Cap RNA Cap
Native elongating transcript sequencing (NET-Seq) maps transcription through the capture of 3’ RNA16
. In this method the RNA polymerase II
elongation complex is immunoprecipitated, and RNA is extracted and reverse-transcribed to cDNA. Deep sequencing of the cDNA allows for 3’-end
sequencing of nascent RNA, providing nucleotide resolution at transcription.
Pros Cons
• Mapping of nascent RNA-bound protein
• Transcription is mapped at nucleotide resolution
• Antibodies not specific to target will precipitate
References
Larson M. H., Gilbert L. A., Wang X., Lim W. A., Weissman J. S., et al. (2013) CRISPR interference (CRISPRi) for sequence-specific control of
gene expression. Nat Protoc 8: 2180-2196
This paper describes a protocol for selective gene repression based on clustered regularly interspaced palindromic repeats interference
(CRISPRi). The protocol provides a simplified approach for rapid gene repression within 1-2 weeks. The method can also be adapted
for high-throughput interrogation of genome-wide gene functions and genetic interactions, thus providing a complementary approach to
standard RNA interference protocols.
Associated Kits
ARTseq™
Ribo-Zero Kit
16 Churchman L. S. and Weissman J. S. (2011) Nascent transcript sequencing visualizes transcription at nucleotide resolution. Nature 469: 368-373

25
References
Mellen M., Ayata P., Dewell S., Kriaucionis S. and Heintz N. (2012) MeCP2 binds to 5hmC enriched within active genes and accessible
chromatin in the nervous system. Cell 151: 1417-1430
Epigenetic markers, such as chromatin-binding factors and modifications to the DNA itself, are important for regulation of gene expression
and differentiation. In this study, the DNA methylation 5-hydroxymethylcytosine (5hmC) was profiled in differentiated central nervous system
cells in vivo. The authors found 5hmC enriched in active genes along with a strong depletion of the alternative methylation 5mC. The authors
hypothesize that binding of 5hmC by methyl CpG binding protein 2 (MeCP2) plays a central role in the epigenetic regulation of neural
chromatin and gene expression.
Illumina Technology: TruSeq DNA Sample Prep Kit, HiSeq 2000
Associated Kits
ARTseq™
Ribo-Zero Kit
TARGETED PURIFICATION OF POLYSOMAL MRNA (TRAP-SEQ)
cDNAreverse transcription
Cap
Polysomes GFP Bead RNA
Targeted purification of polysomal mRNA (TRAP-Seq) maps translating mRNAs under various conditions17
. In this method, tagged ribosomal
proteins are expressed in cells. The tagged ribosomal proteins are then purified and the RNA isolated. RNAs are reverse-transcribed to cDNA.
Deep sequencing of the cDNA provides single-base resolution of translating RNA.
Pros Cons
• Allows detection of translating RNAs
• RNAs translated by specific targeted ribosomes can
be assessed
• Not as specific as more recently developed methods, such
as Ribo-Seq
17 Jiao Y. and Meyerowitz E. M. (2010) Cell-type specific analysis of translating RNAs in developing flowers reveals new levels of control. Mol Syst Biol 6: 419

26
CROSSLINKING, LIGATION, AND SEQUENCING OF HYBRIDS (CLASH-SEQ)
UV crosslinking Ligate endsRNA duplex
A
B
A
B
A
B
cDNAreverse transcriptionAffinity
purification
Crosslinked
complex
Crosslinking, ligation, and sequencing of hybrids (CLASH-Seq) maps RNA-RNA interactions18
. In this method RNA-protein complexes are UV
crosslinked and affinity-purified. RNA-RNA hybrids are then ligated, isolated, and reverse-transcribed to cDNA. Deep sequencing of the cDNA
provides high-resolution chimeric reads of RNA-RNA interactions.
Pros Cons
• Maps RNA-RNA interactions
• Performed in vivo
• Hybrid ligation may be difficult between short RNA fragments
References
Kudla G., Granneman S., Hahn D., Beggs J. D. and Tollervey D. (2011) Cross-linking, ligation, and sequencing of hybrids reveals RNA-RNA
interactions in yeast. Proc Natl Acad Sci U S A 108: 10010-10015
Associated Kits
18 Kudla G., Granneman S., Hahn D., Beggs J. D. and Tollervey D. (2011) Cross-linking, ligation, and sequencing of hybrids reveals RNA-RNA interactions in yeast. Proc Natl Acad Sci U S A 108:
10010-10015

27
PARALLEL ANALYSIS OF RNA ENDS SEQUENCING (PARE-SEQ) OR
GENOME-WIDE MAPPING OF UNCAPPED TRANSCRIPTS (GMUCT)
5’ GPPP AA(A)n
5’ GPPP
5’ P AA(A)n
5’ P AA(A)n
TT(T) 21
miRNA directed cleavage
or degraded RNA
capped mRNA
3’ OH
MmeI
MmeI digestion purify ligate PCR
3’adapter
cDNAsecond
strand
synthesis
fragment
RNA
poly(A) RNA
extraction
ligate
adapter
reverse
transcription
Parallel analysis of RNA ends sequencing (PARE-Seq) or genome-wide mapping of uncapped transcripts (GMUCT) maps miRNA cleavage sites.
Various RNA degradation processes impart characteristic sequence ends. By analyzing the cleavage sites, the degradation processes can be
inferred19
. In this method, degraded capped mRNA is adapter-ligated and reverse-transcribed. Fragments are then Mmel-digested, purified,
3’-adapter-ligated, and PCR-amplified. Deep sequencing of the cDNA provides information about uncapped transcripts that undergo degradation.
Pros Cons
• Maps degrading RNA
• miRNA cleavage sites are identified
• No prior knowledge of the target RNA sequence is required
• Non-linear PCR amplification can lead to biases,
• Amplification errors caused by polymerases will be represented
and sequenced incorrectly
References
Karlova R, van Haarst JC, Maliepaard C, van de Geest H, Bovy AG, Lammers M, Angenent GC, de Maagd RA; (2013) Identification of
microRNA targets in tomato fruit development using high-throughput sequencing and degradome analysis. J Exp Bot 64: 1863-78
The biochemical and genetic processes of fruit development and ripening are of great interest for the food production industry. In this study,
the involvement of miRNA in gene regulation was investigated for tomato plants to determine the fruit development processes regulated by
miRNA. Using PARE-Seq, the authors identified a total of 119 target genes of miRNAs. Auxin response factors as well as two known ripening
regulators were among the identified target genes, indicating an involvement of miRNAs in regulation of fruit ripening.
Yang X, Wang L, Yuan D, Lindsey K, Zhang X; (2013) Small RNA and degradome sequencing reveal complex miRNA regulation during cotton
somatic embryogenesis. J Exp Bot 64: 1521-36
The authors used PARE-seq to study miRNA expression during cotton somatic embryogenesis. They identified 25 novel miRNAs, as well as
their target genes during development.
, HiSeq 2000
19
German M. A., Pillay M., Jeong D. H., Hetawal A., Luo S., et al. (2008) Global identification of microRNA-target RNA pairs by parallel analysis of RNA ends. Nat Biotechnol 26: 941-946

28
Shamimuzzaman M, Vodkin L; (2012) Identification of soybean seed developmental stage-specific and tissue-specific miRNA targets by
degradome sequencing. BMC Genomics 13: 310
Bracken CP, Szubert JM, Mercer TR, Dinger ME, Thomson DW, Mattick JS, Michael MZ, Goodall GJ; (2011) Global analysis of the mammalian
RNA degradome reveals widespread miRNA-dependent and miRNA-independent endonucleolytic cleavage. Nucleic Acids Res 39: 5658-68
Mercer TR, Neph S, Dinger ME, Crawford J, Smith MA, Shearwood AM, Haugen E, Bracken CP, Rackham O, Stamatoyannopoulos JA,
Filipovska A, Mattick JS; (2011) The human mitochondrial transcriptome. Cell 146: 645-58
Associated Kits

29
TRANSCRIPT ISOFORM SEQUENCING (TIF-SEQ) OR
PAIRED-END ANALYSIS OF TSSS (PEAT)
5’ GPPP AA (A)n
capped mRNA cDNA
5’ P AA(A)n
tobacco acid
pyrophosphatase
(TAP) treatment
5’ P AA(A)n
TT(T)n
ligate‘5oligocap’
oligonucleotide
Biotin
Second
strand
synthesis
Incorporate
biotinilated
primers
purifyreverse
transcription
Circularize
and
fragment
Transcript isoform sequencing (TIF-Seq)20
or paired-end analysis of transcription start sites (TSSs) (PEAT)21
maps RNA isoforms. In this method, the
5’ cap is removed with tobacco acid pyrophosphatase (TAP) treatment, then a “5’-oligocap” oligonucleotide is ligated and the RNA is reverse-
transcribed. Biotinylated primers are incorporated and the circularized fragment is purified. Deep sequencing of the cDNA provides high-resolution
information of the 5’ and 3’ ends of transcripts.
Pros Cons
• Transcript isoforms are identified by 5’ and 3’
paired-end sequencing
• Low-level transcripts may be missed or underrepresented
• Artifacts may be introduced during the circularization step
References
Pelechano V., Wei W. and Steinmetz L. M. (2013) Extensive transcriptional heterogeneity revealed by isoform profiling.
Nature 497: 127-131
Identifying gene transcripts by sequencing allows high-throughput profiling of gene expression. However, methods that identify either 5’ or 3’
transcripts individually do not convey information about the occurrence of transcript isoforms. This paper presents TIF-Seq, a new assay for
transcript isoform sequencing. By jointly determining both transcript ends for millions of RNA molecules, this method provides genome-wide
detection and annotation of transcript isoforms. The authors demonstrate the TIF-Seq assay for yeast and note that over 26 major transcript
isoforms per protein-coding gene were found to be expressed in yeast, suggesting a much higher genome expression repertoire than
previously expected.
Ni T., Corcoran D. L., Rach E. A., Song S., Spana E. P., et al. (2010) A paired-end sequencing strategy to map the complex landscape of
transcription initiation. Nat Methods 7: 521-527
Associated Kits
Ribo-Zero Kit
Enzyme Solutions:
Tobacco Acid Pyrophosphatase (TAP)
20 Pelechano V., Wei W. and Steinmetz L. M. (2013) Extensive transcriptional heterogeneity revealed by isoform profiling. Nature 497: 127-131
21 Ni T., Corcoran D. L., Rach E. A., Song S., Spana E. P., et al. (2010) A paired-end sequencing strategy to map the complex landscape of transcription initiation. Nat Methods 7: 521-527

30
RNA STRUCTURE
RNA has the ability to form secondary structures that can either promote or inhibit RNA-protein or protein-protein interactions22,23
. The most diverse
secondary and tertiary structures are found in transfer RNAs (tRNAs) and are thought to play a major role in modulating protein translation. RNA
structures were first studied in Tetrahymena thermophilia using X-ray crystallography, but those studies are inherently cumbersome and limited24
.
Sequencing not only provides information on secondary structures, but it can also determine point mutation effects on RNA structures in a large
number of samples. Recent studies have shown that sequencing is a powerful tool to identify RNA structures and determine their significance.
Reviews
Lai D., Proctor J. R. and Meyer I. M. (2013) On the importance of cotranscriptional RNA structure formation. RNA 19: 1461-1473
Thapar R., Denmon A. P. and Nikonowicz E. P. (2014) Recognition modes of RNA tetraloops and tetraloop-like motifs by RNA-binding proteins.
Wiley Interdiscip Rev RNA 5: 49-67
22 Osborne R. J. and Thornton C. A. (2006) RNA-dominant diseases. Hum Mol Genet 15 Spec No 2: R162-169
23 Thapar R., Denmon A. P. and Nikonowicz E. P. (2014) Recognition modes of RNA tetraloops and tetraloop-like motifs by RNA-binding proteins. Wiley Interdiscip Rev RNA 5: 49-67
24 Rich A. and RajBhandary U. L. (1976) Transfer RNA: molecular structure, sequence, and properties. Annu Rev Biochem 45: 805-860
Paramecia species were one of the first model organisms used to study tRNA structure.

31
SELECTIVE 2’-HYDROXYL ACYLATION ANALYZED BY PRIMER EXTENSION SEQUENCING (SHAPE-SEQ)
Selective 2’-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq)25
provides structural information about RNA. In this method,
a unique barcode is first added to the 3’ end of RNA, and the RNA is then allowed to fold under pre-established in vitro conditions. The barcoded
and folded RNA is treated with a SHAPE reagent, 1M7, that blocks reverse transcription. The RNA is then reverse-transcribed to cDNA. Deep
sequencing of the cDNA provides single-nucleotide sequence information for the positions occupied by 1M7. The structural information of the RNA
can then be deduced.
Pros Cons
• Provides RNA structural information
• Multiplexed analysis of barcoded RNAs provides information for
multiple RNAs
• Effect of point mutations on RNA structure can be assessed
• Alternative to mass spectrometry, NMR, and crystallography
• Need positive and negative controls to account for transcriptase
drop-off
• Need pre-established conditions for RNA folding
• The folding in vitro may not reflect actual folding in vivo
References
Lucks J. B., Mortimer S. A., Trapnell C., Luo S., Aviran S., et al. (2011) Multiplexed RNA structure characterization with selective 2’-hydroxyl
acylation analyzed by primer extension sequencing (SHAPE-Seq). Proc Natl Acad Sci U S A 108: 11063-11068
Associated Kits
25 Lucks J. B., Mortimer S. A., Trapnell C., Luo S., Aviran S., et al. (2011) Multiplexed RNA structure characterization with selective 2’-hydroxyl acylation analyzed by primer
extension sequencing (SHAPE-Seq). Proc Natl Acad Sci U S A 108: 11063-11068
N
OO O
NO2
1-methyl-7-nitroisatoic
anhydride
(1M7)
cDNABarcoded RNA 1M7 reaction Reverse transcription RNA hydrolysis

32
PARALLEL ANALYSIS OF RNA STRUCTURE (PARS-SEQ)
cDNAReverse transcription
RNAse V1
RNAse S1
RNAse digestion Random fragmentation
3’OH
3’OH
5’ OH 3’OH
3’OH
3’OH
3’OH
3’OH
5’OH 3’OH
3’OH
3’OH
5’ P
5’ P
5’ P
5’ P
3’OH
3’OH
5’ OH 3’OH
3’OH
3’OH
3’OH
3’OH
5’OH 3’OH
3’OH
3’OH
5’ P
5’ P
5’ P
5’ P
polyA RNA RNA fragments with 5’phosphate ends
Parallel analysis of RNA structure (PARS-Seq)26
mapping gives information about the secondary and tertiary structure of RNA. In this method
RNA is digested with RNases that are specific for double-stranded and single-stranded RNA, respectively. The resulting fragments are reverse-
transcribed to cDNA. Deep sequencing of the cDNA provides high-resolution sequences of the RNA. The RNA structure can be deduced by
comparing the digestion patterns of the various RNases.
Pros Cons
• Provides RNA structural information
• Distinguishes between paired and unpaired bases
• Enzyme digestion can be nonspecific
• Digestion conditions must be carefully controlled
• RNA can be overdigested
References
Wan Y, Qu K, Ouyang Z, Chang HY; (2013) Genome-wide mapping of RNA structure using nuclease digestion and high-throughput
sequencing. Nat Protoc 8: 849-69
RNA structure is important for RNA function and regulation, and there is growing interest in determining the RNA structure of many
transcripts. This is the first paper to describe the PARS protocol. In this method, enzymatic footprinting is coupled with high-throughput
sequencing to retrieve information about secondary RNA structure for thousands of RNAs simultaneously.
, HiSeq 2000
Associated Kits
26 Pelechano V., Wei W. and Steinmetz L. M. (2013) Extensive transcriptional heterogeneity revealed by isoform profiling. Nature 497: 127-131

33
FRAGMENTATION SEQUENCIN2G (FRAG-SEQ)
Fragmentation sequencing (FRAG-Seq)27
is a method for probing RNA structure. In this method, RNA is digested using nuclease P1, followed by
reverse transcription. Deep sequencing of the cDNA provides high-resolution single-stranded reads, which can be used to determine the structure
of RNA by mapping P1 endonuclease digestion sites.
Pros Cons
• Simple and fast protocol compared to PARS-seq
• High throughput
• Need endogenous controls
• Potential for contamination between samples and controls
Associated Kits
27 Underwood J. G., Uzilov A. V., Katzman S., Onodera C. S., Mainzer J. E., et al. (2010) FragSeq: transcriptome-wide RNA structure probing using high-throughput sequencing. Nat Methods 7: 995-1001
cDNAReverse transcription
P1 endonuclease
P1 endonuclease digestionIn vitro folded polyA RNA
Endogenous 5’P control
Endogenous 5’OH control
5’ P 5’ P3’OH
5’ P 5’ P3’OH
3’OH 5’ OH
3’OH
T4 kinase
5’ P 5’ P

34
CXXC AFFINITY PURIFICATION SEQUENCING (CAP-SEQ)
5’ OH
5’ P
5’ PPP
5’ GPPP
5’ OH
5’ PPP
5’ GPPP
5’ OH
5’ GPPP
5’ OH
5’ P
5’ OH
5’ P
5’ OH
5’ P
TerminatorTotal RNA CIP TAP Primer Ligation Random Primer Reverse
transcription
purification cDNA
CXXC affinity purification sequencing (CAP-Seq)28
maps the 5’ end of RNAs anchored to RNA polymerase II. In this method, RNA transcripts are
treated with a terminator, calf intestine alkaline phosphatase (CIP), and then tobacco acid pyrophosphatase (TAP), followed by linker ligation and
reverse transcription to cDNA. Deep sequencing of the cDNA provides high-resolution sequences of RNA polymerase II transcripts.
Pros Cons
• Maps RNAs anchored to RNA polymerase II • Multiple steps and treatments can lead to loss of material
28 Illingworth R. S., Gruenewald-Schneider U., Webb S., Kerr A. R., James K. D., et al. (2010) Orphan CpG islands identify numerous conserved promoters in the mammalian genome. PLoS Genet
6: e1001134
References
Farcas A. M., Blackledge N. P., Sudbery I., Long H. K., McGouran J. F., et al. (2012) KDM2B links the Polycomb Repressive Complex 1
(PRC1) to recognition of CpG islands. Elife 1: e00205
DNA methylation occurs naturally throughout the genome, mostly at positions where cytosine is bonded to guanine to form a CpG
dinucleotide. Many stretches of CpGs, also called CpG islands, contain a high proportion of unmethylated CpGs. In this study, the
unmethylated CpG islands were studied for possible mechanisms favoring the unmethylated sites. Using ChIP-Seq experiments for various
transcription factors, the authors showed that CpG islands are occupied by low levels of polycomb repressive complex 1 throughout the
genome, potentially making the sites susceptible to polycomb-mediated silencing.

35
Gu W., Lee H. C., Chaves D., Youngman E. M., Pazour G. J., et al. (2012) CapSeq and CIP-TAP identify Pol II start sites and reveal capped
small RNAs as C. elegans piRNA precursors. Cell 151: 1488-1500
Small RNA molecules account for many different functions in the cell. Piwi-interacting RNAs (piRNAs) represent one type of germline-
expressed small RNAs linked to epigenetic programming. This study presents CAP-Seq, an assay developed to characterize the transcription
of piRNAs in C. elegans. To their surprise, the authors found that likely piRNA precursors are capped small RNAs that initiate precisely 2
bpupstream of mature piRNAs. In addition, they identified a new class of piRNAs, further adding to the complexity of small RNA molecules.
, HiSeq 2000
Clouaire T., Webb S., Skene P., Illingworth R., Kerr A., et al. (2012) Cfp1 integrates both CpG content and gene activity for accurate H3K4me3
deposition in embryonic stem cells. Genes Dev 26: 1714-1728
Gendrel A. V., Apedaile A., Coker H., Termanis A., Zvetkova I., et al. (2012) Smchd1-dependent and -independent pathways determine
developmental dynamics of CpG island methylation on the inactive x chromosome. Dev Cell 23: 265-279
Matsushita H., Vesely M. D., Koboldt D. C., Rickert C. G., Uppaluri R., et al. (2012) Cancer exome analysis reveals a T-cell-dependent mechanism
of cancer immunoediting. Nature 482: 400-404
Illingworth R. S., Gruenewald-Schneider U., Webb S., Kerr A. R., James K. D., et al. (2010) Orphan CpG islands identify numerous conserved
promoters in the mammalian genome. PLoS Genet 6: e1001134
Associated Kits
TruSeq Stranded mRNA and Total RNA®
Sample Preparation Kit
Enzyme Solutions:
Calf Intestinal Phosphatase (CIP)
APex Heat-Labile Alkaline Phosphatase

36
ALKALINE PHOSPHATASE, CALF INTESTINE-TOBACCO ACID
PYROPHOSPHATASE SEQUENCING (CIP-TAP)
5’ OH
5’ P
5’ PPP
5’ GPPP
5’ P
5’ OH
5’ GPPP
5’ OH
5’ GPPP
CIP 3’primer ligation
Gel purify
5’primer ligation
gel purify
cDNAcsRNA
5’ OH
5’ P
5’ OH
TAP cDNA synthesis PCR purification
Alkaline phosphatase, calf intestine-tobacco acid pyrophosphatase sequencing (CIP-TAP) maps capped small RNAs29
. In this method, RNA
is treated with CIP followed by 3’-end linker ligation, then treated with TAP followed by 5’-end linker ligation. The fragments are then reverse-
transcribed to cDNA, PCR-amplified, and sequenced. Deep sequencing provides single-nucleotide resolution reads of the capped small RNAs.
Pros Cons
• Identifies capped small RNAs missed by CAP-Seq
• High throughput
• Amplification errors caused by polymerases
References
Yang L., Lin C., Jin C., Yang J. C., Tanasa B., et al. (2013) lncRNA-dependent mechanisms of androgen-receptor-regulated gene activation
programs. Nature 500: 598-602
LncRNAs have recently been indicated to play a role in physiological aspects of cell-type determination and tissue homeostasis. In this
paper, the authors applied three sequencing assays (GRO-Seq, ChIRP-Seq, and ChIP-Seq) using the Illumina HiSeq 2000 platform to study
expression and epigenetic profiles of prostate cancer cells. The authors found two lncRNAs highly overexpressed and showed that they
enhance androgen-receptor-mediated gene activation programs and proliferation of prostate cancer cells.
29 Gu W., Lee H. C., Chaves D., Youngman E. M., Pazour G. J., et al. (2012) CapSeq and CIP-TAP identify Pol II start sites and reveal capped small RNAs as C. elegans piRNA precursors. Cell 151:
1488-1500

37
Gu W., Lee H. C., Chaves D., Youngman E. M., Pazour G. J., et al. (2012) CapSeq and CIP-TAP identify Pol II start sites and reveal capped
small RNAs as C. elegans piRNA precursors. Cell 151: 1488-1500
Small RNA molecules account for many different functions in the cell. Piwi-interacting RNAs (piRNAs) represent one type of germline-
expressed small RNAs linked to epigenetic programming. This study presents CAP-Seq, an assay developed to characterize the transcription
of piRNAs in C. elegans. To their surprise, the authors found that likely piRNA precursors are capped small RNAs that initiate precisely 2
ntupstream of mature piRNAs. In addition, they identified a new class of piRNAs, further adding to the complexity of small RNA molecules.
, HiSeq 2000
Associated Kits
Enzyme Solutions:
Calf Intestinal Phosphatase (CIP)
APex Heat-Labile Alkaline Phosphatase

38
INOSINE CHEMICAL ERASING SEQUENCING (ICE)
cDNA
Acrylonitrile
N1-cyanoethylinosine
C GICT
Inosine residue
Control C GICT
C GCT
C GGCTReverse transcription
PCR amplification
Reverse transcription
PCR amplification
X
Inosine chemical erasing (ICE)30
identifies adenosine to inosine editing. In this method, RNA is treated with acrylonitrile, while control RNA is
untreated. Control and treated RNAs are then reverse-transcribed and PCR-amplified. Inosines in RNA fragments treated with acrylonitrile cannot
be reverse-transcribed. Deep sequencing of the cDNA of control and treated RNA provides high-resolution reads of inosines in RNA fragments.
Pros Cons
• Mapping of adenosine to inosine editing
• Can be performed with limited material
• Non-linear PCR amplification can lead to biases,
• Amplification errors caused by polymerases will be represented and
sequenced incorrectly
Associated Kits
30 Sakurai M., Yano T., Kawabata H., Ueda H. and Suzuki T. (2010) Inosine cyanoethylation identifies A-to-I RNA editing sites in the human transcriptome. Nat Chem Biol 6: 733-740
Inosine
N
CH2
OH
O
O
OH OH
NHN
N
N1-cyanoethyl inosine
N
Acrylonitrile
CH2
OH
O
OH OH
N
N
O
NN
N

39
M6
A-SPECIFIC METHYLATED RNA IMMUNOPRECIPITATION SEQUENCING (MERIP-SEQ)
cDNAReverse
Transcription
RBPRBP Extract RNA Fractionate RNA
Methylated RNA
Immunoprecipitate
m6
A-specific methylated RNA immunoprecipitation with next generation sequencing (MeRIP-Seq)31
maps m6
A methylated RNA. In this method,
m6
A-specific antibodies are used to immunoprecipitate RNA. RNA is then reverse-transcribed to cDNA and sequenced. Deep sequencing provides
high resolution reads of m6A-methylated RNA.
Pros Cons
• Maps m6
A methylated RNA • Antibodies not specific to target will precipitate nonspecific
RNA modifications
31 Meyer K. D., Saletore Y., Zumbo P., Elemento O., Mason C. E., et al. (2012) Comprehensive analysis of mRNA methylation reveals enrichment in 3’ UTRs and near stop codons. Cell 149: 1635-1646
References
Meyer K. D., Saletore Y., Zumbo P., Elemento O., Mason C. E., et al. (2012) Comprehensive analysis of mRNA methylation reveals
enrichment in 3’ UTRs and near stop codons. Cell 149: 1635-1646
In addition to DNA, RNA may also carry epigenetic modifications. Methylation of the N6 position of adenosine (m6A) has been implicated
in the regulation of physiological processes. In this study, the authors apply MeRIP-Seq to determine mammalian genes containing m6A in
their mRNA. The sites of m6A residues are enriched near stop codons and in 3’-untranslated regions (3’-UTRs), pointing to a non-random
distribution and possibly functional relevance of methylated RNA transcripts.
, HiSeq 2000
Associated Kits
EpiGnome™
Methyl-Seq®
Kit

40
LOW-LEVEL RNA DETECTION
Low-level RNA detection refers to both detection of rare RNA molecules in a cell-free environment, such as circulating tumor RNA, or the
expression patterns of single cells. Tissues consist of a multitude of different cell types, each with a distinctly different set of functions. Even within
a single cell type, the transcriptomes are highly dynamic and reflect temporal, spatial, and cell cycle–dependent changes. Cell harvesting, handling,
and technical issues with sensitivity and bias during amplification add an additional level of complexity. To resolve this multi-tiered complexity would
require the analysis of many thousands of cells. The use of unique barcodes has greatly increased the number of samples that can be multiplexed
and pooled, with little to no decrease in reads associated with each sample. Recent improvements in cell capture and sample preparation will
provide more information, faster, and at lower cost32
. This promises to fundamentally expand our understanding of cell function with significant
implications for research and human health33
.
Organs, such as the kidney depicted in this cross-section, consist of a myriad of
phenotypically distinct cells. Single-cell transcriptomics can characterize the function of
each of these cell types.
Reviews
0Blainey P. C. (2013) The future is now: single-cell genomics of bacteria and archaea. FEMS Microbiol Rev 37: 407-427
Lovett M. (2013) The applications of single-cell genomics. Hum Mol Genet 22: R22-26
Shapiro E., Biezuner T. and Linnarsson S. (2013) Single-cell sequencing-based technologies will revolutionize whole-organism science.
Nat Rev Genet 14: 618-630
Spaethling J. M. and Eberwine J. H. (2013) Single-cell transcriptomics for drug target discovery. Curr Opin Pharmacol 13: 786-790
32 Shapiro E., Biezuner T. and Linnarsson S. (2013) Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat Rev Genet 14: 618-630
33 Spaethling J. M. and Eberwine J. H. (2013) Single-cell transcriptomics for drug target discovery. Curr Opin Pharmacol 13: 786-790

41
References
Shalek A. K., Satija R., Adiconis X., Gertner R. S., Gaublomme J. T., et al. (2013) Single-cell transcriptomics reveals bimodality in expression
and splicing in immune cells. Nature 498: 236-240
Xue Z., Huang K., Cai C., Cai L., Jiang C. Y., et al. (2013) Genetic programs in human and mouse early embryos revealed by single-cell RNA
sequencing. Nature 500: 593-597
Yan L., Yang M., Guo H., Yang L., Wu J., et al. (2013) Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem
cells. Nat Struct Mol Biol 20: 1131-1139
Goetz J. J. and Trimarchi J. M. (2012) Transcriptome sequencing of single cells with Smart-Seq. Nat Biotechnol 30: 763-765

42
DIGITAL RNA SEQUENCING
cDNA1
cDNA2
cDNA1
cDNA2
Amplify SequenceAdapters with
unique barcodes
Align sequences and
determine actual ratio
based on barcodes
Some fragments
amplify preferentially
True RNA
abundance
cDNA1
cDNA2
Digital RNA sequencing is an approach to RNA-Seq that removes sequence-dependent PCR amplification biases by barcoding the RNA molecules
before amplification34
. RNA is reverse-transcribed to cDNA, then an excess of adapters, each with a unique barcode, is added to the preparation.
This barcoded cDNA is then amplified and sequenced. Deep sequencing reads are compared, and barcodes are used to determine the actual
ratio of RNA abundance.
Pros Cons
• Low amplification bias during PCR
• Information about abundance of RNA
• Detection of low-copy–number RNA
• Single-copy resolution
• Some amplification bias still persists
• Barcodes may miss targets during ligation
Associated Kits
34 Shiroguchi K., Jia T. Z., Sims P. A. and Xie X. S. (2012) Digital RNA sequencing minimizes sequence-dependent bias and amplification noise with optimized single-molecule barcodes. Proc Natl
Acad Sci U S A 109: 1347-1352
References
Shiroguchi K., Jia T. Z., Sims P. A. and Xie X. S. (2012) Digital RNA sequencing minimizes sequence-dependent bias and amplification noise
with optimized single-molecule barcodes. Proc Natl Acad Sci U S A 109: 1347-1352
Experimental protocols that include PCR as an amplification step are subject to the sequence-dependent bias of the PCR. For RNA-Seq,
this results in difficulties in quantifying expression levels, especially at very low copy numbers. In this study, digital RNA-Seq is introduced as
an accurate method for quantitative measurements by appending unique barcode sequences to the pool of RNA fragments. The authors
demonstrate how digital RNA-Seq allows transcriptome profiling of Escherichia coli with more accurate and reproducible quantification than
conventional RNA-Seq. The efficacy of optimization was estimated by comparison to simulated data.

43
WHOLE-TRANSCRIPT AMPLIFICATION FOR SINGLE CELLS (QUARTZ-SEQ)
AAAAA AAAAA
TTTTT
TTTTT T7 PCR
Add polyA primer
with T7 promoter
and PCR target
AAAAA
TTTTT
Reverse transcription
and Primer digestion
T7 PCR T7 PCR
Poly A addition and
oligo dT primer with
PCR target
Generate
second strand
Add blocking
primer
Enrich with
suppression PCR
TTTTT
PCR
TTTTT T7 PCR
AAAAA
TTTTT
PCR
AAAAA
TTTTT T7 PCR
AAAAA
Blocking primer with LNA
cDNA
The Quartz-Seq method optimizes whole-transcript amplification (WTA) of single cells35
. In this method, a reverse-transcription (RT) primer with a
T7 promoter and PCR target is first added to extracted mRNA. Reverse transcription synthesizes first-strand cDNA, after which the RT primer is
digested by exonuclease I. A poly(A) tail is then added to the 3’ ends of first-strand cDNA, along with a dT primer containing a PCR target. After
second-strand generation, a blocking primer is added to ensure PCR enrichment in sufficient quantity for sequencing. Deep sequencing allows for
accurate, high-resolution representation of the whole transcriptome of a single cell.
Pros Cons
• Single-tube reaction suitable for automation
• Digestion of RT primers by exonuclease I eliminates amplification
of byproducts
• Short fragments and byproducts are suppressed
during enrichment
• PCR biases can underrepresent GC-rich templates
• Targets smaller than 500 bp are preferentially amplified by
polymerases during PCR
Associated Kits
35 Sasagawa Y., Nikaido I., Hayashi T., Danno H., Uno K. D., et al. (2013) Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression
heterogeneity. Genome Biol 14: R31
References
Sasagawa Y., Nikaido I., Hayashi T., Danno H., Uno K. D., et al. (2013) Quartz-Seq: a highly reproducible and sensitive single-cell RNA
sequencing method, reveals non-genetic gene-expression heterogeneity. Genome Biol 14: R31
Individual cells may exhibit variable gene expression even if they share the same genome. The analysis of single-cell variability in gene
expression requires robust protocols with a minimum of bias. This paper presents a novel single-cell RNA-Seq method, Quartz-Seq, based
on Illumina sequencing that has a simpler protocol and higher reproducibility and sensitivity than existing methods. The authors implemented
improvements in three main areas: 1) they optimized the protocol for suppression of byproduct synthesis; 2) they identified a robust PCR
enzyme to allow a single-tube reaction; and 3) they determined optimal conditions for RT and second-strand synthesis.

44
DESIGNED PRIMER–BASED RNA SEQUENCING (DP-SEQ)
DNAcDNA
Define set of
heptamer primers
PolyA selection First strand
cDNA synthesis
Hybridize primers PCR
AA(A)n
TT(T)n
No secondary
structure
Unique sequence
AA(A)n
TT(T)n
Designed Primer–based RNA sequencing (DP-Seq) is a method that amplifies mRNA from limited starting material, as low as 50 pg36
. In this
method, a specific set of heptamer primers are first designed. Enriched poly(A)-selected mRNA undergoes first-strand cDNA synthesis. Designed
primers are then hybridized to first-strand cDNA, followed by second strand synthesis and PCR. Deep sequencing of amplified DNA allows for
accurate detection of specific mRNA expression at the single-cell level.
Pros Cons
• As little as 50 pg of starting material can be used
• Little transcript-length bias
• The sequences of the target areas must be known to design
the heptamers
• Exponential amplification during PCR can lead to primer-dimers and
spurious PCR products37
• Some read-length bias
36 Sasagawa Y., Nikaido I., Hayashi T., Danno H., Uno K. D., et al. (2013) Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression
heterogeneity. Genome Biol 14: R31
37 Bhargava V., Head S. R., Ordoukhanian P., Mercola M. and Subramaniam S. (2014) Technical variations in low-input RNA-seq methodologies. Sci Rep 4: 3678
References
Bhargava V., Ko P., Willems E., Mercola M. and Subramaniam S. (2013) Quantitative transcriptomics using designed primer-based
amplification. Sci Rep 3: 1740
Standard amplification of RNA transcripts before sequencing is prone to introduce bias. This paper presents a protocol for selecting a unique
subset of primers to target the majority of expressed transcripts in mouse for amplification while preserving their relative abundance. This
protocol was developed for Illumina sequencing platforms and the authors show how the protocol yielded high levels of amplification from as
little as 50 pg of mRNA, while offering a dynamic range of over five orders of magnitude.
Associated Kits

45
SWITCH MECHANISM AT THE 5’ END OF RNA TEMPLATES (SMART-SEQ)
mRNA fragment
AAAAAAA
Second strand synthesis
AAAAAAA
TTTTTTT
DNA
TTTTTTT
Adaptor
Adaptor
PCR amplification PurifyFirst strand synthesis with Moloney murine
leukemia virus reverse transcriptase
CCC
CCC
Smart-Seq was developed as a single-cell sequencing protocol with improved read coverage across transcripts38
. Complete coverage across the
genome allows the detection of alternative transcript isoforms and single-nucleotide polymorphisms. In this protocol, cells are lysed and the RNA
hybridized to an oligo(dT)-containing primer. The first strand is then created with the addition of a few untemplated C nucleotides. This poly(C)
overhang is added exclusively to full-length transcripts. An oligonucleotide primer is then hybridized to the poly(C) overhang and used to synthesize
the second strand. Full-length cDNAs are PCR-amplified to obtain nanogram amounts of DNA. The PCR products are purified for sequencing.
Pros Cons
• The sequence of the mRNA does not have to be known
• Improved coverage across transcripts
• High level of mappable reads
• Not strand-specific
• No early multiplexing39
• Transcript length bias with inefficient transcription of reads
over 4 Kb40
• Preferential amplification of high-abundance transcripts
• The purification step may lead to loss of material
• Could be subject to strand-invasion bias41
38 Ramskold D., Luo S., Wang Y. C., Li R., Deng Q., et al. (2012) Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol 30: 777-782
41 Tang D. T., Plessy C., Salimullah M., Suzuki A. M., Calligaris R., et al. (2013) Suppression of artifacts and barcode bias in high-throughput transcriptome analyses utilizing template switching.
Nucleic Acids Res 41: e44
References
Kadkhodaei B., Alvarsson A., Schintu N., Ramsköld D., Volakakis N., et al. (2013) Transcription factor Nurr1 maintains fiber integrity and
nuclear-encoded mitochondrial gene expression in dopamine neurons. Proc Natl Acad Sci U S A 110: 2360-2365
Developmental transcription factors important in early neuron differentiation are often found expressed also in the adult brain. This study set
out to investigate the development of ventral midbrain dopamine (DA) neurons by studying the transcriptional expression in a mouse model
system. By using the Smart-Seq method, which allows sequencing from low amounts of total RNA, the authors could sequence RNA from
laser-microdissected DA neurons. Their analysis showed transcriptional activation of the essential transcription factor Nurr1 and its key role in
sustaining healthy DA cells.
Illumina Technology: HiSeq 2000, Genomic DNA Sample Prep Kit (FC-102-1001; Illumina)

46
Marinov G. K., Williams B. A., McCue K., Schroth G. P., Gertz J., et al. (2014) From single-cell to cell-pool transcriptomes: Stochasticity in
gene expression and RNA splicing. Genome Res 24: 496-510
Recent studies are increasingly discovering cell-to-cell variability in gene expression levels and transcriptional regulation. This study examined
the lymphoblastoid cell line GM12878 using the Smart-Seq single-cell RNA-Seq protocol on the Illumina HiSeq 2000 platform to determine
variation in transcription among individual cells. The authors determined, through careful quantification, that there aresignificant differences in
expression among individual cells, over and above technical variation. In addition, they showed that the transcriptomes from small pools of
30-100 cells approach the information content and reproducibility of contemporary pooled RNA-Seq analysis from large amounts of
input material.
Illumina Technology: Nextera DNA®
Sample Prep Kit, HiSeq 2000
Shalek A. K., Satija R., Adiconis X., Gertner R. S., Gaublomme J. T., et al. (2013) Single-cell transcriptomics reveals bimodality in expression
and splicing in immune cells. Nature 498: 236-240
Individual cells can exhibit substantial differences in gene expression, and only recently have genome profiling methods been developed to
monitor the expression of single cells. This study applied the Smart-Seq single-cell RNA sequencing on the Illumina HiSeq 2000 platform to
investigate heterogeneity in the response of mouse bone marrow–derived dendritic cells (BMDCs) to lipopolysaccharide. The authors found
extensive bimodal variation in mRNA abundance and splicing patterns, which was subsequently validated using RNA fluorescence in situ
hybridization for select transcripts.
Yamaguchi S., Hong K., Liu R., Inoue A., Shen L., et al. (2013) Dynamics of 5-methylcytosine and 5-hydroxymethylcytosine during germ cell
reprogramming. Cell Res 23: 329-339
Mouse primordial germ cells (PGCs) undergo genome-wide DNA methylation reprogramming to reset the epigenome for totipotency. In this
study, the dynamics between 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) were characterized using immunostaining tech-
niques and analyzed in combination with transcriptome profiles obtained with Illumina RNA sequencing. The study revealed that the dynam-
ics of 5mC and 5hmC during PGC reprogramming support a model in which DNA demethylation in PGCs occurs through multiple steps, with
both active and passive mechanisms. In addition, the transcriptome study suggests that PGC reprogramming may have an important role in
the activation of a subset of meiotic and imprinted genes.
Ramskold D., Luo S., Wang Y. C., Li R., Deng Q., et al. (2012) Full-length mRNA-Seq from single-cell levels of RNA and individual circulating
tumor cells. Nat Biotechnol 30: 777-782
Yamaguchi S., Hong K., Liu R., Shen L., Inoue A., et al. (2012) Tet1 controls meiosis by regulating meiotic gene expression.
Nature 492: 443-447
Associated Kits
Nextera DNA Sample Prep Kit

47
SWITCH MECHANISM AT THE 5’ END OF RNA TEMPLATES VERSION 2 (SMART-SEQ2)
Smart-Seq2 includes several improvements over the original Smart-Seq protocol42,43
. The new protocol includes a locked nucleic acid (LNA),
an increased MgCl2
concentration, betaine, and elimination of the purification step to significantly improve the yield. In this protocol, single cells
are lysed in a buffer that contains free dNTPs and oligo(dT)-tailed oligonucleotides with a universal 5’-anchor sequence. Reverse transcription
is performed, which adds 2–5 untemplated nucleotides to the cDNA 3’ end. A template-switching oligo (TSO) is added, carrying two
riboguanosines and a modified guanosine to produce a LNA as the last base at the 3’ end. After the first-strand reaction, the cDNA is amplified
using a limited number of cycles. Tagmentation is then used to quickly and efficiently construct sequencing libraries from the amplified cDNA.
Pros Cons
• The sequence of the mRNA does not have to be known
• Improved coverage across transcripts
• High level of mappable reads
• Not strand-specific
• No early multiplexing
• Applicable only to poly(A)+ RNA
42 Picelli S., Bjorklund A. K., Faridani O. R., Sagasser S., Winberg G., et al. (2013) Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods 10: 1096-1098
43 Picelli S., Faridani O. R., Björklund Å. K., Winberg G., Sagasser S., et al. (2014) Full-length RNA-Seq from single cells using Smart-seq2. Nat. Protocols 9: 171-181
Betaine
mRNA fragment
AAAAAA
cDNA synthesis Tagmentation
AAAAAA
AAAAAATTTTTT TTTTTT
Adaptor
PCRFirst strand synthesis with
Moloney murine leukemia
virus reverse transcriptase
CCC
CCC GGG
Tem-
plate-switc
hing
oligoo
Locked nucleic acid (LNA)
CCC
GGG
Enrichment-ready fragment
P5 P7
Index 1Index 2
Gap repair,
enrichment PCR and
PCR purification
CH3
CH3
H2
C C
O
O
NH3
C
+

48
References
Picelli S., Bjorklund A. K., Faridani O. R., Sagasser S., Winberg G., et al. (2013) Smart-seq2 for sensitive full-length transcriptome profiling in
single cells. Nat Methods 10: 1096-1098
Single-cell gene expression analyses hold promise for characterizing cellular heterogeneity, but current methods compromise on the
coverage, sensitivity, or throughput. This paper introduces Smart-Seq2 with improved reverse transcription, template switching, and
preamplification to increase both yield and length of cDNA libraries generated from individual cells. The authors evaluated the efficacy of
the Smart-Seq2 protocol using the Illumina HiSeq 2000 platform and concluded that Smart-Seq2 transcriptome libraries have improved
detection, coverage, bias, and accuracy compared to Smart-Seq libraries. In addition, they are generated with off-the-shelf reagents at
lower cost.
Illumina Technology: Nextera DNA Sample Prep Kit, HiSeq 2000
Associated Kits

49
UNIQUE MOLECULAR IDENTIFIERS (UMI)
mRNA fragment
AAAAAAA
First strand synthesis Second strand synthesis
AAAAAAA
TTTTTTT
P7
True variant
Random error
DNA
TTTTTTT
P5
Index
Degenerate molecular tag (N10)
PCR amplification Align fragments from every
unique molecular tag
CCC CCC
Unique molecular identifiers (UMI) is a method that uses molecular tags to detect and quantify unique mRNA transcripts44
. In this method, mRNA
libraries are generated by fragmentation and then reverse-transcribed to cDNA. Oligo(dT) primers with specific sequencing linkers are added to
cDNA. Another sequencing linker with a 10 bp random label and an index sequence is added to the 5’ end of the template, which is amplified and
sequenced. Sequencing allows for high-resolution reads, enabling accurate detection of true variants.
Pros Cons
• Can sequence unique mRNA transcripts
• Can be used to detect transcripts occurring at low frequencies
• Transcripts can be quantified based on sequencing reads
specific to each barcode
• Can be applied to multiple platforms to karyotype chromosomes
as well
44 Kivioja T., Vaharautio A., Karlsson K., Bonke M., Enge M., et al. (2012) Counting absolute numbers of molecules using unique molecular identifiers. Nat Methods 9: 72-74
References
Islam S., Zeisel A., Joost S., La Manno G., Zajac P., et al. (2014) Quantitative single-cell RNA-seq with unique molecular identifiers. Nat
Methods 11: 163-166
Gene expression varies among different tissues, in effect giving rise to different tissue types out of undifferentiated cells; however, expression
also varies among different cells in the same tissue. Most assays for measuring gene expression depend on input material from multiple cells,
but in this study a method for single-cell RNA sequencing is presented based on Illumina sequencing technology. This technology can be
applied to characterize sources of transcriptional noise, or to study expression in early embryos and other sample types where the cell count
is naturally limited. One attractive possibility is the application of single-cell sequencing to assess cell type diversity in complex tissues.

50
Murtaza M., Dawson S. J., Tsui D. W., Gale D., Forshew T., et al. (2013) Non-invasive analysis of acquired resistance to cancer therapy by
sequencing of plasma DNA. Nature 497: 108-112
Recent studies have shown that genomic alterations in solid cancers can be characterized by sequencing of circulating cell-free tumor DNA
released from cancer cells into plasma, representing a non-invasive liquid biopsy. This study describes how this approach was applied using
Illumina HiSeq sequencing technology to track the genomic evolution of metastatic cancers in response to therapy. Six patients with breast,
ovarian, and lung cancers were followed over 1–2 years. For two cases, synchronous biopsies were also analyzed, confirming genome-wide
representation of the tumor genome in plasma and establishing the proof-of-principle of exome-wide analysis of circulating tumor DNA.
Illumina Technology: TruSeq Exome®
Enrichment Kit, HiSeq 2000
Kivioja T., Vaharautio A., Karlsson K., Bonke M., Enge M., et al. (2012) Counting absolute numbers of molecules using unique molecular
identifiers. Nat Methods 9: 72-74
This is the first paper to describe the UMI method and its utility as a tool for sequencing. The authors use UMIs, which make each molecule
in a population distinct for genome-scale karyotyping and mRNA sequencing.
, HiSeq 2000
Associated Kits

51
CELL EXPRESSION BY LINEAR AMPLIFICATION SEQUENCING (CEL-SEQ)
AA(A)n
AA(A)n
AA(A)n
AA(A)n
AA(A)n
AA(A)nCell 1
Cell 2
Cell 3
T7prom
oter
Unique
index
5’adaptor
TT(T)n
TT(T)n
TT(T)n
TT(T)n
AA(A)n
AA(A)n
AA(A)n
TT(T)n
TT(T)n
TT(T)n
Second strand
RNA synthesis
Fragment, add
adapters and
reverse transcribe
Separate cell sequences
based on unique indices
Pool
Cell 3
Cell 2
Cell 1
PCR
Cell expression by linear amplification sequencing (CEL-Seq) is a method that utilizes barcoding and pooling of RNA to overcome challenges from
low input45
. In this method, each cell undergoes reverse transcription with a unique barcoded primer in its individual tube. After second-strand
synthesis, cDNAs from all reaction tubes are pooled, and PCR-amplified. Paired-end deep sequencing of the PCR products allows for accurate
detection of sequence derived from sequencing both strands.
Pros Cons
• Barcoding and pooling allow for multiplexing and studying many
different single cells at a time
• Cross-contamination is greatly reduced due to using one tube
per cell
• Fewer steps than STRT-Seq
• Very little read-length bias46
• Strand-specific
• Strongly 3’ biased47
• Abundant transcripts are preferentially amplified
• Requires at least 400 pg of total RNA
45 Hashimshony T., Wagner F., Sher N. and Yanai I. (2012) CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep 2: 666-673
References
Hashimshony T., Wagner F., Sher N. and Yanai I. (2012) CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification.
Cell Rep 2: 666-673
High-throughput sequencing has allowed for unprecedented detail in gene expression analyses, yet its efficient application to single cells is
challenged by the small starting amounts of RNA. This paper presents the CEL-Seq protocol, which uses barcoding, pooling of samples, and
linear amplification with one round of in vitro transcription. The assay is designed around a modified version of the Illumina directional RNA
protocol and sequencing is done on the Illumina HiSeq 2000 system. The authors demonstrate their method by
single-cell expression profiling of early C. elegans embryonic development.
Associated Kits

52
SINGLE-CELL TAGGED REVERSE TRANSCRIPTION SEQUENCING (STRT-SEQ)
AA(A)n
AA(A)n
AA(A)n
Cell 1
Cell 2
Cell 3
TT(T)n
TT(T)n
TT(T)n
AA(A)n
AA(A)n
AA(A)n
TT(T)n
TT(T)n
TT(T)n
CCC
CCC
CCC
cDNA
synthesis
Add 3 to 6
cytosines
TT(T)n
TT(T)n
CCC
CCC
CCC
GGG
GGG
GGG
Template
switching
primer
Introduce
unique index
Add oligo-dT primer Pool Single-primer
PCR and purify
Separate cell sequences
based on unique indices
Cell 3
Cell 2
Cell 1
TT(T)n
Unique
index
5’adaptor
GGG
Single-cell tagged reverse transcription sequencing (STRT-Seq) is a method similar to CEL-seq that involves unique barcoding and sample
pooling to overcome the challenges of samples with limited material48
. In this method, single cells are first picked in individual tubes, where first-
strand cDNA synthesis occurs using an oligo(dT) primer with the addition of 3–6 cytosines. A helper oligo promotes template switching, which
introduces the barcode on the cDNA. Barcoded cDNA is then amplified by single-primer PCR. Deep sequencing allows for accurate transcriptome
sequencing of individual cells.
Pros Cons
• Barcoding and pooling allows for multiplexing and studying
many different single cells at a time
• Sample handling and the potential for cross-contamination are
greatly reduced due to using one tube per cell
• Non-linear PCR amplification can lead to biases affecting
reproducibility
• Loss of accuracy due to PCR bias
48
Islam S., Kjallquist U., Moliner A., Zajac P., Fan J. B., et al. (2011) Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res 21: 1160-1167
References
Islam S., Kjallquist U., Moliner A., Zajac P., Fan J. B., et al. (2011) Characterization of the single-cell transcriptional landscape by highly
multiplex RNA-seq. Genome Res 21: 1160-1167
Gene expression varies among different tissues, in effect giving rise to different tissue types out of undifferentiated cells; however, expression
also varies among different cells in the same tissue. Most assays for measuring gene expression depend on input material from multiple cells,
but in this study a method for single-cell RNA sequencing is presented based on Illumina sequencing technology. This technology can be
applied to characterize sources of transcriptional noise, or to study expression in early embryos and other sample types where the cell count
is naturally limited. One attractive possibility is the application of single-cell sequencing to assess cell type diversity in complex tissues.
Associated Kits

53
LOW-LEVEL DNA DETECTION
Single-cell genomics can be used to identify and study circulating tumor cells, cell-free DNA, microbes, uncultured microbes, for preimplantation
diagnosis, and to help us better understand tissue-specific cellular differentiation49, 50
. DNA replication during cell division is not perfect; as a result,
progressive generations of cells accumulate unique somatic mutations. Consequently, each cell in our body has a unique genomic signature, which
allows the reconstruction of cell lineage trees with very high precision.51
These cell lineage trees can predict the existence of small populations of
stem cells. This information is important for fields as diverse as cancer development52, 53
preimplantation, and genetic diagnosis. 54, 55
50 Blainey P. C. (2013) The future is now: single-cell genomics of bacteria and archaea. FEMS Microbiol Rev 37: 407-427
51
Frumkin D., Wasserstrom A., Kaplan S., Feige U. and Shapiro E. (2005) Genomic variability within an organism exposes its cell lineage tree. PLoS Comput Biol 1: e5
52
Navin N., Kendall J., Troge J., Andrews P., Rodgers L., et al. (2011) Tumour evolution inferred by single-cell sequencing. Nature 472: 90-94
53
Potter N. E., Ermini L., Papaemmanuil E., Cazzaniga G., Vijayaraghavan G., et al. (2013) Single-cell mutational profiling and clonal phylogeny in cancer. Genome Res 23: 2115-2125
54
Van der Aa N., Esteki M. Z., Vermeesch J. R. and Voet T. (2013) Preimplantation genetic diagnosis guided by single-cell genomics. Genome Med 5: 71
55
Hou Y., Fan W., Yan L., Li R., Lian Y., et al. (2013) Genome analyses of single human oocytes. Cell 155: 1492-1506
Reviews:
Blainey P. C. (2013) The future is now: single-cell genomics of bacteria and archaea. FEMS Microbiol Rev 37: 407-427
Shapiro E., Biezuner T. and Linnarsson S. (2013) Single-cell sequencing-based technologies will revolutionize whole-organism science.
Single-cell genomics can help characterize and identify circulating
tumor cells as well as microbes.

54
Baslan T., Kendall J., Rodgers L., Cox H., Riggs M., et al. (2012) Genome-wide copy number analysis of single cells. Nat Protoc 7: 1024-1041
Böttcher R., Amberg R., Ruzius F. P., Guryev V., Verhaegh W. F., et al. (2012) Using a priori knowledge to align sequencing reads to their exact
genomic position. Nucleic Acids Res 40: e125
Kalisky T. and Quake S. R. (2011) Single-cell genomics. Nat Methods 8: 311-314
Navin N. and Hicks J. (2011) Future medical applications of single-cell sequencing in cancer. Genome Med 3: 31
Yilmaz S. and Singh A. K. (2011) Single cell genome sequencing. Curr Opin Biotechnol 23: 437-443
References
Voet T., Kumar P., Van Loo P., Cooke S. L., Marshall J., et al. (2013) Single-cell paired-end genome sequencing reveals structural variation
per cell cycle. Nucleic Acids Res 41: 6119-6138
Hou Y., Song L., Zhu P., Zhang B., Tao Y., et al. (2012) Single-cell exome sequencing and monoclonal evolution of a JAK2-negative
myeloproliferative neoplasm. Cell 148: 873-885

55
Genomic DNA
Degenerate
molecular tag
Copy target sequence Exonuclease Align fragments from
every unique molecular tag
Sample indexRead1
Read2
True variant
Random error
DNAPCR amplification
SINGLE-MOLECULE MOLECULAR INVERSION PROBES (SMMIP)
The single-molecule molecular inversion probes (smMIP) method uses single-molecule tagging and molecular inversion probes to detect and
quantify genetic variations occurring at very low frequencies56
. In this method, probes are used to detect targets in genomic DNA. After the probed
targets are copied, exonuclease digestion leaves the target with a tag, which undergoes PCR amplification and sequencing. Sequencing allows for
high-resolution sequence reads of targets, while greater depth allows for better alignment for every unique molecular tag.
56 Hiatt J. B., Pritchard C. C., Salipante S. J., O’Roak B. J. and Shendure J. (2013) Single molecule molecular inversion probes for targeted, high-accuracy detection of low-frequency variation.
Genome Res 23: 843-854
References
Hiatt J. B., Pritchard C. C., Salipante S. J., O’Roak B. J. and Shendure J. (2013) Single molecule molecular inversion probes for targeted,
high-accuracy detection of low-frequency variation. Genome Res 23: 843-854
This is the first paper to describe the smMIP assay, along with its practicality, ability for multiplexing, scaling, and compatibility with desktop
sequencing for rapid data collection. The authors demonstrated the assay by resequencing 33 clinically informative cancer genes in 8 cell
lines and 45 clinical cancer samples, retrieving accurate data.
Illumina Technology: MiSeq®
, HiSeq 2000
Associated Kits
TruSeq Nano DNA®
Sample Prep Kit
TruSeq DNA PCR-Free®
Sample Prep Kit
Nextera XT DNA®
Sample Prep Kit
Nextera Rapid Capture Exome/Custom®
Enrichment Kit
Pros Cons
• Detection of low-frequency targets
• Can perform single-cell sequencing or sequencing for samples
with very limited starting material
• PCR amplification errors

56
Hybridize primers
Nascent replication fork
Phi 29 Phi 29 S1 nuclease
3’blocked random hexamer primers
Synthesis Synthesis
MULTIPLE DISPLACEMENT AMPLIFICATION (MDA)
Multiple displacement amplification (MDA) is a method commonly used for sequencing microbial genomes due to its ability to amplify templates
larger than 0.5 Mbp, but it can also be used to study genomes of other sizes57
. In this method, 3’-blocked random hexamer primers are hybridized
to the template, followed by synthesis with Phi 29 polymerase. Phi 29 performs strand-displacement DNA synthesis, allowing for efficient and rapid
DNA amplification. Deep sequencing of the amplified DNA allows for accurate representation of reads, while sequencing depth provides better
alignment and consensus for sequences.
57 Dean F. B., Nelson J. R., Giesler T. L. and Lasken R. S. (2001) Rapid amplification of plasmid and phage DNA using Phi 29 DNA polymerase and multiply-primed rolling circle amplification. Genome
Res 11: 1095-1099
58
Navin N., Kendall J., Troge J., Andrews P., Rodgers L., et al. (2011) Tumour evolution inferred by single-cell sequencing. Nature 472: 90-94
59
Woyke T., Sczyrba A., Lee J., Rinke C., Tighe D., et al. (2011) Decontamination of MDA reagents for single cell whole genome amplification. PLoS ONE 6: e26161
References
Embree M., Nagarajan H., Movahedi N., Chitsaz H. and Zengler K. (2013) Single-cell genome and metatranscriptome sequencing reveal
metabolic interactions of an alkane-degrading methanogenic community. ISME J
Microbial communities amass a wealth of biochemical processes, and metagenomics approaches are often unable to decipher the key
functions of individual microorganisms. This study analyzed a microbial community by first determining the genome sequence of a dominant
bacterial member of the genus Smithella, using a single-cell sequencing approach on the Illumina Genome Analyzer. After establishing a
working draft genome of Smithella, the authors used low-input metatranscriptomics to determine which genes were active during alkane
degradation. The authors then designed a genome-scale metabolic model to integrate the genomic and transcriptomic data.
Illumina Technology: Nextera DNA Sample Prep Kit, MiSeq, Genome AnalyzerIIx
Pros Cons
• Templates used for this method can be circular DNA (plasmids,
bacterial DNA)
• Can sequence large templates
• Strong amplification bias. Genome coverage as low as ~6%58
• Contaminated reagents can impact results59

57
Chromosomal crossover occurs in the oocyte, producing unique combinations of the parent chromosomes in the fertilized egg. This paper
presents a protocol for single-cell genome analysis of human oocytes. Using multiple annealing and looping-based amplification cycle
(MALBAC)-based sequencing, the authors sequenced triads of the first and second polar bodies from oocyte pronuclei. These pronuclei
were derived from the same female egg donors and the authors phased their genomes to determine crossover maps for the oocytes. This
breakthrough assay makes important progress toward using whole-genome sequencing for meiosis research and embryo selection for
in vitro fertilization.
McLean J. S., Lombardo M. J., Ziegler M. G., Novotny M., Yee-Greenbaum J., et al. (2013) Genome of the pathogen Porphyromonas gingi-
valis recovered from a biofilm in a hospital sink using a high-throughput single-cell genomics platform. Genome Res 23: 867-877
Single-cell genomics is becoming an accepted method to capture novel genomes, primarily in marine and soil environments. This study
shows, for the first time, that it also enables comparative genomic analysis of strain variation in a pathogen captured from complex biofilm
samples in a healthcare facility. The authors present a nearly complete genome representing a novel strain of the periodontal pathogen
Porphyromonas gingivalis using the single-cell assembly tool SPAdes.
Illumina Technology: Nextera DNA Sample Prep Kit, Genome AnalyzerIIx
Seth-Smith H. M., Harris S. R., Skilton R. J., Radebe F. M., Golparian D., et al. (2013) Whole-genome sequences of Chlamydia
trachomatis directly from clinical samples without culture. Genome Res 23: 855-866
The use of whole-genome sequencing as a tool to study infectious bacteria is of growing clinical interest. Cultures of Chlamydia trachomatis
have, until now, been a prerequisite to obtaining DNA for whole-genome sequencing. Unfortunately, culturing C. trachomatis is a technically
demanding and time-consuming procedure. This paper presents IMS-MDA: a new approach combining immunomagnetic separation (IMS)
and multiple-displacement amplification (MDA) for whole-genome sequencing of bacterial genomes directly from
clinical samples.
Illumina Technology: Genome AnalyzerIIx, HiSeq 2000
Dunowska M., Biggs P. J., Zheng T. and Perrott M. R. (2012) Identification of a novel nidovirus associated with a neurological disease of the
Australian brushtail possum (Trichosurus vulpecula). Vet Microbiol 156: 418-424
Wobbly possum disease (WPD) is a fatal neurological disease of the Australian brushtail possum. In this study, the previously unconfirmed
mechanism of disease transmission was identified as a novel virus. The identification utilized enrichment for viral DNA followed by sequencing
on an Illumina Genome Analyzer.

58
Chitsaz H., Yee-Greenbaum J. L., Tesler G., Lombardo M. J., Dupont C. L., et al. (2011) Efficient de novo assembly of single-cell bacterial
genomes from short-read data sets. Nat Biotechnol 29: 915-921
Woyke T., Tighe D., Mavromatis K., Clum A., Copeland A., et al. (2010) One bacterial cell, one complete genome. PLoS ONE 5: e10314
Valentim C. L., LoVerde P. T., Anderson T. J. and Criscione C. D. (2009) Efficient genotyping of Schistosoma mansoni miracidia following whole
genome amplification. Mol Biochem Parasitol 166: 81-84
Jasmine F., Ahsan H., Andrulis I. L., John E. M., Chang-Claude J., et al. (2008) Whole-genome amplification enables accurate genotyping for
microarray-based high-density single nucleotide polymorphism array. Cancer Epidemiol Biomarkers Prev 17: 3499-3508
Associated Kits
TruSeq Nano DNA Sample Prep Kit
TruSeq DNA PCR-Free Sample Prep Kit
Nextera XT DNA Sample Prep Kit
Nextera Rapid Capture Exome/Custom Enrichment Kit

59
Hybridize primers PCR
27-bp common sequence
8 random nucleotides
Bst DNA
polymerase
partial amplicons
Template
Denature
Denature
Hybridize primers Synthesis
Looped full
amplicons
cycles of quasilinear
MULTIPLE ANNEALING AND LOOPING–BASED AMPLIFICATION CYCLES (MALBAC)
Multiple annealing and looping–based amplification cycles (MALBAC) is intended to address some of the shortcomings of MDA60
. In this method,
MALBAC primers randomly anneal to a DNA template. A polymerase with displacement activity at elevated temperatures amplifies the template,
generating “semi-amplicons.” As the amplification and annealing process is repeated, the semi-amplicons are amplified into full amplicons that
have a 3’ end complimentary to the 5’ end. As a result, full-amplicon ends hybridize to form a looped structure, inhibiting further amplification of the
looped amplicon, while only the semi-amplicons and genomic DNA undergo amplification. Deep sequencing of the full-amplicon sequences allows
for accurate representation of reads, while sequencing depth provides improved alignment for consensus sequences.
60
Zong C., Lu S., Chapman A. R. and Xie X. S. (2012) Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 338: 1622-1626
61
62 Lasken R. S. (2013) Single-cell sequencing in its prime. Nat Biotechnol 31: 211-212
References
Chromosomal crossover occurs in the oocyte, producing unique combinations of the parent chromosomes in the fertilized egg. This paper
presents a protocol for single-cell genome analysis in human oocytes. Using multiple annealing and looping-based amplification cycle
(MALBAC)-based sequencing, the authors sequenced triads of the first and second polar bodies from oocyte pronuclei. These pronuclei
were derived from the same female egg donors and the authors phased their genomes to determine crossover maps for the oocytes. This
breakthrough assay makes important progress toward using whole-genome sequencing for meiosis research and embryo selection for
in vitro fertilization.
Pros Cons
• Can sequence large templates
• Full-amplicon looping inhibits over-representation of templates,
reducing PCR bias
• Can amplify GC-rich regions
• Uniform genome coverage
• Lower allele drop-out rate compared to MDA
• Polymerase is relatively error prone compared to Phi 29
• Temperature-sensitive protocol
• Genome coverage up to ~90%,61
but some regions of the genome
are consistently underrepresented62

60
Ni X., Zhuo M., Su Z., Duan J., Gao Y., et al. (2013) Reproducible copy number variation patterns among single circulating tumor cells of lung
cancer patients. Proc Natl Acad Sci U S A 110: 21083-21088
There is a great deal of interest in identifying and studying circulating tumor cells (CTCs). Cells from primary tumors enter the bloodstream
and can seed metastases. A major barrier to such analysis is low input amounts from single cells, leading to lower coverage. In this study the
authors use MALBAC for whole-genome sequencing of single CTCs from patients with lung cancer. They identify copy-number variations
that were consistent in patients with the same cancer subtype. Such information about cancers can help identify drug resistance and cancer
subtypes, and offers potential for diagnostics, allowing for individualized treatment.
Illumina Technology: MiSeq, HiSeq 2000
Zong C., Lu S., Chapman A. R. and Xie X. S. (2012) Genome-wide detection of single-nucleotide and copy-number variations of a single
human cell. Science 338: 1622-1626
This is the first paper that describes the MALBAC method, which the authors indicate has a higher detection efficiency than the traditional
MDA method for single-cell studies. The authors show detection of copy-number variations and single-nucleotide variations of single cancer
cells with no false positives.
Associated Kits

61
Extend
Denature
Extend
Denature
Extend
Denature
Fragment
Add single adaptors
Target sequence
Adaptor sequence
Flow cell
Sequencing Primers
Target sequence Single adaptor library
Hybridize Hybridize Sequence reads
1 and 2
Sequence
Create target-specific oligos
OLIGONUCLEOTIDE-SELECTIVE SEQUENCING (OS-SEQ)
Oligonucleotide-selective sequencing (OS-Seq)63
was developed to improve targeted resequencing, by capturing and sequencing gene targets
directly on the flow cell. In this method target sequences with adapters are used to modify the flow cell primers. Targets in the template are
captured onto the flow cell with the modified primers. Further extension, denaturation, and hybridization provide sequence reads for target genes.
Deep sequencing provides accurate representation of reads.
63 Myllykangas S., Buenrostro J. D., Natsoulis G., Bell J. M. and Ji H. P. (2011) Efficient targeted resequencing of human germline and cancer genomes by oligonucleotide-selective sequencing. Nat
Biotechnol 29: 1024-1027
References
Myllykangas S., Buenrostro J. D., Natsoulis G., Bell J. M. and Ji H. P. (2011) Efficient targeted resequencing of human germline and cancer
genomes by oligonucleotide-selective sequencing. Nat Biotechnol 29: 1024-1027
As a new method for targeted genome resequencing, the authors present OS-Seq. The method uses a modification of the immobilized lawn
of oligonucleotide primers on the flow cell to function as both a capture and sequencing substrate. The method is demonstrated by targeted
sequencing of tumor/normal tissue from colorectal cancer.
Associated Kits
Pros Cons
• Can resequence multiple targets at a time
• No gel excision or narrow size purification required
• Very fast (single-day) protocol
• Samples can be multiplexed
• Reduced PCR bias due to removal of amplification steps
• Avoids loss of material
• Primers may interact with similar target sequences, leading to
sequence ambiguity

62
α βP5
P7 P5
P7
A mutation occurs on
both strands
12 random
base index
12 random
base index
True variantRandom error
Ligate and PCR Rare variantSequence Create single strand
consensus sequence from
every unique molecular tag
Consensus
Create duplex sequences
based on molecular tags
and sequencing primers
Add
Adaptors
DUPLEX SEQUENCING (DUPLEX-SEQ)
Duplex sequencing is a tag-based error correction method to improve sequencing accuracy64
. In this method, adapters (with primer sequences
and random 12 bp indices) are ligated onto the template and amplified using PCR. Deep sequencing provides consensus sequence information
from every unique molecular tag. Based on molecular tags and sequencing primers, duplex sequences are aligned, determining the true sequence
on each DNA strand.
64 Schmitt M. W., Kennedy S. R., Salk J. J., Fox E. J., Hiatt J. B., et al. (2012) Detection of ultra-rare mutations by next-generation sequencing. Proc Natl Acad Sci U S A 109: 14508-14513
Pros Cons
• Very low error rate due to duplex tagging system
• PCR amplification errors can be detected and removed
from analysis
• No additional library preparation steps after addition of adapters
• Non-linear PCR amplification can lead to biases affecting
reproducibility
References
Kennedy S. R., Salk J. J., Schmitt M. W. and Loeb L. A. (2013) Ultra-sensitive sequencing reveals an age-related increase in somatic
mitochondrial mutations that are inconsistent with oxidative damage. PLoS Genet 9: e1003794
Studies of mitochondrial DNA (mtDNA) mutations have been limited due to technical limitations of the protocols applied. In this paper, the
authors present a highly sensitive Duplex-Seq method, based on the HiSeq platform, which can detect a single mutation among 107
wild-type molecules. The authors applied the method to study the accumulation of mutations in mtDNA over the course of 80 years of life.
Their results show that the mutation spectra of brain tissue of old compared to young individuals are dominated by transition mutations and
not G to T mutations, which are the characteristic mutations caused by oxidative damage.
Illumina Technology: HiSeq 2000/2500; 101 bp paired-end reads
Schmitt M. W., Kennedy S. R., Salk J. J., Fox E. J., Hiatt J. B., et al. (2012) Detection of ultra-rare mutations by next-generation sequencing.
The authors propose a tag-based error correction method to improve sequencing accuracy, especially in heterogeneous samples. The
method allows double-stranded DNA sequence read collection, proving mutation status on both strands. The method is demonstrated by
sequencing M13mp2 DNA. This method is proposed to be useful for assessing mutations due to DNA damage, as well as the determining
the mutational status of genes on both DNA strands.
Associated Kits

63
DNA METHYLATION
DNA methylation and hydroxymethylation are involved in development, X-chromosome inactivation, cell differentiation, tissue-specific gene
expression, plant epigenetic variation, imprinting, cancers, and diseases65,66,67,68
. Methylation usually occurs at the 5’ position of cytosines and
plays a crucial role in gene regulation and chromatin remodeling.
65 Smith Z. D. and Meissner A. (2013) DNA methylation: roles in mammalian development. Nat Rev Genet 14: 204-220
66 Jullien P. E. and Berger F. (2010) DNA methylation reprogramming during plant sexual reproduction? Trends Genet 26: 394-399
67
Schmitz R. J., He Y., Valdes-Lopez O., Khan S. M., Joshi T., et al. (2013) Epigenome-wide inheritance of cytosine methylation variants in a recombinant inbred population. Genome Res 23:
1663-1674
68 Koh K. P. and Rao A. (2013) DNA methylation and methylcytosine oxidation in cell fate decisions. Curr Opin Cell Biol 25: 152-161
69 Dolinoy D. C., Weidman J. R., Waterland R. A. and Jirtle R. L. (2006) Maternal genistein alters coat color and protects Avy mouse offspring from obesity by modifying the fetal epigenome. Environ
Health Perspect 114: 567-572
70
Dolinoy D. C. (2008) The agouti mouse model: an epigenetic biosensor for nutritional and environmental alterations on the fetal epigenome. Nutr Rev 66 Suppl 1: S7-11, Dolinoy D. C. and Faulk C.
(2012) Introduction: The use of animals models to advance epigenetic science. ILAR J 53: 227-231
71 Pfeifer G. P., Kadam S. and Jin S. G. (2013) 5-hydroxymethylcytosine and its potential roles in development and cancer. Epigenetics Chromatin 6: 10
72 Thomson J. P., Lempiainen H., Hackett J. A., Nestor C. E., Muller A., et al. (2012) Non-genotoxic carcinogen exposure induces defined changes in the 5-hydroxymethylome. Genome Biol 13: R93
The active agouti gene in mice codes for yellow coat color. When pregnant mice with the
active agouti gene are fed a diet rich in methyl donors, the offspring are born with the
agouti gene turned off69
This effect has been used as an epigenetic biosensor for nutritional
and environmental alterations on the fetal epigenome70
.
Most cytosine methylation occurs on cytosines located near guanines, called CpG sites. These CpG sites are often located upstream of
promoters, or within the gene body. CpG islands are defined as regions that are greater than 500 bp in length with greater than 55% GC and an
expected/observed CpG ratio of 0.65.
While cytosine methylation (5mC) is known as a silencing mark that represses genes, cytosine hydroxymethylation (5hmC) is shown to be an
activating mark that promotes gene expression and is a proposed intermediate in the DNA demethylation pathway1,4,6
. Similar to 5mC, 5hmC
is involved during development, cancers, cell differentiation, and diseases71
.
5mC and/or 5hmC can be a diagnostic tool to help identify the effects of nutrition, carcinogens72
, and environmental factors in relation to diseases.
The impact of these modifications on gene regulation depends on their locations within the genome. It is therefore important to determine the
exact position of the modified bases.

64
Sequencing reads created by various methods
Reviews
Koh K. P. and Rao A. (2013) DNA methylation and methylcytosine oxidation in cell fate decisions. Curr Opin Cell Biol 25: 152-161
Lister R., Mukamel E. A., Nery J. R., Urich M., Puddifoot C. A., et al. (2013) Global epigenomic reconfiguration during mammalian brain
development. Science 341: 1237905
Pfeifer G. P., Kadam S. and Jin S. G. (2013) 5-hydroxymethylcytosine and its potential roles in development and cancer. Epigenetics
Chromatin 6: 10
Piccolo F. M. and Fisher A. G. (2014) Getting rid of DNA methylation. Trends Cell Biol 24: 136-143
Rivera C. M. and Ren B. (2013) Mapping human epigenomes. Cell 155: 39-55
Schweiger M. R., Barmeyer C. and Timmermann B. (2013) Genomics and epigenomics: new promises of personalized medicine for cancer
patients. Brief Funct Genomics 12: 411-421
Smith Z. D. and Meissner A. (2013) DNA methylation: roles in mammalian development. Nat Rev Genet 14: 204-220
Telese F., Gamliel A., Skowronska-Krawczyk D., Garcia-Bassets I. and Rosenfeld M. G. (2013) “Seq-ing” insights into the epigenetics of neuronal
gene regulation. Neuron 77: 606-623
Veluchamy A., Lin X., Maumus F., Rivarola M., Bhavsar J., et al. (2013) Insights into the role of DNA methylation in diatoms by genome-wide
profiling in Phaeodactylum tricornutum. Nat Commun 4: 2091
Vidaki A., Daniel B. and Court D. S. (2013) Forensic DNA methylation profiling--Potential opportunities and challenges. Forensic Sci Int
Genet 7: 499-507
References
Meaburn E. and Schulz R. (2012) Next generation sequencing in epigenetics: insights and challenges. Semin Cell Dev Biol 23: 192-199
Thomson J. P., Lempiainen H., Hackett J. A., Nestor C. E., Muller A., et al. (2012) Non-genotoxic carcinogen exposure induces defined changes
in the 5-hydroxymethylome. Genome Biol 13: R93
Jin S. G., Kadam S. and Pfeifer G. P. (2010) Examination of the specificity of DNA methylation profiling techniques towards 5-methylcytosine and
5-hydroxymethylcytosine. Nucleic Acids Res 38: e125
Dolinoy D. C., Weidman J. R., Waterland R. A. and Jirtle R. L. (2006) Maternal genistein alters coat color and protects Avy mouse offspring from
obesity by modifying the fetal epigenome. Environ Health Perspect 114: 567-572
Base Sequence BS Sequence oxBS Sequence TAB Sequence RRBS Sequence
C C T T T T
5mC C C C T C
5hmC C C T C C

65
DNAShear DNAMethylated DNA Bisulfite conversion
C GTCT
C GTUT
Bisulfite
C GTTT
PCR
BISULFITE SEQUENCING (BS-SEQ)
Bisulfite sequencing (BS-Seq) or whole-genome bisulfite sequencing (WGBS) is a well-established protocol to detect methylated cytosines in
genomic DNA73
. In this method, genomic DNA is treated with sodium bisulfite and then sequenced, providing single-base resolution of methylated
cytosines in the genome. Upon bisulfite treatment, unmethylated cytosines are deaminated to uracils which, upon sequencing, are converted to
thymidines. Simultaneously, methylated cytosines resist deamination and are read as cytosines. The location of the methylated cytosines can then
be determined by comparing treated and untreated sequences. Bisulfite treatment of DNA converts unmethylated cytosines to thymidines, leading
to reduced sequence complexity. Very accurate deep sequencing serves to mitigate this loss of complexity
The EpiGnome™
Kit uses a unique library construction method that incorporates bisulfite conversion as the first step. The EpiGnome method
retains sample diversity while providing uniform coverage.
73
Feil R., Charlton J., Bird A. P., Walter J. and Reik W. (1994) Methylation analysis on individual chromosomes: improved protocol for bisulphite genomic sequencing. Nucleic Acids Res 22: 695-696
Pros Cons
BS-Seq or WGBS
• CpG and non-CpG methylation throughout the genome is
covered at single-base resolution
• 5mC in dense, less dense, and repeat regions are covered
• Bisulfite converts unmethylated cytosines to thymidines, reducing
sequence complexity, which can make it difficult to create
alignments
• NPs where a cytosine is converted to thymidine will be missed upon
bisulfite conversion
• Bisulfite conversion does not distinguish between 5mC and 5hmC
BS-Seq or WGBS
EpiGnome Methyl-Seq
EpiGnome
• Pre-library bisulfite conversion
• Low input gDNA (50 ng)
• Uniform CpG, CHG, and CHH coverage
• No fragmentation and no methylated adapters
• Retention of sample diversity
• Bisulfite converts unmethylated cytosines to thymidines,reducing
sequence complexity, which can make it difficult to create
alignments
• SNPs where a cytosine is converted to thymidine will be missed
upon bisulfite conversion
• Higher duplicate percentage
DNAMethylated DNA Bisulfite conversion
C GTCT
C GTUT
Bisulfite
C GTTT
PCR
Converted single-stranded
fragments
Random priming
DNA synthesis
3’tagging PCR

66
References
Gustems M., Woellmer A., Rothbauer U., Eck S. H., Wieland T., et al. (2013) c-Jun/c-Fos heterodimers regulate cellular genes via a newly
identified class of methylated DNA sequence motifs. Nucleic Acids Res
Transcription factors bind with specificity to their preferred DNA sequence motif. However, a virus-encoded transcription factor Zta was the
first example of a sequence-specific transcription factor binding selectively and preferentially to methylated CpG residues. In this study the
authors present their finding of a novel AP-1 binding site, termed meAP-1, which contains a CpG nucleotide. Using ChIP-Seq with Illumina
sequencing, they show how the methylation state of this nucleotide affects binding by c-Jun/c-Fos in vitro and in vivo.
Habibi E., Brinkman A. B., Arand J., Kroeze L. I., Kerstens H. H., et al. (2013) Whole-genome bisulfite sequencing of two distinct
interconvertible DNA methylomes of mouse embryonic stem cells. Cell Stem Cell 13: 360-369
Mouse embryonic stem cells (ESCs) provide an excellent model system for studying mammalian cell differentiation on the molecular level.
This study uses two kinase inhibitors (2i) to derive mouse ESCs in the pluripotent ground state to study the deposition and loss of DNA
methylation during differentiation. The epigenetic state and expression of the cells were monitored using ChIP-Seq and RNA-Seq on the
Illumina HiSeq platform.
Illumina Technology: HiSeq 2000, MiSeq
Hussain S., Sajini A. A., Blanco S., Dietmann S., Lombard P., et al. (2013) NSun2-mediated cytosine-5 methylation of vault noncoding RNA
determines its processing into regulatory small RNAs. Cell Rep 4: 255-261
This paper presents miCLIP: a new technique for identifying RNA methylation sites in transcriptomes. The authors use the miCLIP method
with Illumina sequencing to determine site-specific methylation in tRNAs and additional messenger and noncoding RNAs. As a case study,
the authors studied the methyltransferase NSun2 and showed that loss of cytosine-5 methylation in vault RNAscauses aberrant processing
that may interrupt processing of small RNA fragments, such as microRNAs.
Illumina Technology: TruSeq RNA Kit, Genome AnalyzerIIx
Kozlenkov A., Roussos P., Timashpolsky A., Barbu M., Rudchenko S., et al. (2014) Differences in DNA methylation between human
neuronal and glial cells are concentrated in enhancers and non-CpG sites. Nucleic Acids Res 42: 109-127
Epigenetic regulation by DNA methylation varies among different cell types. In this study, the authors compared the methylation status of
neuronal and non-neuronal nuclei using Illumina Human Methylation450k arrays. They classified the differentially methylated (DM) sites into
those undermethylated in the neuronal cell type, and those that were undermethylated in non-neuronal cells. Using this approach, they
identified sets of cell type–specific patterns and characterized these by their genomic locations.
Illumina Technology: HumanMethylation450 BeadChip, HumanOmni1-Quad (Infinium GT), HiSeq 2000

67
Lun F. M., Chiu R. W., Sun K., Leung T. Y., Jiang P., et al. (2013) Noninvasive prenatal methylomic analysis by genomewide bisulfite
sequencing of maternal plasma DNA. Clin Chem 59: 1583-1594
The presence of fetal DNA in maternal plasma opens up possibilities for non-invasive prenatal DNA testing of the fetus through blood
samples from the mother. Using SNP differences between mother and fetus to identify fetal molecules, this study inspected the
genome-wide methylome of the unborn child by bisulfite sequencing. The authors determined the methylation density over each 1 Mbp
region of the genome for samples taken in each trimester and after delivery to show how the fetal methylome is established gradually
throughout pregnancy.
Illumina Technology: HiSeq 2000, HumanMethylation450 BeadChip
Regulski M., Lu Z., Kendall J., Donoghue M. T., Reinders J., et al. (2013) The maize methylome influences mRNA splice sites and reveals
widespread paramutation-like switches guided by small RNA. Genome Res 23: 1651-1662
The maize genome encompasses a widely unexplored landscape for epigenetic mechanisms of paramutation and imprinting. In this study
whole-exome bisulfite sequencing was applied to map the cytosine methylation profile of two maize inbred lines. The analysis revealed that
frequent methylation switches, guided by siRNA, may persist for up to eight generations, suggesting that epigenetic inheritance resembling
paramutation is much more common than previously supposed.
Illumina Technology: HiSeq 2000, Genome AnalyzerIIx
Schmitz R. J., He Y., Valdes-Lopez O., Khan S. M., Joshi T., et al. (2013) Epigenome-wide inheritance of cytosine methylation variants in a
recombinant inbred population. Genome Res 23: 1663-1674
In an effort to elucidate the mammalian DNA methylome, this study applied whole-genome bisulfite sequencing using the Illumina HiSeq
platform and gene expression analysis to define functional classes of hypomethylated regions (HMRs). Comparing HMR profiles in embryonic
stem and primary blood cells, the authors showed that the HMRs in intergenic space (iHMRs) mark an exclusive subset of active DNase
hypersensitive sites. The authors went on to compare primate-specific and human population variation at iHMRs, and they derived models of
the cellular timelines for DHS and iHMR establishment.
Schlesinger F., Smith A. D., Gingeras T. R., Hannon G. J. and Hodges E. (2013) De novo DNA demethylation and noncoding transcription
define active intergenic regulatory elements. Genome Res 23: 1601-1614
In an effort to elucidate the mammalian DNA methylome, this study applied whole-genome bisulfite sequencing using the Illumina HiSeq
platform and gene expression analysis to define functional classes of hypomethylated regions (HMRs). Comparing HMR profiles in embryonic
stem and primary blood cells, the authors showed that the HMRs in intergenic space (iHMRs) mark an exclusive subset of active DNase
hypersensitive sites. The authors went on to compare primate-specific and human population variation at iHMRs, and they derived models of
the cellular timelines for DHS and iHMR establishment.

68
Blaschke K., Ebata K. T., Karimi M. M., Zepeda-Martinez J. A., Goyal P., et al. (2013) Vitamin C induces Tet-dependent DNA demethylation and
a blastocyst-like state in ES cells. Nature 500: 222-226
Potok M. E., Nix D. A., Parnell T. J. and Cairns B. R. (2013) Reprogramming the maternal zebrafish genome after fertilization to match the
paternal methylation pattern. Cell 153: 759-772
Rodrigues J. A., Ruan R., Nishimura T., Sharma M. K., Sharma R., et al. (2013) Imprinted expression of genes and small RNA is associated with
localized hypomethylation of the maternal genome in rice endosperm. Proc Natl Acad Sci U S A 110: 7934-7939
Shirane K., Toh H., Kobayashi H., Miura F., Chiba H., et al. (2013) Mouse oocyte methylomes at base resolution reveal genome-wide
accumulation of non-CpG methylation and role of DNA methyltransferases. PLoS Genet 9: e1003439
Warden C. D., Lee H., Tompkins J. D., Li X., Wang C., et al. (2013) COHCAP: an integrative genomic pipeline for single-nucleotide resolution
DNA methylation analysis. Nucleic Acids Res 41: e117
Adey A. and Shendure J. (2012) Ultra-low-input, tagmentation-based whole-genome bisulfite sequencing. Genome Res 22: 1139-1143
Diep D., Plongthongkum N., Gore A., Fung H. L., Shoemaker R., et al. (2012) Library-free methylation sequencing with bisulfite padlock probes.
Nat Methods 9: 270-272
Seisenberger S., Andrews S., Krueger F., Arand J., Walter J., et al. (2012) The dynamics of genome-wide DNA methylation reprogramming in
mouse primordial germ cells. Mol Cell 48: 849-862
Feng S., Cokus S. J., Zhang X., Chen P. Y., Bostick M., et al. (2010) Conservation and divergence of methylation patterning in plants and
animals. Proc Natl Acad Sci U S A 107: 8689-8694
Li N., Ye M., Li Y., Yan Z., Butcher L. M., et al. (2010) Whole genome DNA methylation analysis based on high throughput sequencing
technology. Methods 52: 203-212
Lyko F., Foret S., Kucharski R., Wolf S., Falckenhayn C., et al. (2010) The honey bee epigenomes: differential methylation of brain DNA in
queens and workers. PLoS Biol 8: e1000506
Xie W., Schultz M. D., Lister R., Hou Z., Rajagopal N., et al. (2013) Epigenomic analysis of multilineage differentiation of human embryonic
stem cells. Cell 153: 1134-1148
The authors studied the differentiation of hESCs into four cell types: trophoblast-like cells, mesendoderm, neural progenitor cells, and
mesenchymal stem cells. DNA methylation (WGBS) and histone modifications were examined for each cell type. The study provides insight
into the dynamic changes that accompany lineage-specific cell differentiation in hESCs.
Yamaguchi S., Shen L., Liu Y., Sendler D. and Zhang Y. (2013) Role of Tet1 in erasure of genomic imprinting. Nature 504: 460-464
Genomic imprinting is the cellular mechanism for switching off one of two alleles by DNA methylation. This allele-specific gene expression
system is very important for mammalian development and function. In this study, the Tet1 protein was studied for its function in primordial
germ cells, the phase of development where the imprinting methylation mark of the parent is erased. Using ChIP-Seq and bisulfite
sequencing on the Illumina HiSeq platform, the authors showed that Tet1 knockout males exhibited aberrant hypermethylation in the paternal
allele of differential methylated regions.
Illumina Technology: HiSeq 2500®

69
Ball M. P., Li J. B., Gao Y., Lee J. H., LeProust E. M., et al. (2009) Targeted and genome-scale strategies reveal gene-body methylation
signatures in human cells. Nat Biotechnol 27: 361-368
Gehring M., Bubb K. L. and Henikoff S. (2009) Extensive demethylation of repetitive elements during seed development underlies gene
imprinting. Science 324: 1447-1451
Hodges E., Smith A. D., Kendall J., Xuan Z., Ravi K., et al. (2009) High definition profiling of mammalian DNA methylation by array capture and
single molecule bisulfite sequencing. Genome Res 19: 1593-1605
Hsieh T. F., Ibarra C. A., Silva P., Zemach A., Eshed-Williams L., et al. (2009) Genome-wide demethylation of Arabidopsis endosperm. Science
324: 1451-1454
Jacob Y., Feng S., Leblanc C. A., Bernatavichute Y. V., Stroud H., et al. (2009) ATXR5 and ATXR6 are H3K27 monomethyltransferases required
for chromatin structure and gene silencing. Nat Struct Mol Biol 16: 763-768
Cokus S. J., Feng S., Zhang X., Chen Z., Merriman B., et al. (2008) Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA
methylation patterning. Nature 452: 215-219
He Y., Vogelstein B., Velculescu V. E., Papadopoulos N. and Kinzler K. W. (2008) The antisense transcriptomes of human cells. Science 322:
1855-1857
Meissner A., Mikkelsen T. S., Gu H., Wernig M., Hanna J., et al. (2008) Genome-scale DNA methylation maps of pluripotent and differentiated
cells. Nature 454: 766-770
Associated Kits
EpiGnome™
Methyl-Seq®
Kit
Infinium HumanMethylation450®
Arrays

70
Methylated DNA Capture first strand on
Streptavidin coated
magnetic beads
Second random
priming
Streptavidin
Biotin
Adaptor Random primer 1
Bisulfite
conversion
First random
priming
Adaptor
Random primer 2
Generate second strand DNA with adaptorsElution
POST-BISULFITE ADAPTER TAGGING (PBAT)
To avoid the bisulfite-induced loss of intact sequencing templates, in post-bisulfite adapter tagging (PBAT)74
bisulfite treatment is followed by
adapter tagging and two rounds of random primer extension. This procedure generates a substantial number of unamplified reads from as little
as subnanogram quantities of DNA.
Pros Cons
• Requires only 100 ng of DNA for amplification-free WGBS of
mammalian genomes
• Bisulfite converts unmethylated cytosines to thymidines,
reducing sequence complexity, which can make it difficult to
create alignments
• SNPs where a cytosine is converted to thymidine will be missed
upon bisulfite conversion
References
Kobayashi H., Sakurai T., Miura F., Imai M., Mochiduki K., et al. (2013) High-resolution DNA methylome analysis of primordial germ cells iden-
tifies gender-specific reprogramming in mice. Genome Res 23: 616-627
Dynamic epigenetic reprogramming occurs during mammalian germ cell development. One of these processes is DNA methylation
and demethylation, which is commonly studied using bisulfite sequencing. This study used an Illumina HiSeq 2000 system for WGBS
to characterize the DNA methylation profiles of male and female mouse primordial germ cells (PGCs) at different stages of embryonic
development. The authors found sex- and chromosome-specific differences in genome-wide CpG and CGI methylation during early-
to late-stage PGC development. They also obtained high-resolution details of DNA methylation changes, for instance, that LINE/LTR
retrotransposons were resistant to DNA methylation at high CpG densities.
74 Miura F., Enomoto Y., Dairiki R. and Ito T. (2012) Amplification-free whole-genome bisulfite sequencing by post-bisulfite adaptor tagging. Nucleic Acids Res 40: e136

71
Shirane K., Toh H., Kobayashi H., Miura F., Chiba H., et al. (2013) Mouse oocyte methylomes at base resolution reveal genome-wide
accumulation of non-CpG methylation and role of DNA methyltransferases. PLoS Genet 9: e1003439
DNA methylation is an epigenetic modification that plays a crucial role in normal mammalian development, retrotransposon silencing, and
cellular reprogramming. Using amplification-free WGBS, the authors constructed the base-resolution methylome maps of germinal vesicle
oocytes (GVOs), non-growing oocytes (NGOs), and mutant GVOs lacking the DNA methyltransferases Dnmt1, Dnmt3a, Dnmt3b, or Dnmt3L.
They found that nearly two-thirds of all methylcytosines occur in a non-CG context in GVOs. The distribution of non-CG methylation closely
resembled that of CG methylation throughout the genome and showed clear enrichment in gene bodies.
Kobayashi H., Sakurai T., Imai M., Takahashi N., Fukuda A., et al. (2012) Contribution of Intragenic DNA Methylation in Mouse Gametic DNA
Methylomes to Establish Oocyte-Specific Heritable Marks. PLoS Genet 8: e1002440
Miura F., Enomoto Y., Dairiki R. and Ito T. (2012) Amplification-free whole-genome bisulfite sequencing by post-bisulfite adaptor tagging.
Associated Kits
EpiGnome™
Methyl-Seq Kit
Infinium HumanMethylation450 Arrays

72
DNAMethylated DNA TagmentationTransposome with
methylated adaptor
Displaced oligo
Oligo with
methylated
adaptor
Displace oligo
Hybridize methylated
adaptor
Gap repair
PCRBisulfite
conversion
TAGMENTATION-BASED WHOLE GENOME BISULFITE SEQUENCING (T-WGBS)
Tagmentation-based whole-genome bisulfite sequencing (T-WGBS) is a protocol that utilizes the Epicentre®
Tn5 transposome and bisulfite
conversion to study 5mC75
. In this method, DNA is incubated with Tn5 transposome containing methylated primers, which fragments the DNA and
ligates adapters. Tagged DNA first undergoes oligo displacement, followed by methylated oligo replacement and gap repair, assuring methylated
adapter addition to tagmented DNA. DNA is then treated with sodium bisulfite, PCR-amplified, and sequenced. Deep sequencing provides single-
base resolution of 5mC in the genome.
Pros Cons
• Can sequence samples with very limited starting material
(~20 ng)
• Fast protocol with few steps
• Elimination of multiple steps prevents loss of DNA
• Bisulfite converts unmethylated cytosines to thymidines, reducing
sequence complexity, which can make it difficult to create alignments
• SNPs where a cytosine is converted to thymidine will be missed upon
References
Wang Q., Gu L., Adey A., Radlwimmer B., Wang W., et al. (2013) Tagmentation-based whole-genome bisulfite sequencing.
Nat Protoc 8: 2022-2032
Scaling up bisulfite sequencing to genome-wide analysis has been hindered by the requirements for large amounts of DNA and high
sequencing costs. This paper presents a protocol for T-WGBS with sequencing on the Illumina HiSeq 2000 system. The authors demonstrate
the robustness of the protocol in comparison with conventional WGBS. T-WGBS requires not more than 20 ng of input DNA; hence, the
protocol allows the comprehensive methylome analysis of limited amounts of DNA isolated from precious biological specimens.
Illumina Technology: Nextera DNA Sample Prep Kit, HiSeq 2000; 101 bp paired-end reads
Associated Kits
EpiGnome™
Methyl-Seq Kit
75 Wang Q., Gu L., Adey A., Radlwimmer B., Wang W., et al. (2013) Tagmentation-based whole-genome bisulfite sequencing. Nat Protoc 8: 2022-2032

73
DNAKRuO
4
C GC CT
5hmC residue Control
C GCT C
GCT T C
Bisulfite treatment
PCR amplification
Bisulfite treatment
PCR amplification
C GC CT
C GC CT
5fC residue
OXIDATIVE BISULFITE SEQUENCING (OXBS-SEQ)
Oxidative bisulfite sequencing (oxBS-Seq) differentiates between 5mC and 5hmC76
. With oxBS, 5hmC is oxidized to 5formylcytosine (5fC) with
an oxidizing agent, while 5mC remains unchanged. Sodium bisulfite treatment of oxidized 5hmC results in its deamination to uracil which, upon
sequencing, is read as a thymidine. Deep sequencing of oxBS-treated DNA and sequence comparison of treated vs. untreated can identify 5mC
locations at base resolution.
Pros Cons
• CpG and non-CpG methylation throughout the genome is
covered at single-base resolution
• 5mC dense and less dense in repeat regions are covered
• Method clearly differentiates between 5mC and 5hmC,
precisely identifying 5mC
• SNPs where a cytosine is converted to thymidine will be
missed upon bisulfite conversion
References
Booth M. J., Ost T. W., Beraldi D., Bell N. M., Branco M. R., et al. (2013) Oxidative bisulfite sequencing of 5-methylcytosine and
5-hydroxymethylcytosine. Nat Protoc 8: 1841-1851
This is the first paper to report a method combining chemical treatment of DNA with the well-established bisulfite protocol, highlighting
Illumina’s TruSeq kit and calling for the use of MiSeq or HiSeq platforms. The OxBS-Seq protocol helps distinguish between 5mC and 5hmC,
while standard bisulfite sequencing is incapable of distinguishing between 5mC and 5hmC. Genomic DNA is first treated with an oxidizing
agent that reacts with 5hmC, promoting its deamination to uracil, while the 5mC modification remains unchanged and is read as cytosine.
Using Illumina technology, this method allows base resolution of the exact location of 5hmC and 5mC modifications.
Illumina Technology: TruSeq DNA Sample Prep Kit, MiSeq, HiSeq 2000
Associated Kits
EpiGnome™
Methyl-Seq Kit
TruSeq DNA Sample Prep Kit
TruSeq Nano DNA Sample Prep Ki
76
Booth M. J., Branco M. R., Ficz G., Oxley D., Krueger F., et al. (2012) Quantitative sequencing of 5-methylcytosine and 5-hydroxymethylcytosine at single-base resolution. Science 336: 934-937

74
5cmC
DNA
GCC CT
5hmC residue ßGT
Glucosylation Oxidation
5mC
GCC CT
g5hmC residue5mC TET
GCC CT GCT TT
g5hmC residue
Bisulfite treatment
PCR amplification
TET-ASSISTED BISULFITE SEQUENCING (TAB-SEQ)
TAB-Seq is a novel method that uses bisulfite conversion and Tet proteins to study 5hmC77
. In this protocol, 5hmC is first protected with a glucose
moiety that allows selective interaction and subsequent oxidation of 5mC with the Tet proteins. The oxidized genomic DNA is then treated with
bisulfite, where 5hmC remains unchanged and is read as a cytosine, while 5mC and unmethylated cytosines are deaminated to uracil and read
as thymidines upon sequencing. Deep sequencing of TAB-treated DNA compared with untreated DNA provides accurate representation of 5hmC
localization in the genome.
Pros Cons
• CpG and non-CpG hydroxymethylation throughout the genome
is covered at single-base resolution
• Dense, less dense, and 5hmC in repeat regions are covered
• Method clearly differentiates between 5hmC and 5mC,
specifically identifying 5hmC
• SNPs where a cytosine is converted to thymidine will be missed upon
• Requires deep sequencing to provide sufﬁcient depth to cover
the entire genome and accurately map the low amounts 5hmC78
References
Kim M., Park Y. K., Kang T. W., Lee S. H., Rhee Y. H., et al. (2013) Dynamic changes in DNA methylation and hydroxymethylation when hES
cells undergo differentiation toward a neuronal lineage. Hum Mol Genet 23: 657-667
Epigenetic markers on chromatin include the methylation of DNA. Several forms of DNA methylation exist and their function and interaction is
a field of intensive study. This paper describes how an in vitro model system of gradual differentiation of hESCs underwent dramatic genome-
wide changes in 5mC and 5hmC methylationpatterns during lineage commitment. The authors used Illumina BeadArray for expression
profiling and Genome Analyzer hMeDIP-sequencing to study the correlation between gene expression and DNA methylation.
Illumina Technology: Human-6 Whole-Genome Expression BeadChip, Genome AnalyzerIIx, HiScanSQ®
Scanner, Infinium
HumanMethylation 450 BeadChip
77
Yu M., Hon G. C., Szulwach K. E., Song C. X., Zhang L., et al. (2012) Base-resolution analysis of 5-hydroxymethylcytosine in the Mammalian genome. Cell 149: 1368-1380
78
Thomson J. P., Hunter J. M., Nestor C. E., Dunican D. S., Terranova R., et al. (2013) Comparative analysis of affinity-based 5-hydroxymethylation enrichment techniques.

75
Lister R., Mukamel E. A., Nery J. R., Urich M., Puddifoot C. A., et al. (2013) Global epigenomic reconfiguration during mammalian brain
development. Science 341: 1237905
DNA methylation is implicated in mammalian brain development and plasticity underlying learning and memory. This paper reports the
genome-wide composition, patterning, cell specificity, and dynamics of DNA methylation at single-base resolution in human and mouse
frontal cortex throughout their lifespan. The extensive methylome profiling was performed with ChIP-Seq on an Illumina HiSeq sequencer at
single-base resolution.
Illumina Technology: TruSeq RNA Sample Prep Kit, TruSeq DNA Sample Prep Kit, HiSeq 2000
Wang T., Wu H., Li Y., Szulwach K. E., Lin L., et al. (2013) Subtelomeric hotspots of aberrant 5-hydroxymethylcytosine-mediated epigenetic
modifications during reprogramming to pluripotency. Nat Cell Biol 15: 700-711
The transcriptional reprogramming that allows mammalian somatic cells to be reprogrammed into pluripotent stem cells (iPSCs) includes a
complete reconfiguration of the epigenetic marks in the genome. This study examined the levels of 5hmC in hESCs during reprogramming
to iPSCs. The authors found reprogramming hotspots in subtelomeric regions, most of which featured incomplete hydroxymethylation at CG
sites.
Illumina Technology: HiSeq 2000, HiScanSQ, MiSeq
Jiang L., Zhang J., Wang J. J., Wang L., Zhang L., et al. (2013) Sperm, but not oocyte, DNA methylome is inherited by zebrafish early embryos.
Cell 153: 773-784
Song C. X., Szulwach K. E., Dai Q., Fu Y., Mao S. Q., et al. (2013) Genome-wide profiling of 5-formylcytosine reveals its roles in epigenetic
priming. Cell 153: 678-691
Yu M., Hon G. C., Szulwach K. E., Song C. X., Jin P., et al. (2012) Tet-assisted bisulfite sequencing of 5-hydroxymethylcytosine.
Nat Protoc 7: 2159-2170
Yu M., Hon G. C., Szulwach K. E., Song C. X., Zhang L., et al. (2012) Base-resolution analysis of 5-hydroxymethylcytosine in the mammalian
genome. Cell 149: 1368-1380
Associated Kits
EpiGnome™
Methyl-Seq Kit

76
Extract DNA Fractionate
Denature
ImmunoprecipitateMethylated DNA DNADNA
purification
METHYLATED DNA IMMUNOPRECIPITATION SEQUENCING (MEDIP-SEQ)
Methylated DNA immunoprecipitation sequencing (MeDIP-Seq) is commonly used to study 5mC or 5hmC modification79
. Specific antibodies can
be used to study cytosine modifications. If using 5mC-specific antibodies, methylated DNA is isolated from genomic DNA via immunoprecipitation.
Anti-5mC antibodies are incubated with fragmented genomic DNA and precipitated, followed by DNA purification and sequencing. Deep
sequencing provides greater genome coverage, representing the majority of immunoprecipitated methylated DNA.
Pros Cons
• Covers CpG and non-CpG 5mC throughout the genome
• 5mC in dense, less dense, and repeat regions are covered
• Antibody-based selection is independent of sequence and does
not enrich for 5hmC due to antibody specificity
• Base-pair resolution is lower (~150 bp) as opposed to single
base resolution
• Antibody specificity and selectivity must be tested to avoid
nonspecific interaction
• Antibody-based selection is biased towards
hypermethylated regions
References
Puszyk W., Down T., Grimwade D., Chomienne C., Oakey R. J., et al. (2013) The epigenetic regulator PLZF represses L1 retrotransposition in
germ and progenitor cells. EMBO J 32: 1941-1952
Each transcription factor in the human cell may regulate a large number of target genes through specific chromatin interactions.
Promyelocytic leukemia zinc finger protein (PLZF) acts as an epigenetic regulator of stem cell maintenance in germ cells and hematopoietic
stem cells. In this study, L1 retrotransposons were identified as the primary targets of PLZF. Using ChIP-Seq and MeDIP-Seq onIllumina
Genome Analyzer, the authors identified how PLZF-mediated DNA methylation induces silencing of L1 and inhibits L1 retrotransposition.
Shen H., Qiu C., Li J., Tian Q. and Deng H. W. (2013) Characterization of the DNA methylome and its interindividual variation in human pe-
ripheral blood monocytes. Epigenomics 5: 255-269
Peripheral blood monocytes (PBMs) play multiple and critical roles in the immune response, and abnormalities in PBMs have been linked to a
variety of human disorders. In this study, the epigenome-wide DNA methylation profiles of purified PBMs were identified using MeDIP-Seq on
an Illumina Genome Analyzer. Interestingly, the authors observed substantial interindividual variation in DNA methylation across the individual
PBM methylomes.
79 Weber M., Davies J. J., Wittig D., Oakeley E. J., Haase M., et al. (2005) Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and trans-
formed human cells. Nat Genet 37: 853-862

77
Tan L., Xiong L., Xu W., Wu F., Huang N., et al. (2013) Genome-wide comparison of DNA hydroxymethylation in mouse embryonic stem cells
and neural progenitor cells by a new comparative hMeDIP-seq method. Nucleic Acids Res 41: e84
The genome-wide distribution patterns of the “sixth base” 5hmC in many tissues and cells have recently been revealed by hydroxymethylated
DNA immunoprecipitation (hMeDIP) followed by high throughput sequencing or tiling arrays. This paper presents a new comparative hMeDIP-
seq method which allows for direct genome-wide comparison of DNA hydroxymethylation across multiplesamples. The authors demonstrate
the new method by profiling DNA hydroxymethylation and gene expression during neural differentiation.
Saied M. H., Marzec J., Khalid S., Smith P., Down T. A., et al. (2012) Genome wide analysis of acute myeloid leukemia reveal leukemia spe-
cific methylome and subtype specific hypomethylation of repeats. PLoS One 7: e33213
Epigenetic modifications in the form of DNA methylation are part of the regulatory machinery of the cell. By studying the patterns of DNA
methylation in disease tissue, we may characterize disease mechanisms. In this study, bone marrow samples from 12 patients with acute
myeloid leukemia (AML) were analyzed with MeDIP-Seq and compared to normal bone marrow. The investigators found considerable
cytogenetic subtype specificity in the methylomes affecting different genomic features.
Illumina Technology: HumanMethylation27 arrays, Genome AnalyzerIIx
Taiwo O., Wilson G. A., Morris T., Seisenberger S., Reik W., et al. (2012) Methylome analysis using MeDIP-seq with low DNA concentrations.
Nat Protoc 7: 617-636
DNA methylation can be assayed at high throughput using MeDIP-Seq, but the application has been limited to samples where the amount
of DNA was sufficient for the assay (5–20 µg). This study presents a new optimized protocol for MeDIP-Seq, requiring as little as 50 ng of
starting DNA.
Bian C. and Yu X. (2013) PGC7 suppresses TET3 for protecting DNA methylation. Nucleic Acids Res
Colquitt B. M., Allen W. E., Barnea G. and Lomvardas S. (2013) Alteration of genic 5-hydroxymethylcytosine patterning in olfactory neurons
correlates with changes in gene expression and cell identity. Proc Natl Acad Sci U S A 110: 14682-14687
Neri F., Krepelova A., Incarnato D., Maldotti M., Parlato C., et al. (2013) Dnmt3L Antagonizes DNA Methylation at Bivalent Promoters and Favors
DNA Methylation at Gene Bodies in ESCs. Cell 155: 121-134
Stevens M., Cheng J. B., Li D., Xie M., Hong C., et al. (2013) Estimating absolute methylation levels at single-CpG resolution from methylation
enrichment and restriction enzyme sequencing methods. Genome Res 23: 1541-1553
Zhang B., Zhou Y., Lin N., Lowdon R. F., Hong C., et al. (2013) Functional DNA methylation differences between tissues, cell types, and across
individuals discovered using the MM algorithm. Genome Res 23: 1522-1540
Zilbauer M., Rayner T. F., Clark C., Coffey A. J., Joyce C. J., et al. (2013) Genome-wide methylation analyses of primary human leukocyte
subsets identifies functionally important cell-type-specific hypomethylated regions. Blood 122: e52-60

78
Sati S., Tanwar V. S., Kumar K. A., Patowary A., Jain V., et al. (2012) High resolution methylome map of rat indicates role of intragenic DNA
methylation in identification of coding region. PLoS One 7: e31621
Gao Q., Steine E. J., Barrasa M. I., Hockemeyer D., Pawlak M., et al. (2011) Deletion of the de novo DNA methyltransferase Dnmt3a promotes
lung tumor progression. Proc Natl Acad Sci U S A 108: 18061-18066
Bock C., Tomazou E. M., Brinkman A. B., Muller F., Simmer F., et al. (2010) Quantitative comparison of genome-wide DNA methylation mapping
technologies. Nat Biotechnol 28: 1106-1114
Chavez L., Jozefczuk J., Grimm C., Dietrich J., Timmermann B., et al. (2010) Computational analysis of genome-wide DNA methylation during
the differentiation of human embryonic stem cells along the endodermal lineage. Genome Res 20: 1441-1450
Harris R. A., Wang T., Coarfa C., Nagarajan R. P., Hong C., et al. (2010) Comparison of sequencing-based methods to profile DNA methylation
and identification of monoallelic epigenetic modifications. Nat Biotechnol 28: 1097-1105
Li N., Ye M., Li Y., Yan Z., Butcher L. M., et al. (2010) Whole genome DNA methylation analysis based on high throughput sequencing
Maunakea A. K., Nagarajan R. P., Bilenky M., Ballinger T. J., D’Souza C., et al. (2010) Conserved role of intragenic DNA methylation in regulating
alternative promoters. Nature 466: 253-257
Ruike Y., Imanaka Y., Sato F., Shimizu K. and Tsujimoto G. (2010) Genome-wide analysis of aberrant methylation in human breast cancer cells
using methyl-DNA immunoprecipitation combined with high-throughput sequencing. BMC Genomics 11: 137
Hammoud S. S., Nix D. A., Zhang H., Purwar J., Carrell D. T., et al. (2009) Distinctive chromatin in human sperm packages genes for embryo
development. Nature 460: 473-478
Pomraning K. R., Smith K. M. and Freitag M. (2009) Genome-wide high throughput analysis of DNA methylation in eukaryotes.
Methods 47: 142-150
Down T. A., Rakyan V. K., Turner D. J., Flicek P., Li H., et al. (2008) A Bayesian deconvolution strategy for immunoprecipitation-based DNA
methylome analysis. Nat Biotechnol 26: 779-785
Associated Kits

79
Extract DNA Fractionate Elute with increasing
salt concentration
Methylated DNA DNADNA
purification
Capture biotinylated MBD on
Streptavidin coated magnetic beads
MBD
Streptavidin
Biotin
METHYLATION-CAPTURE (METHYLCAP) SEQUENCING OR
METHYL-BINDING-DOMAIN–CAPTURE (MBDCAP) SEQUENCING
MethylCap80, 81
or MBDCap82, 83
uses proteins to capture methylated DNA in the genome. Genomic DNA is first sonicated and incubated with
tagged MBD proteins that can bind methylated cytosines. The protein-DNA complex is then precipitated with antibody-conjugated beads that are
specific to the protein tag. Deep sequencing provides greater genome coverage, representing the majority of MBD-bound methylated DNA.
Pros Cons
• Genome-wide coverage of 5mC in dense CpG areas and
repeat regions
• MBD proteins do not interact with 5hmC
• Genome-wide CpGs and non-CpG methylation is not covered
Areas with less dense 5mC are also missed
• Base-pair resolution is lower (~150 bp) as opposed to single
base resolution
• Protein-based selection is biased towards
hypermethylated regions
References
Kim M., Park Y. K., Kang T. W., Lee S. H., Rhee Y. H., et al. (2013) Dynamic changes in DNA methylation and hydroxymethylation when hES
cells undergo differentiation toward a neuronal lineage. Hum Mol Genet 23: 657-667
Epigenetic markers on chromatin include the methylation of DNA. Several forms of DNA methylation exist and their function and interaction is
a field of intensive study. This paper describes how an in vitro model system of gradual differentiation of hESCs underwent dramatic genome-
wide changes in 5mC and 5hmC methylationpatterns during lineage commitment. The authors used Illumina BeadArray for expression
profiling and Genome Analyzer hMeDIP-sequencing to study the correlation between gene expression and DNA methylation.
Illumina Technology: Human-6 Whole-Genome Expression BeadChip, Genome AnalyzerIIx
, HiScanSQ Scanner, Infinium HumanMethylation
450 BeadChip
80
Bock C., Tomazou E. M., Brinkman A. B., Muller F., Simmer F., et al. (2010) Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat Biotechnol 28: 1106-1114
81 Brinkman A. B., Simmer F., Ma K., Kaan A., Zhu J., et al. (2010) Whole-genome DNA methylation profiling using MethylCap-seq. Methods 52: 232-236
82
Rauch T. A., Zhong X., Wu X., Wang M., Kernstine K. H., et al. (2008) High-resolution mapping of DNA hypermethylation and hypomethylation in lung cancer. Proc Natl Acad Sci U S A 105:
252-257
83
Rauch T. A. and Pfeifer G. P. (2009) The MIRA method for DNA methylation analysis. Methods Mol Biol 507: 65-75

80
Huang T. T., Gonzales C. B., Gu F., Hsu Y. T., Jadhav R. R., et al. (2013) Epigenetic deregulation of the anaplastic lymphoma kinase gene
modulates mesenchymal characteristics of oral squamous cell carcinomas. Carcinogenesis 34: 1717-1727
Promoter methylation is associated with silencing tumor suppressor genes in oral squamous cell carcinomas (OSCCs). The authors used
MBDCap-Seq to study methylation in OSCC cell lines, sequencing on the Illumina HiSeq platform, and identifying differentially methylated
regions. The authors note the ALK gene was susceptible to epigenetic silencing during oral tumorigenesis.
Zhao Y., Guo S., Sun J., Huang Z., Zhu T., et al. (2012) Methylcap-seq reveals novel DNA methylation markers for the diagnosis and
recurrence prediction of bladder cancer in a Chinese population. PLoS ONE 7: e35175
Bladder cancer (BC) has a high mortality rate and is the sixth most common cancer in the world. For successfully treated BCs, the relapse
rate is 60-70% within the first 5 years, necessitating the development of efficient diagnostics and biomarkers for monitoring disease
progression. The presence of cells in the urine allow for noninvasive genetic screening directly from urine. In this study, the authors identify
and validate nine DNA methylation markers through genome-wide profiling of DNA methylation from clinical urine samples.
Brinkman A. B., Gu H., Bartels S. J., Zhang Y., Matarese F., et al. (2012) Sequential ChIP-bisulfite sequencing enables direct genome-scale
investigation of chromatin and DNA methylation cross-talk. Genome Res 22: 1128-1138
Rodriguez B. A., Frankhouser D., Murphy M., Trimarchi M., Tam H. H., et al. (2012) Methods for high-throughput MethylCap-Seq data analysis.
BMC Genomics 13 Suppl 6: S14
Yu W., Jin C., Lou X., Han X., Li L., et al. (2011) Global analysis of DNA methylation by Methyl-Capture sequencing reveals epigenetic control of
cisplatin resistance in ovarian cancer cell. PLoS One 6: e29450
Brinkman A. B., Simmer F., Ma K., Kaan A., Zhu J., et al. (2010) Whole-genome DNA methylation profiling using MethylCap-seq.
Methods 52: 232-236
Serre D., Lee B. H. and Ting A. H. (2010) MBD-isolated Genome Sequencing provides a high-throughput and comprehensive survey of DNA
methylation in the human genome. Nucleic Acids Res 38: 391-399
Associated Kits
Nextera Rapid Capture Exome/Custom Enrichment

81
Methylated DNA DNAMethylated
regions
Methylated
adapter
End repair
and ligation
Bisulfite
conversion
Converted
fragments
PCRPCRMspI
digestion
REDUCED-REPRESENTATION BISULFITE SEQUENCING (RRBS-SEQ)
Reduced-representation bisulfite sequencing (RRBS-Seq) is a protocol that uses one or multiple restriction enzymes on the genomic DNA to
produce sequence-specific fragmentation84
. The fragmented genomic DNA is then treated with bisulfite and sequenced. This is the method of
choice to study specific regions of interest. It is particularly effective where methylation is high, such as in promoters and repeat regions.
Pros Cons
• Genome-wide coverage of CpGs in islands at
single-base resolution
• Areas dense in CpG methylation are covered
• Restriction enzymes cut at specific sites, providing biased
sequence selection
• Method measures 10-15% of all CpGs in genome
• Cannot distinguish between 5mC and 5hmC
• Does not cover non-CpG areas, genome-wide CpGs, and CpGs in
areas without the enzyme restriction site
References
Kozlenkov A., Roussos P., Timashpolsky A., Barbu M., Rudchenko S., et al. (2014) Differences in DNA methylation between human neuronal
and glial cells are concentrated in enhancers and non-CpG sites. Nucleic Acids Res 42: 109-127
Epigenetic regulation by DNA methylation varies among different cell types. In this study, the authors compared the methylation status of
neuronal and non-neuronal nuclei using Illumina Human Methylation450k arrays. They classified the differentially methylated (DM) sites into
those undermethylated in the neuronal cell type, and those that were undermethylated in non-neuronal cells. Using this approach, they
identified sets of cell type–specific patterns and characterized these by their genomic locations.
Schillebeeckx M., Schrade A., Lobs A. K., Pihlajoki M., Wilson D. B., et al. (2013) Laser capture microdissection-reduced representation
bisulfite sequencing (LCM-RRBS) maps changes in DNA methylation associated with gonadectomy-induced adrenocortical neoplasia in the
mouse. Nucleic Acids Res 41: e116
DNA methylation profiling by sequencing is challenging due to inaccurate cell enrichment methods and low DNA yields. This proof-of-concept
study presents a new method for genome-wide DNA methylation profiling using down to 1 ng of input DNA. The method—laser-capture
microdissection reduced-representation bisulfite sequencing (LCM-RRBS)—combines Illumina HiSeq sequencing with customized
methylated adapter sequences and bisulfite-PCR. The protocol allows for base-pair resolution of methylated sites.
Illumina Technology: HiSeq 2000, MiSeq
84
Meissner A., Gnirke A., Bell G. W., Ramsahoye B., Lander E. S., et al. (2005) Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids
Res 33: 5868-5877

82
Stevens M., Cheng J. B., Li D., Xie M., Hong C., et al. (2013) Estimating absolute methylation levels at single-CpG resolution from methylation
enrichment and restriction enzyme sequencing methods. Genome Res 23: 1541-1553
Current methods for sequencing-based DNA methylation profiling are continuously improving, but each common method, on its own, is
insufficient in providing a genome-wide single-CpG resolution of DNA methylation at a low cost. In this paper the authors present a novel
algorithm, methylCRF, which enables integration of data from MeDIP-Seq and MRE-Seq to provide single-CpG classification of methylation
state. The method provides similar or higher accuracy than any array or sequencing method on its own. The authors demonstrate the
algorithm on whole-genome bisulfite sequencing on Illumina HiSeq 2000 systems and Methylation450 arrays.
Will B., Vogler T. O., Bartholdy B., Garrett-Bakelman F., Mayer J., et al. (2013) Satb1 regulates the self-renewal of hematopoietic stem cells by
promoting quiescence and repressing differentiation commitment. Nat Immunol 14: 437-445
This study evaluated genome-wide DNA cytosine methylation by enhanced reduced-representation bisulfite sequencing (ERRBS). DNA was
digested with MspI, then end-repaired and ligated to paired-end Illumina sequencing adapters. This was followed by size selection and bisul-
fite treatment, clean-up, and PCR prior to sequencing.
Xi Y., Bock C., Muller F., Sun D., Meissner A., et al. (2012) RRBSMAP: a fast, accurate and user-friendly alignment tool for reduced representation bisulfite
sequencing. Bioinformatics 28: 430-432
Bock C., Kiskinis E., Verstappen G., Gu H., Boulting G., et al. (2011) Reference Maps of human ES and iPS cell variation enable high-throughput
characterization of pluripotent cell lines. Cell 144: 439-452
Gu H., Smith Z. D., Bock C., Boyle P., Gnirke A., et al. (2011) Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA
methylation profiling. Nat Protoc 6: 468-481
Gertz J., Varley K. E., Reddy T. E., Bowling K. M., Pauli F., et al. (2011) Analysis of DNA methylation in a three-generation family reveals widespread
genetic influence on epigenetic regulation. PLoS Genet 7: e1002228
Smallwood S. A., Tomizawa S., Krueger F., Ruf N., Carli N., et al. (2011) Dynamic CpG island methylation landscape in oocytes and preimplantation
embryos. Nat Genet 43: 811-814
Ziller M. J., Muller F., Liao J., Zhang Y., Gu H., et al. (2011) Genomic distribution and inter-sample variation of non-CpG methylation across human cell
types. PLoS Genet 7: e1002389
Smith Z. D., Gu H., Bock C., Gnirke A. and Meissner A. (2009) High-throughput bisulfite sequencing in mammalian genomes. Methods 48: 226-232
Associated Kits
EpiGnome™
Methyl-Seq Kit

83
85 Rivera C. M. and Ren B. (2013) Mapping human epigenomes. Cell 155: 39-55
86
Pinello L., Xu J., Orkin S. H. and Yuan G. C. (2014) Analysis of chromatin-state plasticity identifies cell-type-specific regulators of H3K27me3 patterns. Proc Natl Acad Sci U S A 111: E344-353
87
Yeh H. H., Young D., Gelovani J. G., Robinson A., Davidson Y., et al. (2013) Histone deacetylase class II and acetylated core histone immunohistochemistry in human brains with Huntington’s
disease. Brain Res 1504: 16-24
88
Kahrstrom C. T. (2013) Epigenetics: Legionella makes its mark on histones. Nat Rev Genet 14: 370
89
Warnault V., Darcq E., Levine A., Barak S. and Ron D. (2013) Chromatin remodeling--a novel strategy to control excessive alcohol drinking. Transl Psychiatry 3: e231
90
Marwick J. A., Kirkham P. A., Stevenson C. S., Danahay H., Giddings J., et al. (2004) Cigarette smoke alters chromatin remodeling and induces proinflammatory genes in rat lungs. Am J Respir Cell
Mol Biol 31: 633-642
Cigarette smoking disrupts DNA-protein interactions leading
to the development of cancers or pulmonary diseases.
DNA-PROTEIN INTERACTIONS
Chromatin remodeling is a dynamic process driven by factors that change DNA-protein interactions. These epigenetic factors can involve protein
modifications, such as histone methylation, acetylation, phosphorylation, and ubiquitination85
. Histone modifications determine gene activation
by recruiting regulatory factors and maintaining an open or closed chromatin state. Epigenetic factors play roles in tissue development86
,
embryogenesis, cell fate, immune response, and diseases such as cancer87
. Bacterial pathogens can elicit transcriptional repression of immune
genes by chromatin remodeling88
. The study of protein-DNA interactions has also demonstrated that chromatin remodeling can respond to external
factors such as excessive alcohol-seeking behaviors89
, cigarette smoking90
, and clinical drugs.
Reviews
Capell B. C. and Berger S. L. (2013) Genome-wide epigenetics. J Invest Dermatol 133: e9
Jakopovic M., Thomas A., Balasubramaniam S., Schrump D., Giaccone G., et al. (2013) Targeting the Epigenome in Lung Cancer:
Expanding Approaches to Epigenetic Therapy. Front Oncol 3: 261
Kahrstrom C. T. (2013) Epigenetics: Legionella makes its mark on histones. Nat Rev Genet 14: 370 and local translation in neurons.
Neuron 80: 648-657
Lee T. I. and Young R. A. (2013) Transcriptional regulation and its misregulation in disease. Cell 152: 1237-1251
Rivera C. M. and Ren B. (2013) Mapping human epigenomes. Cell 155: 39-55

84
Rocha P. P., Chaumeil J. and Skok J. A. (2013) Molecular biology. Finding the right partner in a 3D genome. Science 342: 1333-1334
Ronan J. L., Wu W. and Crabtree G. R. (2013) From neural development to cognition: unexpected roles for chromatin.
Telese F., Gamliel A., Skowronska-Krawczyk D., Garcia-Bassets I. and Rosenfeld M. G. (2013) “Seq-ing” insights into the epigenetics of neuronal
gene regulation. Neuron 77: 606-623
Yadon A. N. and Tsukiyama T. (2013) DNA looping-dependent targeting of a chromatin remodeling factor. Cell Cycle 12: 1809-1810
de Wit E. and de Laat W. (2012) A decade of 3C technologies: insights into nuclear organization. Genes Dev 26: 11-24
Sajan S. A. and Hawkins R. D. (2012) Methods for identifying higher-order chromatin structure. Annu Rev Genomics Hum Genet 13: 59-82
Zentner G. E. and Henikoff S. (2012) Surveying the epigenomic landscape, one base at a time. Genome Biol 13: 250

85
Active chromatin Isolate trimmed complexesDNase I digestion DNA
extraction
DNA
DNASE L HYPERSENSITIVE SITES SEQUENCING (DNASE-SEQ)
DNase l hypersensitive sites sequencing (DNase-Seq) is based on a well-established DNase I footprinting protocol91
that was optimized for
sequencing92
. In this method, DNA-protein complexes are treated with DNase l, and the DNA is then extracted and sequenced. Sequences bound
by regulatory proteins are protected from DNase l digestion. Deep sequencing provides accurate representation of the location of regulatory
proteins in genome. In a variation on this approach, the DNA-protein complexes are stabilized by formaldehyde crosslinking before DNase I
digestion. The crosslinking is reversed before DNA purification. In an alternative modification, called GeF-Seq, both the crosslinking and the
DNase I digestion are carried out in vivo, within permeabilized cells93
.
Pros Cons
• Can detect “open” chromatin94
• No prior knowledge of the sequence or binding protein is required
• Compared to FAIRE-Seq, has greater sensitivity at promoters95
• DNase l is sequence-specific and hypersensitive sites might not
account for the entire genome
• Integration of DNase I with ChIP data is necessary to identify and
differentiate similar protein-binding sites
References
Chumsakul O., Nakamura K., Kurata T., Sakamoto T., Hobman J. L., et al. (2013) High-resolution mapping of in vivo genomic transcription
factor binding sites using in situ DNase I footprinting and ChIP-seq. DNA Res 20: 325-338
This study describes an improvement and combination of DNase-Seq with ChIP-Seq, called genome footprinting by high throughput
sequencing (GeF-Seq).The authors claim GeF-seq provides better alignment due to shorter reads, resulting in higher resolution of DNA-
binding factor recognition sites.
91 Galas D. J. and Schmitz A. (1978) DNAse footprinting: a simple method for the detection of protein-DNA binding specificity. Nucleic Acids Res 5: 3157-3170
92 Anderson S. (1981) Shotgun DNA sequencing using cloned DNase I-generated fragments. Nucleic Acids Res 9: 3015-3027
93 Chumsakul O., Nakamura K., Kurata T., Sakamoto T., Hobman J. L., et al. (2013) High-resolution mapping of in vivo genomic transcription factor binding sites using in situ DNase I footprinting and
ChIP-seq. DNA Res 20: 325-338
94 Zentner G. E. and Henikoff S. (2012) Surveying the epigenomic landscape, one base at a time. Genome Biol 13: 250
95 Kumar V., Muratani M., Rayan N. A., Kraus P., Lufkin T., et al. (2013) Uniform, optimal signal processing of mapped deep-sequencing data. Nat Biotechnol 31: 615-622

86
Deng T., Zhu Z. I., Zhang S., Leng F., Cherukuri S., et al. (2013) HMGN1 modulates nucleosome occupancy and DNase I hypersensitivity at
the CpG island promoters of embryonic stem cells. Mol Cell Biol 33: 3377-3389
The authors use mouse ESCs and NPCs to study the interplay between histone H1 variants and high-mobility group (HMG) proteins in
chromatin remodeling. They use ChIP-Seq and DNase-Seq to elucidate the role of HMGN1 (a HMG protein) in affecting chromatin structure
at transcription start sites of promoters.
Iwata M., Sandstrom R. S., Delrow J. J., Stamatoyannopoulos J. A. and Torok-Storb B. (2013) Functionally and Phenotypically Distinct
Subpopulations of Marrow Stromal Cells Are Fibroblast in Origin and Induce Different Fates in Peripheral Blood Monocytes. Stem Cells Dev
Individual cell growth and differentiation is under constant influence by the surrounding tissue and nearby cell types. This study examined
marrow stromal cells (MSCs) and their gene expression profiles in comparison to monocyte-derived macrophages that often exist in close
proximity to MSCs. Using Illumina sequencing for DNase 1 hypersensitivity mapping, the authors showed a lineage association between two
types of MSCs (CD146+,CD146–) and marrow fibroblasts. Subpopulations of CD146+ MSCs were found to increase the expression of genes
relevant to hematopoietic regulation upon contact with monocytes, indicating an interaction of fibroblast-macrophage expression.
Ballare C., Castellano G., Gaveglia L., Althammer S., Gonzalez-Vallinas J., et al. (2013) Nucleosome-driven transcription factor binding and gene
regulation. Mol Cell 49: 67-79
Bertucci P. Y., Nacht A. S., Allo M., Rocha-Viegas L., Ballare C., et al. (2013) Progesterone receptor induces bcl-x expression through intragenic
binding sites favoring RNA polymerase II elongation. Nucleic Acids Res 41: 6072-6086
Lazarovici A., Zhou T., Shafer A., Dantas Machado A. C., Riley T. R., et al. (2013) Probing DNA shape and methylation state on a genomic scale
with DNase I. Proc Natl Acad Sci U S A 110: 6376-6381
Lo K. A., Labadorf A., Kennedy N. J., Han M. S., Yap Y. S., et al. (2013) Analysis of in vitro insulin-resistance models and their physiological
relevance to in vivo diet-induced adipose insulin resistance. Cell Rep 5: 259-270
Degner J. F., Pai A. A., Pique-Regi R., Veyrieras J. B., Gaffney D. J., et al. (2012) DNase I sensitivity QTLs are a major determinant of human
expression variation. Nature 482: 390-394
Dunowska M., Biggs P. J., Zheng T. and Perrott M. R. (2012) Identification of a novel nidovirus associated with a neurological disease of the
Australian brushtail possum (Trichosurus vulpecula). Vet Microbiol 156: 418-424
He H. H., Meyer C. A., Chen M. W., Jordan V. C., Brown M., et al. (2012) Differential DNase I hypersensitivity reveals factor-dependent chroma-
tin dynamics. Genome Res 22: 1015-1025
Lassen K. S., Schultz H., Heegaard N. H. and He M. (2012) A novel DNAseq program for enhanced analysis of Illumina GAII data: a case study
on antibody complementarity-determining regions. N Biotechnol 29: 271-278
Liu M., Li C. L., Stamatoyannopoulos G., Dorschner M. O., Humbert R., et al. (2012) Gammaretroviral Vector Integration Occurs Overwhelmingly
Within and Near DNase Hypersensitive Sites. Hum Gene Ther 23: 231-237
Maurano M. T., Humbert R., Rynes E., Thurman R. E., Haugen E., et al. (2012) Systematic localization of common disease-associated variation
in regulatory DNA. Science 337: 1190-1195

87
Neph S., Vierstra J., Stergachis A. B., Reynolds A. P., Haugen E., et al. (2012) An expansive human regulatory lexicon encoded in transcription
factor footprints. Nature 489: 83-90
Rosseel T., Scheuch M., Hoper D., De Regge N., Caij A. B., et al. (2012) DNase SISPA-next generation sequencing confirms Schmallenberg
virus in Belgian field samples and identifies genetic variation in Europe. PLoS One 7: e41967
Wang Y. M., Zhou P., Wang L. Y., Li Z. H., Zhang Y. N., et al. (2012) Correlation between DNase I hypersensitive site distribution and gene
expression in HeLa S3 cells. PLoS One 7: e42414
Zhang W., Wu Y., Schnable J. C., Zeng Z., Freeling M., et al. (2012) High-resolution mapping of open chromatin in the rice genome.
Zhang W., Zhang T., Wu Y. and Jiang J. (2012) Genome-wide identification of regulatory DNA elements and protein-binding footprints using
signatures of open chromatin in Arabidopsis. Plant Cell 24: 2719-2731
Boyle A. P., Song L., Lee B. K., London D., Keefe D., et al. (2011) High-resolution genome-wide in vivo footprinting of diverse transcription
factors in human cells. Genome Res 21: 456-464
Stadler M. B., Murr R., Burger L., Ivanek R., Lienert F., et al. (2011) DNA-binding factors shape the mouse methylome at distal regulatory
regions. Nature 480: 490-495
McDaniell R., Lee B. K., Song L., Liu Z., Boyle A. P., et al. (2010) Heritable individual-specific and allele-specific chromatin signatures in humans.
Science 328: 235-239
Turnbaugh P. J., Quince C., Faith J. J., McHardy A. C., Yatsunenko T., et al. (2010) Organismal, genetic, and transcriptional variation in the
deeply sequenced gut microbiomes of identical twins. Proc Natl Acad Sci U S A 107: 7503-7508
Audit B., Zaghloul L., Vaillant C., Chevereau G., d’Aubenton-Carafa Y., et al. (2009) Open chromatin encoded in DNA sequence is the signature
of ‘master’ replication origins in human cells. Nucleic Acids Res 37: 6064-6075
Turnbaugh P. J., Ridaura V. K., Faith J. J., Rey F. E., Knight R., et al. (2009) The effect of diet on the human gut microbiome: a metagenomic
analysis in humanized gnotobiotic mice. Sci Transl Med 1: 6ra14
Associated Kits
TruSeq ChIP-Seq kit
TruSeq DNA Sample Preparation Kit

88
Open chromatin Isolate trimmed complexesMNase digestion DNA
extraction
DNA
MNASE-ASSISTED ISOLATION OF NUCLEOSOMES SEQUENCING (MAINE-SEQ)
Micrococcal nuclease (MNase)-assisted isolation of nucleosomes sequencing (MAINE-Seq)96, 97
, is a variation on the well-established use of
MNase digestion to map nucleosome positions (MNase-Seq)98
. It is estimated that almost half the genome contains regularly spaced arrays
of nucleosomes, which are enriched in active chromatin domains99
. In MAINE-Seq, genomic DNA is treated with MNase. The DNA from the
DNA-protein complexes is then extracted and sequenced. Sequences bound by regulatory proteins are protected from MNase digestion. Deep
sequencing provides accurate representation of the location of regulatory proteins in the genome100
. To identify the regulatory proteins, MNase-Seq
can be followed by ChIP (NChIP)101
.
Pros Cons
• Can map nucleosomes and other DNA-binding proteins102
• Identifies location of various regulatory proteins in the genome
• Covers broad range of regulatory sites
• MNase sites might not account for the entire genome
• Does not provide much information about the kind of
regulatory elements
• Integration of MNase with ChIP data is necessary to identify and
differentiate similar protein-binding sites
References
Ballare C., Castellano G., Gaveglia L., Althammer S., Gonzalez-Vallinas J., et al. (2013) Nucleosome-driven transcription factor binding and
gene regulation. Mol Cell 49: 67-79
This study combines DNase, ChIP, and MAINE sequencing to understand the effects of chromatin remodeling at hormone-responsive regions
and thereby the access of hormone receptors to hormone-responsive elements. The authors report nucleosomal involvement in progesterone
receptor binding and hormonal gene regulation.
96 Cusick M. E., Herman T. M., DePamphilis M. L. and Wassarman P. M. (1981) Structure of chromatin at deoxyribonucleic acid replication forks: prenucleosomal deoxyribonucleic acid is rapidly
excised from replicating simian virus 40 chromosomes by micrococcal nuclease. Biochemistry 20: 6648-6658
97 Ponts N., Harris E. Y., Prudhomme J., Wick I., Eckhardt-Ludka C., et al. (2010) Nucleosome landscape and control of transcription in the human malaria parasite. Genome Res 20: 228-238
98 Schlesinger F., Smith A. D., Gingeras T. R., Hannon G. J. and Hodges E. (2013) De novo DNA demethylation and noncoding transcription define active intergenic regulatory elements. Genome Res
23: 1601-1614
99 Gaffney D. J., McVicker G., Pai A. A., Fondufe-Mittendorf Y. N., Lewellen N., et al. (2012) Controls of nucleosome positioning in the human genome. PLoS Genet 8: e1003036
100 Schones D. E., Cui K., Cuddapah S., Roh T. Y., Barski A., et al. (2008) Dynamic regulation of nucleosome positioning in the human genome. Cell 132: 887-898
101Boyd-Kirkup J. D., Green C. D., Wu G., Wang D. and Han J. D. (2013) Epigenomics and the regulation of aging. Epigenomics 5: 205-227

89
Deng T., Zhu Z. I., Zhang S., Leng F., Cherukuri S., et al. (2013) HMGN1 modulates nucleosome occupancy and DNase I hypersensitivity at
the CpG island promoters of embryonic stem cells. Mol Cell Biol 33: 3377-3389
Chromatin structure and the interaction of DNA with epigenetic factors and chromatin-remodeling complexes play key roles in regulating
gene expression and embryonic differentiation. In this study, the authors applied ChIP-Seq, DNAse I-Seq, and MNase-Seq on an Illumina
Genome Analyzer to analyze the organization of nucleosomes in relation to DNase I hypersensitivity and transcription in mouse ESCs. They
found that loss of HMG protein HMGN1 affects two important aspects of chromatin organization: altering the nucleosome positioning at the
TSS and reducing the number of DNase I hypersensitivity sites.
Chai X., Nagarajan S., Kim K., Lee K. and Choi J. K. (2013) Regulation of the boundaries of accessible chromatin. PLoS Genet 9: e1003778
Grøntved L., John S., Baek S., Liu Y., Buckley J. R., et al. (2013) C/EBP maintains chromatin accessibility in liver and facilitates glucocorticoid
receptor recruitment to steroid response elements. EMBO J 32: 1568-1583
Maruyama H., Harwood J. C., Moore K. M., Paszkiewicz K., Durley S. C., et al. (2013) An alternative beads-on-a-string chromatin architecture in
Thermococcus kodakarensis. EMBO Rep 14: 711-717
Nagarajavel V., Iben J. R., Howard B. H., Maraia R. J. and Clark D. J. (2013) Global ‘bootprinting’ reveals the elastic architecture of the yeast
TFIIIB-TFIIIC transcription complex in vivo. Nucleic Acids Res 41: 8135-8143
Nishida H., Katayama T., Suzuki Y., Kondo S. and Horiuchi H. (2013) Base composition and nucleosome density in exonic and intronic regions
in genes of the filamentous ascomycetes Aspergillus nidulans and Aspergillus oryzae. Gene 525: 10-May
Tolstorukov M. Y., Sansam C. G., Lu P., Koellhoffer E. C., Helming K. C., et al. (2013) Swi/Snf chromatin remodeling/tumor suppressor complex
establishes nucleosome occupancy at target promoters. Proc Natl Acad Sci U S A 110: 10165-10170
Allan J., Fraser R. M., Owen-Hughes T. and Keszenman-Pereyra D. (2012) Micrococcal nuclease does not substantially bias nucleosome
mapping. J Mol Biol 417: 152-164
Gaffney D. J., McVicker G., Pai A. A., Fondufe-Mittendorf Y. N., Lewellen N., et al. (2012) Controls of nucleosome positioning in the human
genome. PLoS Genet 8: e1003036
Guertin M. J., Martins A. L., Siepel A. and Lis J. T. (2012) Accurate prediction of inducible transcription factor binding intensities in vivo.
PLoS Genet 8: e1002610
Zhang X., Robertson G., Woo S., Hoffman B. G. and Gottardo R. (2012) Probabilistic Inference for Nucleosome Positioning with MNase-Based
or Sonicated Short-Read Data. PLoS ONE 7: e32095

90
Kent N. A., Adams S., Moorhouse A. and Paszkiewicz K. (2011) Chromatin particle spectrum analysis: a method for comparative chromatin
structure analysis using paired-end mode next-generation DNA sequencing. Nucleic Acids Res 39: e26
Xi Y., Yao J., Chen R., Li W. and He X. (2011) Nucleosome fragility reveals novel functional states of chromatin and poises genes for activation.
Ponts N., Harris E. Y., Prudhomme J., Wick I., Eckhardt-Ludka C., et al. (2010) Nucleosome landscape and control of transcription in the human
malaria parasite. Genome Res 20: 228-238
Associated Kits
TruSeq ChIP-Seq®
Kit

91
Exonuclease digestion Immunoprecipitate DNADNA-protein complex DNA
extraction
Crosslink proteins and
DNA
Sample
fragmentation
CHROMATIN IMMUNOPRECIPITATION SEQUENCING (CHIP-SEQ)
Chromatin immunoprecipitation sequencing (ChIP-Seq) is a well-established method to map specific protein-binding sites103
. In this method,
DNA-protein complexes are crosslinked in vivo. Samples are then fragmented and treated with an exonuclease to trim unbound oligonucleotides.
Protein-specific antibodies are used to immunoprecipitate the DNA-protein complex. The DNA is extracted and sequenced, giving high-resolution
sequences of the protein-binding sites.
Pros Cons
• Base-pair resolution of protein-binding site
• Specific regulatory factors or proteins can be mapped
• The use of exonuclease eliminates contamination by
unbound DNA104
• Nonspecific antibodies can dilute the pool of DNA-protein
complexes of interest
• The target protein must be known and able to raise an antibody
References
Berkseth M., Ikegami K., Arur S., Lieb J. D. and Zarkower D. (2013) TRA-1 ChIP-seq reveals regulators of sexual differentiation and multilevel
feedback in nematode sex determination. Proc Natl Acad Sci U S A 110: 16033-16038
In an effort to identify targets of the nematode global sexual regulator Transformer 1 (TRA-1), this study applied Illumina sequencing for
genome-wide ChIP-Seq analysis of TRA-1 binding sites. The authors identified DNA-binding sites driving male-specific expression patterns
and TRA-1 binding sites adjacent to a number of regulatory genes, some of which drive male-specific expression. Overall, the results suggest
that TRA-1 mediates sex-specific expression.
Bowman S. K., Simon M. D., Deaton A. M., Tolstorukov M., Borowsky M. L., et al. (2013) Multiplexed Illumina sequencing libraries from pico-
gram quantities of DNA. BMC Genomics 14: 466
This study reports a simple and fast library construction method from sub-nanogram quantities of DNA. This protocol yields conventional
libraries with barcodes suitable for multiplexed sample analysis on the Illumina platform. The authors demonstrate the method by constructing
a ChIP-Seq library from 100 pg of ChIP DNA that shows equivalent coverage of target regions to a library produced from a larger-scale
experiment.
103 Solomon M. J., Larsen P. L. and Varshavsky A. (1988) Mapping protein-DNA interactions in vivo with formaldehyde: evidence that histone H4 is retained on a highly transcribed gene.
Cell 53: 937-947

92
Kumar V., Muratani M., Rayan N. A., Kraus P., Lufkin T., et al. (2013) Uniform, optimal signal processing of mapped deep-sequencing data.
ChIP-Seq experiments are used to determine the occupation of chromatin by DNA-binding proteins. Data analysis requires detection of
binding signals above the background noise, and a common secondary analysis is the prediction of an effect, e.g., expression, from the level
of the ChIP-Seq signal. This paper presents algorithms adapted from signal processing theory to solve the two general problems of signal
detection and signal estimation from ChIP-Seq data. Using existing data and a new ChIP-Seq data set from an Illumina Genome Analyzer,
the two tools DFilter and EFilter are shown to outperform the most commonly used methods in the field, including MACS and Quest.
Lesch B. J., Dokshin G. A., Young R. A., McCarrey J. R. and Page D. C. (2013) A set of genes critical to development is epigenetically poised
in mouse germ cells from fetal stages through completion of meiosis. Proc Natl Acad Sci U S A 110: 16061-16066
At conception the zygote is totipotent: incorporating the potential to differentiate into any specialized cell in the body. This study used gene
expression profiling and epigenetic regulatory marks (H3K4me3 and H3K37me3) to examine how germ cells change as they progress from
differentiated cell to totipotent zygote. The authors used ChIP-Seq and RNA-Seq on the Illumina HiSeq platform for both male and female
germ cells at three time points surrounding sex differentiation, meiosis, and post-meiosis. Their results indicate central regulatory genes are
maintained in an epigenetically poised state, permitting establishment of totipotency following fertilization.
Illumina Technology: HiSeq2000
Schauer T., Schwalie P. C., Handley A., Margulies C. E., Flicek P., et al. (2013) CAST-ChIP maps cell-type-specific chromatin states in the
Drosophila central nervous system. Cell Rep 5: 271-282
Accurate assays for epigenetic markers have been limited by the amount of input material required. This study presents a new assay (CAST-
ChIP), based on Illumina sequencing, that allows for characterization of chromatin-associated proteins from specific cell types in complex
tissues. The study validates the assay by profiling PolII and H2A.Z across both glia and neurons in Drosophila brain tissue.
Koldamova R., Schug J., Lefterova M., Cronican A. A., Fitz N. F., et al. (2014) Genome-wide approaches reveal EGR1-controlled regulatory
networks associated with neurodegeneration. Neurobiol Dis 63: 107-114
McMullen P. D., Bhattacharya S., Woods C. G., Sun B., Yarborough K., et al. (2014) A map of the PPARalpha transcription regulatory network
for primary human hepatocytes. Chem Biol Interact 209: 14-24
Waszak S. M., Kilpinen H., Gschwind A. R., Orioli A., Raghav S. K., et al. (2014) Identification and removal of low-complexity sites in
allele-specific analysis of ChIP-seq data. Bioinformatics 30: 165-171
Wolchinsky Z., Shivtiel S., Kouwenhoven E. N., Putin D., Sprecher E., et al. (2014) Angiomodulin is required for cardiogenesis of embryonic stem
cells and is maintained by a feedback loop network of p63 and Activin-A. Stem Cell Res 12: 49-59
Biancolella M., B K. F., Tring S., Plummer S. J., Mendoza-Fandino G. A., et al. (2013) Identification and characterization of functional risk variants
for colorectal cancer mapping to chromosome 11q23.1. Hum Mol Genet
Chang C. Y., Pasolli H. A., Giannopoulou E. G., Guasch G., Gronostajski R. M., et al. (2013) NFIB is a governor of epithelial-melanocyte stem
cell behaviour in a shared niche. Nature 495: 98-102

93
Chauhan C., Zraly C. B. and Dingwall A. K. (2013) The Drosophila COMPASS-like Cmi-Trr coactivator complex regulates dpp/BMP signaling in
pattern formation. Dev Biol 380: 185-198
Jain A., Bacolla A., Del Mundo I. M., Zhao J., Wang G., et al. (2013) DHX9 helicase is involved in preventing genomic instability induced by
alternatively structured DNA in human cells. Nucleic Acids Res 41: 10345-10357
Lai C. F., Flach K. D., Alexi X., Fox S. P., Ottaviani S., et al. (2013) Co-regulated gene expression by oestrogen receptor alpha and liver receptor
homolog-1 is a feature of the oestrogen response in breast cancer cells. Nucleic Acids Res 41: 10228-10240
Lo K. A., Labadorf A., Kennedy N. J., Han M. S., Yap Y. S., et al. (2013) Analysis of in vitro insulin-resistance models and their physiological
relevance to in vivo diet-induced adipose insulin resistance. Cell Rep 5: 259-270
Maehara K., Odawara J., Harada A., Yoshimi T., Nagao K., et al. (2013) A co-localization model of paired ChIP-seq data using a large ENCODE
data set enables comparison of multiple samples. Nucleic Acids Res 41: 54-62
Mazzoni E. O., Mahony S., Closser M., Morrison C. A., Nedelec S., et al. (2013) Synergistic binding of transcription factors to cell-specific
enhancers programs motor neuron identity. Nat Neurosci 16: 1219-1227
Mendoza-Parra M. A., Van Gool W., Mohamed Saleem M. A., Ceschin D. G. and Gronemeyer H. (2013) A quality control system for profiles
obtained by ChIP sequencing. Nucleic Acids Res 41: e196
Neyret-Kahn H., Benhamed M., Ye T., Le Gras S., Cossec J. C., et al. (2013) Sumoylation at chromatin governs coordinated repression of a
transcriptional program essential for cell growth and proliferation. Genome Res 23: 1563-1579
Olovnikov I., Ryazansky S., Shpiz S., Lavrov S., Abramov Y., et al. (2013) De novo piRNA cluster formation in the Drosophila germ line triggered
by transgenes containing a transcribed transposon fragment. Nucleic Acids Res 41: 5757-5768
Prickett A. R., Barkas N., McCole R. B., Hughes S., Amante S. M., et al. (2013) Genome-wide and parental allele-specific analysis of CTCF and
cohesin DNA binding in mouse brain reveals a tissue-specific binding pattern and an association with imprinted differentially methylated regions.
Genome Res 23: 1624-1635
Schmolka N., Serre K., Grosso A. R., Rei M., Pennington D. J., et al. (2013) Epigenetic and transcriptional signatures of stable versus plastic
differentiation of proinflammatory gammadelta T cell subsets. Nat Immunol 14: 1093-1100
Sakabe N. J., Aneas I., Shen T., Shokri L., Park S. Y., et al. (2012) Dual transcriptional activator and repressor roles of TBX20 regulate adult
cardiac structure and function. Hum Mol Genet 21: 2194-2204
Tanaka Y., Joshi A., Wilson N. K., Kinston S., Nishikawa S., et al. (2012) The transcriptional programme controlled by Runx1 during early
embryonic blood development. Dev Biol 366: 404-419
Tang B., Becanovic K., Desplats P. A., Spencer B., Hill A. M., et al. (2012) Forkhead box protein p1 is a transcriptional repressor of immune
signaling in the CNS: implications for transcriptional dysregulation in Huntington disease. Hum Mol Genet 21: 3097-3111
Zhan Q., Fang Y., He Y., Liu H. X., Fang J., et al. (2012) Function annotation of hepatic retinoid x receptor alpha based on genome-wide DNA
binding and transcriptome profiling. PLoS One 7: e50013
Associated Kits
TruSeq ChIP-Seq Kit

94
DNAOpen DNA Crosslink protein and DNA
with formalin
Sonicate Phenol extract and purify DNA
from the aquous phase
FORMALDEHYDE-ASSISTED ISOLATION OF REGULATORY ELEMENTS (FAIRE-SEQ)
Formaldehyde-assisted isolation of regulatory elements (FAIRE-Seq)105,106
is based on differences in crosslinking efficiencies between DNA
and nucleosomes or sequence-specific DNA-binding proteins. In this method, DNA-protein complexes are briefly crosslinked in vivo using
formaldehyde. The sample is then lysed and sonicated. After phenol/chloroform extraction, the DNA in the aqueous phase is purified and
sequenced. Sequencing provides information for regions of DNA that are not occupied by histones.
Pros Cons
• Simple and highly reproducible protocol
• Does not require antibodies
• Does not require enzymes, such as DNase or MNase, avoiding
the optimization and extra steps necessary for
enzymatic processing
• Does not require a single-cell suspension or nuclear isolation, so
it is easily adapted for use on tissue samples107
• Cannot identify regulatory proteins bound to DNA
• DNase-Seq may be better at identifying nucleosome-depleted
promoters of highly expressed genes108
References
Hilton I. B., Simon J. M., Lieb J. D., Davis I. J., Damania B., et al. (2013) The open chromatin landscape of Kaposi’s sarcoma-associated
herpesvirus. J Virol 87: 11831-11842
Kaposi’s sarcoma-associated herpesvirus (KSHV) is a gammaherpesvirus that, upon infection, remains in a latent state. Histone modifications
occupy inactive regions of the latent viral genome. The authors use FAIRE-Seq on the Illumina HiSeq 2000 system to study open chromatin
regions in the KSHV genome, allowing them to identify regions of open chromatin in the latent virus. By integrating data on histone
modifications, they were able to generate a genome-wide KSHV landscape, which indicated localization of active histone modifications near
nucleosome-depleted sites.
Illumina Technology: TruSeq Sample Prep Kit, HiSeq 2000
105 Giresi P. G. and Lieb J. D. (2009) Isolation of active regulatory elements from eukaryotic chromatin using FAIRE (Formaldehyde Assisted Isolation of Regulatory Elements). Methods 48: 233-239
106 Hogan G. J., Lee C. K. and Lieb J. D. (2006) Cell cycle-specified fluctuation of nucleosome occupancy at gene promoters. PLoS Genet 2: e158
107 Simon J. M., Giresi P. G., Davis I. J. and Lieb J. D. (2012) Using formaldehyde-assisted isolation of regulatory elements (FAIRE) to isolate active regulatory DNA. Nat Protoc 7: 256-267
108 Song L., Zhang Z., Grasfeder L. L., Boyle A. P., Giresi P. G., et al. (2011) Open chromatin defined by DNaseI and FAIRE identifies regulatory elements that shape cell-type identity. Genome Res 21:
1757-1767

95
Meredith D. M., Borromeo M. D., Deering T. G., Casey B. H., Savage T. K., et al. (2013) Program specificity for Ptf1a in pancreas versus
neural tube development correlates with distinct collaborating cofactors and chromatin accessibility. Mol Cell Biol 33: 3166-3179
Transcription factors (TFs) are the drivers of cell development and differentiation. The combined regulatory effects of different TFs allow
any factor to play key roles in the different pathways of cell differentiation. This study examined how pancreas-specific transcription factor
1a (Ptf1a) is a critical driver for development of both the pancreas and nervous system. Using Illumina sequencing to perform ChIP-Seq
for Ptf1a, FAIRE-Seq to detect open chromatin, and RNA-Seq for expression profiling, the authors characterized Fox and Sox factors as
potential lineage-specific modifiers of Ptf1a binding.
Paul D. S., Albers C. A., Rendon A., Voss K., Stephens J., et al. (2013) Maps of open chromatin highlight cell type-restricted patterns of
regulatory sequence variation at hematological trait loci. Genome Res 23: 1130-1141
Genome-wide association studies (GWAS) have discovered many non–protein-coding loci associated with complex traits. However, due to
the low resolution of GWAS, the exact location of the causative variant is often not known. In this study, the authors combined GWAS results
with FAIRE-Seq to link complex hematopoietic traits to specific functional loci. They found that the majority of candidate functional variants
coincided with binding sites of five transcription factors key to regulating megakaryopoiesis, and further found that 76.9% of the candidate
regulatory variants affected protein binding at these sites. In conclusion, the combination of GWAS data with high-resolution epigenetic
profiling by sequencing is a powerful assay for mapping complex genetic variants.
, Human Gene Expression—BeadArray
Chai X., Nagarajan S., Kim K., Lee K. and Choi J. K. (2013) Regulation of the boundaries of accessible chromatin. PLoS Genet 9: e1003778
Calabrese J. M., Sun W., Song L., Mugford J. W., Williams L., et al. (2012) Site-specific silencing of regulatory elements as a mechanism of X
inactivation. Cell 151: 951-963
Simon J. M., Giresi P. G., Davis I. J. and Lieb J. D. (2012) Using formaldehyde-assisted isolation of regulatory elements (FAIRE) to isolate active
regulatory DNA. Nat Protoc 7: 256-267
Paul D. S., Nisbet J. P., Yang T. P., Meacham S., Rendon A., et al. (2011) Maps of open chromatin guide the functional follow-up of genome-
wide association signals: application to hematological traits. PLoS Genet 7: e1002139
Ponts N., Harris E. Y., Prudhomme J., Wick I., Eckhardt-Ludka C., et al. (2010) Nucleosome landscape and control of transcription in the human
malaria parasite. Genome Res 20: 228-238
Auerbach R. K., Euskirchen G., Rozowsky J., Lamarre-Vincent N., Moqtaderi Z., et al. (2009) Mapping accessible chromatin regions using
Sono-Seq. Proc Natl Acad Sci U S A 106: 14926-14931
Associated Kits
TruSeq ChIP-Seq Kit

96
Blood draw
5 min
Sequencing
240 min- 120 h
CD4+
T-call purification
90 min
Transposition amplification
180 min
Fragmented and primed DNATn5TransposomeOpen DNA DNA purification
Amplification
Insert in regions of open chromatin
ASSAY FOR TRANSPOSASE-ACCESSIBLE CHROMATIN SEQUENCING (ATAC-SEQ)
Assay for transposase-accessible chromatin using sequencing (ATAC-Seq) is a protocol that utilizes the Epicentre Tn5 transposome109
. In
this method, DNA is incubated with Tn5 transposome, which performs adaptor ligation and fragmentation of open chromatin regions. Deep
sequencing of the purified regions provides base-pair resolution of nucleosome-free regions in the genome.
Pros Cons
• Two-step protocol with no adaptor ligation steps, gel
purification, or crosslink reversal
• Very high signal to noise ratio compared to FAIRE-Seq
• During mechanical sample processing, bound chromatin regions
might open and be tagged by the transposome
References
Buenrostro J. D., Giresi P. G., Zaba L. C., Chang H. Y. and Greenleaf W. J. (2013) Transposition of native chromatin for fast and sensitive
epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods 10: 1213-1218
This is the first paper to describe ATAC-seq as a protocol to study regions of open chromatin. The authors identify the location of
DNA-binding proteins in a B-cell line. They demonstrate that the protocol can analyze an individual’s T-cell epigenome on a timescale
compatible with clinical decision-making.
Illumina Technology: MiSeq, HiSeq 2000
109 Buenrostro J. D., Giresi P. G., Zaba L. C., Chang H. Y. and Greenleaf W. J. (2013) Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding
proteins and nucleosome position. Nat Methods 10: 1213-1218
ATAC-Seq enables real-time personal epigenomics.
Associated Kits
EpiGnome™
Methyl-Seq Kit
TruSeq ChIP-Seq Kit

97
Sample fragmentation Immunoprecipitate Ligation Restriction enzyme digestion DNA
CHROMATIN INTERACTION ANALYSIS BY PAIRED-END TAG SEQUENCING (CHIA-PET)
Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is a variation of Hi-C that features an immunoprecipitation step to map
long-range DNA interactions110, 111
. In this method, DNA-protein complexes are crosslinked and fragmented. Specific antibodies are used to im-
munoprecipitate proteins of interest. Specific linkers are ligated to the DNA fragments, which ligate when in proximity. Linkers are then precipitated
and digested with an enzyme and the DNA is sequenced. Deep sequencing provides base-pair resolution of ligated fragments. Hi-C and ChIA-PET
currently provide the best balance of resolution and reasonable coverage in the human genome to map long-range interactions112
Pros Cons
• Suitable for detecting a large number of both long-range and
short range chromatin interactions globally113
• Studies the interactions made by specific proteins or
protein complexes
• Provides information about DNA interactions aided by
regulatory elements
• Removes background generated during traditional ChIP assays
• The immunoprecipitation step reduces data complexity113
• Nonspecific antibodies can pull down unwanted protein complexes
and contaminate the pool
• Linkers can self-ligate, generating ambiguity about true
DNA interactions
• Limited sensitivity; may detect as little as 10% of interactions113
110 Li G., Fullwood M. J., Xu H., Mulawadi F. H., Velkov S., et al. (2010) ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing. Genome Biol 11: R22
111 Fullwood M. J., Liu M. H., Pan Y. F., Liu J., Xu H., et al. (2009) An oestrogen-receptor-alpha-bound human chromatin interactome. Nature 462: 58-64
112 Dekker J., Marti-Renom M. A. and Mirny L. A. (2013) Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat Rev Genet 14: 390-403
113 Sajan S. A. and Hawkins R. D. (2012) Methods for identifying higher-order chromatin structure. Annu Rev Genomics Hum Genet 13: 59-82
References
DeMare L. E., Leng J., Cotney J., Reilly S. K., Yin J., et al. (2013) The genomic landscape of cohesin-associated chromatin interactions.
Genome Res 23: 1224-1234
Knockdown of cohesin in ESCs results in aberrant gene expression and loss of pluripotency. Cohesin works to stabilize DNA by forming
loops between distant-acting enhancers and their target promoters. The authors studied cohesin interaction in the developing limb using
ChIA-PET, RNA-Seq, and ChIP-Seq analysis performed on a HiSeq 2000 system. They report tissue-specific enhancer-promoter interactions
involving cohesin and the insulator protein CTCF. They also identified interactions that are maintained for tissue-specific activation or
repression during development.
Illumina Technology: TruSeq Sample Prep Kit, HiSeq 2000

98
Stadhouders R., Kolovos P., Brouwer R., Zuin J., van den Heuvel A., et al. (2013) Multiplexed chromosome conformation capture
sequencing for rapid genome-scale high-resolution detection of long-range chromatin interactions. Nat Protoc 8: 509-524
This paper presents an assay for multiplexed chromosome conformation capture sequencing (3C-Seq) using an Illumina HiSeq 2000 system.
This high-throughput assay outperforms PCR-based methods for ease of multiplexing, and outperforms 5C and Hi-C methods in terms of
cost and ease of analysis. The preparation of multiplexed 3C-Seq libraries can be performed by any investigator with basic skills in molecular
biology techniques, and the data analysis requires only basic expertise in bioinformatics.
Li G., Ruan X., Auerbach R. K., Sandhu K. S., Zheng M., et al. (2012) Extensive promoter-centered chromatin interactions provide a topological
basis for transcription regulation. Cell 148: 84-98
Zhang J., Poh H. M., Peh S. Q., Sia Y. Y., Li G., et al. (2012) ChIA-PET analysis of transcriptional chromatin interactions. Methods 58: 289-299
Tan S. K., Lin Z. H., Chang C. W., Varang V., Chng K. R., et al. (2011) AP-2gamma regulates oestrogen receptor-mediated long-range chromatin
interaction and gene transcription. EMBO J 30: 2569-2581
Fullwood M. J., Han Y., Wei C. L., Ruan X. and Ruan Y. (2010) Chromatin interaction analysis using paired-end tag sequencing. Curr Protoc Mol
Biol Chapter 21: Unit 21 15 21-25
Li G., Fullwood M. J., Xu H., Mulawadi F. H., Velkov S., et al. (2010) ChIA-PET tool for comprehensive chromatin interaction analysis with paired-
end tag sequencing. Genome Biol 11: R22
Associated Kits
TruSeq ChIP-Seq Kit
Nextera Mate®
Pair Kit

99
LigationCrosslink proteins and DNA Sample fragmentation PCR amplify ligated junctions DNA
CHROMATIN CONFORMATION CAPTURE (HI-C/3C-SEQ)
Chromatin conformation capture sequencing (Hi-C)114
or 3C-Seq115
is used to analyze chromatin interactions. In this method, DNA-protein
complexes are crosslinked using formaldehyde. The sample is fragmented and DNA ligated and digested. The resulting DNA fragments are
PCR-amplified and sequenced. Deep sequencing provides base-pair resolution of ligated fragments.
Pros Cons
• Allows detection of long-range DNA interactions
• High-throughput method
• Detection may result from random chromosomal collisions
• 3C PCR is difficult and requires careful controls and
experimental design
• Needs further confirmation of interaction
• Due to multiple steps, the method requires large amounts of
starting material
114 Lieberman-Aiden E., van Berkum N. L., Williams L., Imakaev M., Ragoczy T., et al. (2009) Comprehensive mapping of long-range interactions reveals folding principles of the human genome.
Science 326: 289-293
115 Duan Z., Andronescu M., Schutz K., Lee C., Shendure J., et al. (2012) A genome-wide 3C-method for characterizing the three-dimensional architectures of genomes. Methods 58: 277-288
References
Burton J. N., Adey A., Patwardhan R. P., Qiu R., Kitzman J. O., et al. (2013) Chromosome-scale scaffolding of de novo genome assemblies
based on chromatin interactions. Nat Biotechnol 31: 1119-1125
The authors integrate shotgun fragment and short insert mate-pair sequences with Hi-C data to generate assemblies for human, mouse,
and Drosophila genomes. The paper reports a bioinformatics tool used to compute the assemblies: ligating adjacent chromatin enables
scaffolding in situ (LACHESIS).
Jin F., Li Y., Dixon J. R., Selvaraj S., Ye Z., et al. (2013) A high-resolution map of the three-dimensional chromatin interactome in human cells.
Nature 503: 290-294
Cis-acting regulatory elements in the genome interact with their target gene promoter by transcription factors bringing the two locations close
in the three-dimensional conformation of the chromatin. In this study, the chromosome conformation is studied by a genome-wide analysis
method (Hi-C) using the Illumina HiSeq 2000 system. The authors determined over one million long-range chromatin interactions in human
fibroblasts. In addition, they characterized the dynamics of promoter-enhancer contacts after TNF-alpha signaling and discovered pre-existing
chromatin looping with TNF-alpha–responsive enhancers, suggesting the three-dimensional chromatin conformation may be stable over time.

100
Nagano T., Lubling Y., Stevens T. J., Schoenfelder S., Yaffe E., et al. (2013) Single-cell Hi-C reveals cell-to-cell variability in chromosome
structure. Nature 502: 59-64
Belton J. M., McCord R. P., Gibcus J. H., Naumova N., Zhan Y., et al. (2012) Hi-C: a comprehensive technique to capture the conformation of
genomes. Methods 58: 268-276
Demichelis F., Setlur S. R., Banerjee S., Chakravarty D., Chen J. Y., et al. (2012) Identification of functionally active, low frequency copy number
variants at 15q21.3 and 12q21.31 associated with prostate cancer risk. Proc Natl Acad Sci U S A 109: 6686-6691
Dixon J. R., Selvaraj S., Yue F., Kim A., Li Y., et al. (2012) Topological domains in mammalian genomes identified by analysis of chromatin
interactions. Nature 485: 376-380
Imakaev M, Fudenberg G, McCord RP, Naumova N, Goloborodko A, Lajoie BR, Dekker J, Mirny LA; (2012) Iterative correction of Hi-C data
reveals hallmarks of chromosome organization. Nat Methods 9: 999-1003
Imakaev M., Fudenberg G., McCord R. P., Naumova N., Goloborodko A., et al. (2012) Iterative correction of Hi-C data reveals hallmarks of
chromosome organization. Nat Methods 9: 999-1003
Irimia M., Tena J. J., Alexis M. S., Fernandez-Minan A., Maeso I., et al. (2012) Extensive conservation of ancient microsynteny across metazoans
due to cis-regulatory constraints. Genome Res 22: 2356-2367
Lan X., Witt H., Katsumura K., Ye Z., Wang Q., et al. (2012) Integration of Hi-C and ChIP-seq data reveals distinct types of chromatin linkages.
Nucleic Acids Res 40: 7690-7704
Verma-Gaur J., Torkamani A., Schaffer L., Head S. R., Schork N. J., et al. (2012) Noncoding transcription within the Igh distal V(H) region at
PAIR elements affects the 3D structure of the Igh locus in pro-B cells. Proc Natl Acad Sci U S A 109: 17004-17009
Zhang Y., McCord R. P., Ho Y. J., Lajoie B. R., Hildebrand D. G., et al. (2012) Spatial organization of the mouse genome and its role in recurrent
chromosomal translocations. Cell 148: 908-921
Yaffe E. and Tanay A. (2011) Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal
architecture. Nat Genet 43: 1059-1065
Yaffe E, Tanay A; (2011) Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal
architecture. Nat Genet 43: 1059-65
Lieberman-Aiden E., van Berkum N. L., Williams L., Imakaev M., Ragoczy T., et al. (2009) Comprehensive mapping of long-range interactions
reveals folding principles of the human genome. Science 326: 289-293
Associated Kits
TruSeq ChIP-Seq Kit
Nextera Mate Pair Kit

101
LigationCrosslink proteins and DNA Sample fragmentation DNARestriction digest
Self-circularization
and Reverse PCR
CIRCULAR CHROMATIN CONFORMATION CAPTURE (4-C OR 4C-SEQ)
Circular chromatin conformation capture (4-C)116
, also called 4C-Seq, is a method similar to 3-C and is sometimes called circular 3C. It allows the
unbiased detection of all genomic regions that interact with a particular region of interest117
. In this method, DNA-protein complexes are crosslinked
using formaldehyde. The sample is fragmented, and the DNA is ligated and digested. The resulting DNA fragments self-circularize, followed by
reverse PCR and sequencing. Deep sequencing provides base-pair resolution of ligated fragments.
Pros Cons
• 4C is the preferred strategy to assess the DNA contact profile
of individual genomic sites.
• Highly reproducible data
• Will miss local interactions ( 50 kb) from the region of interest
• The large circles do not PCR efficiently
116 Zhao Z., Tavoosidana G., Sjolinder M., Gondor A., Mariano P., et al. (2006) Circular chromosome conformation capture (4C) uncovers extensive networks of epigenetically regulated intra- and
interchromosomal interactions. Nat Genet 38: 1341-1347
References
de Wit E., Bouwman B. A., Zhu Y., Klous P., Splinter E., et al. (2013) The pluripotent genome in three dimensions is shaped around
pluripotency factors. Nature 501: 227-231
Transcriptional regulation is influenced by the availability of specific transcription factors, but the evidence is increasing for the substantial
importance of chromatin conformation within the nucleus. In this study, Illumina sequencing is used to analyze chromatin conformation by
a genome-wide assay (4-C) demonstrating, along with ChIP-Seq data, that inactive chromatin is disorganized in PSC nuclei. In contrast to
inactive chromatin, promoters are seen to engage in contacts between topological domains in a tissue-dependent manner, while enhancers
have a more tissue-restricted interaction. The authors hypothesize that the chromatin interactions enhance the robustness of the
pluripotent state.
, HiSeq 2000

102
Holwerda S. J., van de Werken H. J., Ribeiro de Almeida C., Bergen I. M., de Bruijn M. J., et al. (2013) Allelic exclusion of the immunoglobulin
heavy chain locus is independent of its nuclear localization in mature B cells. Nucleic Acids Res 41: 6905-6916
Chromatin conformation is one of many mechanisms for regulating gene expression. In developing B cells, the immunoglobulin heavy chain
(IgH) locus undergoes a scheduled genomic rearrangement of the V, D, and J gene segments. In this study, an allele-specific chromosome
conformation capture sequencing technique (4C-Seq) was applied to unambiguously follow the individual IgH alleles in mature B
lymphocytes. The authors found that IgH adopts a lymphoid-specific nuclear location, and in mature B cells the distal VH regions of both IgH
alleles position themselves away from active chromatin.
, HiSeq 2000
Wei Z., Gao F., Kim S., Yang H., Lyu J., et al. (2013) Klf4 organizes long-range chromosomal interactions with the oct4 locus in
reprogramming and pluripotency. Cell Stem Cell 13: 36-47
PSCs are capable of differentiation into diverse cell types. The maintenance of pluripotency and the induction of differentiation are both highly
regulated processes. This study examined the epigenetic mechanisms underlying reprogramming of PSCs. Using circular chromosome
conformation capture with Illumina HiSeq sequencing technology (4C-Seq), the authors profiled the PSC-specific long-range chromosomal
interactions during reprogramming to induced PSCs. The high-resolution genome-wide interaction map and a well-designed experimental
setup allowed the authors to show evidence for a functional role of Kruppel-like factor 4 (Klf4) in facilitating long-range interactions.
, HiSeq2000
Chaumeil J., Micsinai M., Ntziachristos P., Roth D. B., Aifantis I., et al. (2013) The RAG2 C-terminus and ATM protect genome integrity by
controlling antigen receptor gene cleavage. Nat Commun 4: 2231
Delpretti S., Montavon T., Leleu M., Joye E., Tzika A., et al. (2013) Multiple Enhancers Regulate Hoxd Genes and the Hotdog LncRNA during
Cecum Budding. Cell Rep 5: 137-150
Denholtz M., Bonora G., Chronis C., Splinter E., de Laat W., et al. (2013) Long-Range Chromatin Contacts in Embryonic Stem Cells Reveal a
Role for Pluripotency Factors and Polycomb Proteins in Genome Organization. Cell Stem Cell 13: 602-616
Jankovic M., Feldhahn N., Oliveira T. Y., Silva I. T., Kieffer-Kwon K. R., et al. (2013) 53BP1 alters the landscape of DNA rearrangements and
suppresses AID-induced B cell lymphoma. Mol Cell 49: 623-631
Medvedovic J., Ebert A., Tagoh H., Tamir I. M., Schwickert T. A., et al. (2013) Flexible long-range loops in the VH gene region of the Igh locus
facilitate the generation of a diverse antibody repertoire. Immunity 39: 229-244
Yamane A., Robbiani D. F., Resch W., Bothmer A., Nakahashi H., et al. (2013) RPA Accumulation during Class Switch Recombination
Represents 5’-3’ DNA-End Resection during the S-G2/M Phase of the Cell Cycle. Cell Rep 3: 138-147
Hakim O., Resch W., Yamane A., Klein I., Kieffer-Kwon K. R., et al. (2012) DNA damage defines sites of recurrent chromosomal translocations in
B lymphocytes. Nature 484: 69-74
Kovalchuk A. L., Ansarah-Sobrinho C., Hakim O., Resch W., Tolarova H., et al. (2012) Mouse model of endemic Burkitt translocations reveals
the long-range boundaries of Ig-mediated oncogene deregulation. Proc Natl Acad Sci U S A 109: 10972-10977

103
Rocha P. P., Micsinai M., Kim J. R., Hewitt S. L., Souza P. P., et al. (2012) Close proximity to Igh is a contributing factor to AID-mediated
translocations. Mol Cell 47: 873-885
Sexton T., Kurukuti S., Mitchell J. A., Umlauf D., Nagano T., et al. (2012) Sensitive detection of chromatin coassociations using enhanced
chromosome conformation capture on chip. Nat Protoc 7: 1335-1350
Splinter E., de Wit E., van de Werken H. J., Klous P. and de Laat W. (2012) Determining long-range chromatin interactions for selected
genomic sites using 4C-seq technology: from fixation to computation. Methods 58: 221-230
van de Werken H. J., Landan G., Holwerda S. J., Hoichman M., Klous P., et al. (2012) Robust 4C-seq data analysis to screen for regulatory
DNA interactions. Nat Methods 9: 969-972
Hakim O., Sung M. H., Voss T. C., Splinter E., John S., et al. (2011) Diverse gene reprogramming events occur in the same spatial clusters
of distal regulatory elements. Genome Res 21: 697-706
Oliveira T., Resch W., Jankovic M., Casellas R., Nussenzweig M. C., et al. (2011) Translocation capture sequencing: A method for high
throughput mapping of chromosomal rearrangements. J Immunol Methods 375: 176-181
Robyr D., Friedli M., Gehrig C., Arcangeli M., Marin M., et al. (2011) Chromosome conformation capture uncovers potential genome-wide
interactions between human conserved non-coding sequences. PLoS ONE 6: e17634
Rodley C. D., Pai D. A., Mills T. A., Engelke D. R. and O’Sullivan J. M. (2011) tRNA gene identity affects nuclear positioning.
PLoS One 6: e29267
Associated Kits
TruSeq ChIP-Seq Kit

104
LigationCrosslink proteins and DNA Sample fragmentation LMA: Ligation-mediated amplification DNA
T7 T3
CHROMATIN CONFORMATION CAPTURE CARBON COPY (5-C)
Chromatin conformation capture carbon copy (5-C)118
allows concurrent determination of interactions between multiple sequences and is a high-
throughput version of 3-C119
. In this method, DNA-protein complexes are crosslinked using formaldehyde. The sample is fragmented and the DNA
ligated and digested. The resulting DNA fragments are amplified using ligation-mediated PCR and sequenced. Deep sequencing provides base-
pair resolution of ligated fragments.
Pros Cons
• Different from 4-C, 5C provides a matrix of interaction frequencies
for many pairs of sites
• Can be used to reconstruct the (average) 3D conformation of
larger genomic regions120
• Detection may not necessarily mean an interaction, resulting from
random chromosomal collisions
• Needs further confirmation of interaction
• Cannot scale to genome-wide studies that would require large
amount of primers
References
Nora E. P., Lajoie B. R., Schulz E. G., Giorgetti L., Okamoto I., et al. (2012) Spatial partitioning of the regulatory landscape of the
X-inactivation centre. Nature 485: 381-385
The authors use 5-C to analyze regulation of Xist, a non–protein coding transcript that is controlled by X-inactivation center (Xic) to initiate X
chromosome inactivation in mouse. They identify a regulatory region of Xist antisense unit that produces a long overriding RNA.
Sanyal A., Lajoie B. R., Jain G. and Dekker J. (2012) The long-range interaction landscape of gene promoters. Nature 489: 109-113
Associated Kits
TruSeq ChIP-Seq Kit
118 Dostie J. and Dekker J. (2007) Mapping networks of physical interactions between genomic elements using 5C technology. Nat Protoc 2: 988-1002
120 de Wit E. and de Laat W. (2012) A decade of 3C technologies: insights into nuclear organization. Genes Dev 26: 11-24

105
SEQUENCE REARRANGEMENTS
A growing body of evidence suggests that somatic genomic rearrangements, such as retrotransposition and copy number variants (CNVs), are
relatively common in healthy individuals121,122,123
. Cancer genomes are also known to contain numerous complex rearrangements124
. While many of
these rearrangements can be detected during routine next-generation sequencing, specific techniques are available to study rearrangements such
as transposable elements.
Transposable genetic elements (TEs) comprise a vast array of DNA sequences with the ability to move to new sites in genomes either directly by
a cut-and-paste mechanism (transposons) or indirectly through an RNA intermediate (retrotransposons)125
. TEs make up about 66-69% of the hu-
man genome126
and play roles in ageing, cancers, brain, development, embryogenesis, and phenotypic variation in populations and evolution. TEs
played a major role in dynamic arrangement of the sex determining region over evolution, giving us distinct X and Y chromosomes127
.
Along with sequence rearrangements by TEs, chromosome and centromere rearrangements can also lead to multiple diseases and disorders128
.
Prenatal diagnostics to study rearrangements predict genetic abnormalities in the fetus. The role of specific TEs and the primary mechanism of
chromosome and centromere rearrangements have yet to be elucidated; studying them will help understand their roles.
121 O’Huallachain M., Weissman S. M. and Snyder M. P. (2013) The variable somatic genome. Cell Cycle 12: 5-6
122 Macosko E. Z. and McCarroll S. A. (2013) Genetics. Our fallen genomes. Science 342: 564-565
123 McConnell M. J., Lindberg M. R., Brennand K. J., Piper J. C., Voet T., et al. (2013) Mosaic copy number variation in human neurons. Science 342: 632-637
124 Malhotra A., Lindberg M., Faust G. G., Leibowitz M. L., Clark R. A., et al. (2013) Breakpoint profiling of 64 cancer genomes reveals numerous complex rearrangements spawned by homology-
independent mechanisms. Genome Res 23: 762-776
125 Fedoroff N. V. (2012) Presidential address. Transposable elements, epigenetics, and genome evolution. Science 338: 758-767
126 Grandi F. C. and An W. (2013) Non-LTR retrotransposons and microsatellites: Partners in genomic variation. Mob Genet Elements 3: e25674
127 Gschwend A. R., Weingartner L. A., Moore R. C. and Ming R. (2012) The sex-specific region of sex chromosomes in animals and plants. Chromosome Res 20: 57-69
128 Chiarle R. (2013) Translocations in normal B cells and cancers: insights from new technical approaches. Adv Immunol 117: 39-71
Transposable elements involved in the evolution of sex chromosomes.

106
Reviews
Bunting S. F. and Nussenzweig A. (2013) End-joining, translocations and cancer. Nat Rev Cancer 13: 443-454
Chiarle R. (2013) Translocations in normal B cells and cancers: insights from new technical approaches.
Adv Immunol 117: 39-71
Gifford W. D., Pfaff S. L. and Macfarlan T. S. (2013) Transposable elements as genetic regulatory substrates in early development.
Trends Cell Biol 23: 218-226
Grandi F. C. and An W. (2013) Non-LTR retrotransposons and microsatellites: Partners in genomic variation.
Mob Genet Elements 3: e25674
van Opijnen T. and Camilli A. (2013) Transposon insertion sequencing: a new tool for systems-level analysis of microorganisms.
Nat Rev Microbiol 11: 435-442
Burns K. H. and Boeke J. D. (2012) Human transposon tectonics. Cell 149: 740-752
Fedoroff N. V. (2012) Presidential address. Transposable elements, epigenetics, and genome evolution. Science 338: 758-767
Gschwend A. R., Weingartner L. A., Moore R. C. and Ming R. (2012) The sex-specific region of sex chromosomes in animals
and plants. Chromosome Res 20: 57-69
Hancks D. C. and Kazazian H. H., Jr. (2012) Active human retrotransposons: variation and disease.
Curr Opin Genet Dev 22: 191-203
Febrer M., McLay K., Caccamo M., Twomey K. B. and Ryan R. P. (2011) Advances in bacterial transcriptome and transposon insertion-site
profiling using second-generation sequencing. Trends Biotechnol 29: 586-594

107
Retrotransposon
binding sites
Genomic DNA Fractionate DNA fragments Hybridize Microarray with
transposon binding sites
Read1
Read2
Transposon sites
Sequenced fragment
Reference sequence Align
Known retrotrans-
poson insertion
Novel retrotranspo-
sition events
RETROTRANSPOSON CAPTURE SEQUENCING (RC-SEQ)
Retrotransposon capture sequencing (RC-Seq) is a high-throughput protocol to map and study retrotransposon insertions129
. In this method,
after genomic DNA is fractionated, retrotransposon binding sites on DNA hybridize to transposon binding sites on a microarray. Deep sequencing
provides accurate information that can be aligned to a reference sequence to discover novel retrotransposition events.
Pros Cons
• Ability to clearly identify and detect novel
retrotransposition events
• Can specifically study transposon binding sites of interest
• High-throughput protocol
• Different types of MEI require separate PCR experiments with
different primers130
• Hybridization errors can lead to sequencing unwanted
DNA fragments
• Similar transposition binding sites can lead to sequence ambiguity
and detection for a transposition event
129 Baillie J. K., Barnett M. W., Upton K. R., Gerhardt D. J., Richmond T. A., et al. (2011) Somatic retrotransposition alters the genetic landscape of the human brain. Nature 479: 534-537
130 Xing J., Witherspoon D. J. and Jorde L. B. (2013) Mobile element biology: new possibilities with high-throughput sequencing. Trends Genet 29: 280-289
References
Shukla R., Upton K. R., Munoz-Lopez M., Gerhardt D. J., Fisher M. E., et al. (2013) Endogenous retrotransposition activates oncogenic path-
ways in hepatocellular carcinoma. Cell 153: 101-111
LINE-1 (L1) retrotransposons are mobile genetic elements comprising ~17% of the human genome. To investigate the significance of novel
L1 insertions in cancer, this study used RC-Seq on an Illumina HiSeq 2000 system for 19 hepatocellular carcinoma (HCC) and colorectal
cancers (MCC). From these data, the authors identified novel L1 insertion events: each individual genome contained on average 244
non-reference L1 insertions. Forty-five non-reference insertions were annotated as tumor-specific and three of these insertions coincided with
strong inhibition of the tumor suppressor MCC. These data provide substantial evidence for L1-mediated retrotransposition playing a role in
HCC development.

108
Solyom S., Ewing A. D., Rahrmann E. P., Doucet T., Nelson H. H., et al. (2012) Extensive somatic L1 retrotransposition in colorectal tumors.
Genome Res 22: 2328-2338
Baillie J. K., Barnett M. W., Upton K. R., Gerhardt D. J., Richmond T. A., et al. (2011) Somatic retrotransposition alters the genetic landscape of
the human brain. Nature 479: 534-537
Associated Kits

109
Transposon
MmeI-
recognition
site
Inverted
MmeI-
recognition
site
20bp
MmeI
MmeI
MmeI digestion Add adapters
20bp
PCR and
sequence
Transposon insertion sites
TRANSPOSON SEQUENCING (TN-SEQ) OR INSERTION SEQUENCING (INSEQ)
Transposon sequencing (Tn-Seq) or insertion sequencing (INSeq) accurately determines quantitative genetic interactions131
. In this method, a
transposon with flanking Mmel digestion sites is transposed into bacteria which, after culturing, can help detect the frequency of mutations within
the transposon. After MmeI digestion and subsequent adapter ligation, PCR amplification and sequencing can provide information about the
transposon insertion sites.
Pros Cons
• Can study mutational frequency of transposons
• Method can be used to deduce fitness of genes
within microorganisms
• Protocol is robust, reproducible, and sensitive
• Limited to bacterial studies
• Errors during PCR amplification can lead to inaccurate
sequence reads
131 van Opijnen T., Bodi K. L. and Camilli A. (2009) Tn-seq: high-throughput parallel sequencing for fitness and genetic interaction studies in microorganisms. Nat Methods 6: 767-772
References
Dong T. G., Ho B. T., Yoder-Himes D. R. and Mekalanos J. J. (2013) Identification of T6SS-dependent effector and immunity proteins by
Tn-seq in Vibrio cholerae. Proc Natl Acad Sci U S A 110: 2623-2628.
T6SS is an important protein for bacterial competition; however, T6SS-dependent effector and immunity proteins have not yet been
determined. In this study, the authors use Tn-Seq to identify these proteins in Vibrio cholerae.
Troy E. B., Lin T., Gao L., Lazinski D. W., Camilli A., et al. (2013) Understanding barriers to Borrelia burgdorferi dissemination during infection
using massively parallel sequencing. Infect Immun 81: 2347-2357
Infection by Borrelia burgdorferi can cause chronic infections of skin, heart, joints, and the central nervous system of infected mammalian
hosts. In this study, the authors characterized the population dynamics of mixed populations of B. burgdorferi during infection in a mouse
model. Using Tn-Seq based on Illumina technology, they mapped the compositions of B. burgdorferi at both the injection site and in distal
tissues. The authors found that the infection site was a population bottleneck that significantly altered the composition of the population;
however, no such bottleneck was observed in colonization of distal tissues.

110
Murray S. M., Panis G., Fumeaux C., Viollier P. H. and Howard M. (2013) Computational and genetic reduction of a cell cycle to its simplest,
primordial components. PLoS Biol 11: e1001749
Sarmiento F., Mrázek J. and Whitman W. B. (2013) Genome-scale analysis of gene function in the hydrogenotrophic methanogenic archaeon
Methanococcus maripaludis. Proc Natl Acad Sci U S A 110: 4726-4731
Skurnik D., Roux D., Cattoir V., Danilchanka O., Lu X., et al. (2013) Enhanced in vivo fitness of carbapenem-resistant oprD mutants of
Pseudomonas aeruginosa revealed through high-throughput sequencing. Proc Natl Acad Sci U S A 110: 20747-20752
Mann B., van Opijnen T., Wang J., Obert C., Wang Y. D., et al. (2012) Control of virulence by small RNAs in Streptococcus pneumoniae. PLoS
Pathog 8: e1002788
Qi X., Daily K., Nguyen K., Wang H., Mayhew D., et al. (2012) Retrotransposon profiling of RNA polymerase III initiation sites.
van Opijnen T. and Camilli A. (2012) A fine scale phenotype-genotype virulence map of a bacterial pathogen. Genome Res 22: 2541-2551
Zomer A., Burghout P., Bootsma H. J., Hermans P. W. and van Hijum S. A. (2012) ESSENTIALS: Software for Rapid Analysis of High
Throughput Transposon Insertion Sequencing Data. PLoS ONE 7: e43012
Goodman A. L., Wu M. and Gordon J. I. (2011) Identifying microbial fitness determinants by insertion sequencing using genome-wide
transposon mutant libraries. Nat Protoc 6: 1969-1980
Associated Kits

111
Infect I-Sel Sonicate
blunt
A-tail
Ligate linkers
Cut I-Scel
Purification
I-SceI site
+AID
-AID AA
AA
Semi-nested PCR
Linker cleavage
DNAGenomic DNA
TRANSLOCATION-CAPTURE SEQUENCING (TC-SEQ)
Translocation-capture sequencing (TC-Seq) is a method developed to study chromosomal rearrangements and translocations132
. In this method,
cells are infected with retrovirus expressing l-Scel sites in cells with and without activation-induced cytidine deaminase (AICDA or AID) protein.
Genomic DNA from cells is sonicated, linker-ligated, purified, and amplified via semi-nested LM-PCR. The linker is then cleaved and the DNA
is sequenced. Any AID-dependent chromosomal rearrangement will be amplified by LM-PCR, while AID-independent translocations will
be discarded.
Pros Cons
• Can study chromosomal translocations within a given model
or environment
• Random sonication generates unique linker ligation points,
and deep sequencing allows reading through
rearrangement breakpoints
132 Klein I. A., Resch W., Jankovic M., Oliveira T., Yamane A., et al. (2011) Translocation-capture sequencing reveals the extent and nature of chromosomal rearrangements in B lymphocytes.
Cell 147: 95-106
References
Jankovic M., Feldhahn N., Oliveira T. Y., Silva I. T., Kieffer-Kwon K. R., et al. (2013) 53BP1 alters the landscape of DNA rearrangements and
suppresses AID-induced B cell lymphoma. Mol Cell 49: 623-631
Programmed DNA rearrangement in lymphocytes is initiated by AID protein. The overexpression of AID is associated with cancer, but
overexpression of AID alone is insufficient to produce malignancy. This study examines the roles of AID and tumor suppressor p53-binding
protein 1 (53BP1) in combination. The results show that the combination of 53BP1 deficiency and AID deregulation increases the rate of
rearrangements and results in B cell lymphoma in a mouse model. The rate of rearrangements and CNVs are studied using the Illumina
Genome Analyzer.

112
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Associated Kits

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131
DNA/RNA PURIFICATION KITS
MasterPure™
Complete DNA and RNA Purification Kit
APPENDIX
www.epicentre.com • epicentral.blogspot.com • Toll-Free U.S. (800) 284-8474 • Fax (608) 258-3089 PDS 010
Table 1. Purify any sample.
Pure Nucleic Acids
MasterPure™ Complete DNA
and RNA Purification kit
„ Extract and purify total nucleic acids (TNA), DNA or RNA
„ Pure for sequencing, qPCR and other molecular biology
applications
„ Scalable reactions
„ High purity and yield
„ Non-Toxic
Workflow
The MasterPure™ Complete Kit purifies high yields of intact
total nucleic acid, DNA, or RNA. MasterPure is suitable for
every type of biological material.
Sample Sample Size TNA µg DNA µg RNA µg
HeLa/HL60 cells 1 X 106
cells 10-30 3-12 7-15
Liver 5 mg 33-42 5-10 13-25
Brain 5 mg 9-13 6-9 4-11
Heart 5 mg 6-10 4-7 4-5
Blood 200 µl 3-10 3-9
Buffy coat 300 µl 40-55 40-55 3-6
E. coli 3.5 x 106
cells 2.5-2.8 1.3-1.6 1.6-1.8
Yeast*
(S. cerevisiae)
2.2 x 106
cells 11-18
1.1 x 107
cells 70-78
Many different, diverse sample types have
been purified by MasterPure. Several are
shown in Table 1. MasterPure is available for
virtually any type of sample.
MasterPure may be used to purify total
nucleic acid, DNA or RNA from any sample.
Total nucleic acid purification permits you
to compare DNA and RNA from the same
sample to gain a deeper understanding of
your sample.
MasterPure is optimized for use with:
„ Illumina® sequencing
„ qPCR
„ PCR
„ Molecular biology applications
NGS
qPCR
Many more
MasterPure™Sample

132
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Figure 2. MasterPure easily purifies many different sample types.
Cat. # Quantity
MasterPure™ Complete DNA and RNA Purification kit
MC85200 200 DNA Purifications
100 RNA Purifications
MC89010 10 DNA Purifications
5 RNA Purifications
MasterPure™ DNA Purification Kit
MCD85201 200 Purifications
Purify any sample
MasterPure has been shown to work for many types of
human tissue and blood samples, plants, and bacteria.
MasterPure is safe and nontoxic. No dangerous chemicals,
phenol or hazards are used in the method. MasterPure is a
wise choice for safety and high yields of RNA, DNA, or total
nucleic acids.
Stop stocking three different kits for small, moderate and
abundant samples! MasterPure is designed to be used with
small, moderate and abundant samples without the need
for many kits. One MasterPure kit permits you to purify RNA,
DNA or total nucleic acid from any amount of sample.
One kit to purify your choice of nucleic acids.
MasterPure has been published showing excellent purification of nearly every type of sample. Here we see just a few of the types of samples that have
been published using MasterPure.
Sputum
Kidney
Brain
Metagenomic
Samples
Semen
Salivia
Plasma
Plants
Heart
Bacteria
Urine
Suitable for Illumina® sequencing
Total nucleic acid, DNA or RNA purified by MasterPure is
suitable for use with Illumina sequencing. All sequencing
applications begin with MasterPure, including:
„ Ribo-Zero
„ RNA-Seq
„ Bisulfite sequencing for epigenetics
„ DNA-Seq
„ Exome capture
„ More...

133
DNA SEQUENCING
DNA-Sequencing
Description Catalog Number
MasterPure™
Complete DNA and RNA Purification Kit MC85200
MasterPure™
DNA Purification Kit MCD85201
TruSeq DNA PCR-Free LT Sample Preparation Kit - Set A FC-121-3001
TruSeq DNA PCR-Free LT Sample Preparation Kit - Set B FC-121-3002
TruSeq DNA PCR-Free HT Sample Preparation Kit FC-121-3003
TruSeq Nano DNA LT Sample Preparation Kit - Set A FC-121-4001
TruSeq Nano DNA LT Sample Preparation Kit - Set B FC-121-4002
TruSeq Nano DNA HT Sample Preparation Kit FC-121-4003
Nextera Rapid Capture Exome (8 rxn x 1 Plex) FC-140-1000
Nextera Rapid Capture Expanded Exome (2 rxn x 12 Plex) FC-140-1004
EpiGnome™
Methyl-Seq Kit EGMK81312
ChIP
TruSeq ChIP Sample Preparation Kit - Set A IP-202-1012
TruSeq ChIP Sample Preparation Kit - Set B IP-202-1024
Methylation Arrays
HumanMethylation450 DNA Analysis BeadChip Kit (24 samples) WG-314-1003

134
TruSeq RNA and DNA Sample Prep Kits
Data Sheet: Illumina®
Sequencing
Highlights
Simple Workflow for RNA and DNA:
Master-mixed reagents and minimal hands-on steps.
Scalable and Cost-Effective Solution:
Optimized formulations and plate-based processing
enables large-scale studies at a lower cost.
Enhanced Multiplex Performance:
Twenty-four adaptor-embedded indexes enable high-
throughput processing and greater application flexibility.
High-Throughput Gene Expression Studies:
Gel-free, automation-friendly RNA sample preparation
for rapid expression profiling.
Introduction
Illumina next-generation sequencing (NGS) technologies continue to
evolve, offering increasingly higher output in less time. Keeping pace
with these developments requires improvements in sample prepara-
tion. To maximize the benefits of NGS and enable delivery of the high-
est data accuracy, Illumina offers the TruSeq RNA and DNA Sample
Preparation Kits (Figure 1).
The TruSeq RNA and DNA Sample Preparation Kits provide a simple,
cost-effective solution for generating libraries from total RNA or genomic
DNA that are compatible with Illumina’s unparalleled sequencing output.
Master-mixed reagents eliminate the majority of pipetting steps and
reduce the amount of clean-up, as compared to previous methods,
minimizing hands-on time. New automation-friendly workflow formats
enable parallel processing of up to 96 samples. This results in economi-
cal, high-throughput RNA or DNA sequencing studies achieved with the
easiest-to-use sample preparation workflow offered by any NGS platform.
Simple and Cost-Effective Solution
Whether processing samples for RNA-Seq, genomic sequencing, or
exome enrichment, the TruSeq kits provide significantly improved library
preparation over previously used methods. New protocols reduce the
number of purification, sample transfer, and pipetting steps. The new
universal, methylated adaptor design incorporates an index sequence at
the initial ligation step for improved workflow efficiency and more robust
multiplex sequencing. For maximum flexibility, the same TruSeq kit can
be used to prepare samples for single-read, paired-end, and multi-
plexed sequencing on all Illumina sequencing instruments.
TruSeq DNA and RNA Sample Prep kits include gel-free protocols
that eliminate the time-intensive gel purification step found in other
methods, making the process more consistent and fully automatable.
The gel-free protocol for TruSeq DNA sample preparation is available
for target enrichment using the TruSeq Exome Enrichment or TruSeq
Custom Enrichment kits.
TruSeq sample preparation makes RNA sequencing for high-through-
put experiments more affordable, enabling gene expression profiling
studies to be performed with NGS at a lower cost than arrays. It also
provides a cost-effective DNA sequencing solution for large-scale
whole-genome resequencing, targeted resequencing, de novo se-
quencing, metagenomics, and methlyation studies.
Enhanced Multiplex Performance
TruSeq kits take advantage of improved multiplexing capabilities to
increase throughput and consistency, without compromising results.
Both the RNA and DNA preparation kits include adapters containing
unique index sequences that are ligated to sample fragments at the
beginning of the library construction process. This allows the samples
to be pooled and then individually identified during downstream
analysis. The result is a more efficient, streamlined workflow that leads
directly into a superior multiplexing solution. There are no additional
PCR steps required for index incorporation, enabling a robust, easy-
to-follow procedure. With 24 unique indexes available, up to 384
samples can be processed in parallel on a single HiSeq 2000 run.
TruSeq RNA Sample Preparation
With TruSeq reagents, researchers can quickly and easily prepare
samples for next-generation sequencing (Figure 2). Improvements in
the RNA to cDNA conversion steps have significantly enhanced the
overall workflow and performance of the assay (Figure 3).
TruSeq™ RNA and DNA Sample Preparation Kits v2
Master-mixed reagents, optimized adapter design, and a flexible workflow provide a simple, cost-
effective method for preparing RNA and DNA samples for scalable next-generation sequencing.
Figure 1: TruSeq Sample Preparation Kits
TruSeq Sample Preparation Kits are available for both genomic DNA and
RNA samples.

135
Sequencing
Starting with total RNA, the messenger RNA is first purified using
polyA selection (Figure 2A), then chemically fragmented and converted
into single-stranded cDNA using random hexamer priming. Next, the
second strand is generated to create double-stranded cDNA (Figure
2B) that is ready for the TruSeq library construction workflow (Figure 4).
Efficiencies gained in the polyA selection process, including reduced
sample transfers, removal of precipitation steps, and combining of
elution and fragmentation into a single step, enable parallel processing
of up to 48 samples in approximately one hour. This represents a 75%
reduction in hands-on time for this portion of library construction. Im-
proving performance, the optimized random hexamer priming strategy
provides the most even coverage across transcripts, while allowing
user-defined adjustments for longer or shorter insert lengths.
Eliminating all column purification and gel selection steps from the
workflow removes the most time-intensive portions, while improving the
assay robustness. It also allows for decreased input levels of RNA—as
low as 100 ng— and maintains single copy per gene sensitivity.
TruSeq DNA Sample Preparation
The TruSeq DNA Sample Preparation Kits are used to prepare DNA
libraries with insert sizes from 300–500 bp for single, paired-end, and
multiplexed sequencing. The protocol supports shearing by either
sonication or nebulization with a low input requirement of 1 ug of DNA.
Sequence-Ready Libraries
Library construction begins with either double-stranded cDNA syn-
thesized from RNA or fragmented gDNA (Figure 4A). Blunt-end DNA
fragments are generated using a combination of fill-in reactions and
exonuclease activity (Figure 4B). An ‘A’- base is then added to the
blunt ends of each strand, preparing them for ligation to the sequenc-
ing adapters (Figures 4C). Each adapter contains a ‘T’-base overhang
on 3’-end providing a complementary overhang for ligating the adapter
50% of pipetting steps eliminated
50% of reagent tubes eliminated
75% of clean-up steps eliminated
50% of sample transfer steps eliminated
Compared to previous kits, processing multiple samples with the
new TruSeq Sample Preparation Kits provides significant reductions
in library construction costs, the number of steps, hands-on time,
and PCR dependency.
Figure 3: TruSeq RNA Sample Preparation Reagents
Provide Significant Savings in Time and Effort
Compared to current methods for preparing mRNA samples for sequencing,
use of the TruSeq reagents significantly reduces the number of steps and
hands-on time.
Figure 2: Optimized TruSeq RNA Sample Preparation
Starting with total RNA, mRNA is polyA-selected and fragmented. It then
undergoes first- and second-strand synthesis to produce products ready
for library construction (Figure 4).
Current
Methods
TruSeq
Methods Savings
No. of Steps 49 18 31
Time (hours) 16 12 25%
Bead cleanup
EtOH cleanup
Column cleanup
mRNA Isolation
22 Steps 10 Steps
Current New
Fragmentation
6 Steps 3 Steps
First Strand Synthesis
13 Steps 3 Steps
Second Strand Synthesis
8 Steps 2 Steps
A. Poly-A selection, fragmentation and random priming
AAAAAAA
TTTTTTT
B. First and second strand synthesis
Table 1: Savings When Processing 96 Samples

136
Sequencing
to the A-tailed fragmented DNA. These newly redesigned adapters
contain the full complement of sequencing primer hybridization sites
for single, paired-end, and multiplexed reads. This eliminates the need
for additional PCR steps to add the index tag and multiplex primer
sites (Figure 4D). Following the denaturation and amplification steps
(Figure 4E), libraries can be pooled with up to 12 samples per lane
(96 sample per flow cell) for cluster generation on either cBot or the
Cluster Station.
Master-mixed reagents and an optimized protocol improve the library
construction workflow, significantly decreasing hands-on time and
reducing the number of clean-up steps when processing samples for
large-scale studies (Table 1). The simple and scalable workflow allows
for high-throughput and automation-friendly solutions, as well as
simultaneous manual processing for up to 96 samples. In addition,
enhanced troubleshooting features are incorporated into each step
of the workflow, with quality control sequences supported by Illumina
RTA software.
Enhanced Quality Controls
Specific Quality Control (QC) sequences, consisting of double-
stranded DNA fragments, are present in each enzymatic reaction of
the TruSeq sample preparation protocol: end repair, A-tailing, and
ligation. During analysis, the QC sequences are recognized by the RTA
software (versions 1.8 and later) and isolated from the sample data.
The presence of these controls indicates that its corresponding step
was successful. If a step was unsuccessful, the control sequences will
be substantially reduced. QC controls assist in comparison between
experiments and greatly facilitate troubleshooting.
Designed For Automation
The TruSeq Sample Preparation Kits are compatible with high-
throughput, automated processing workflows. Sample preparation can
be performed in standard 96-well microplates with master-mixed re-
agent pipetting volumes optimized for liquid-handling robots. Barcodes
on reagents and plates allow end-to-end sample tracking and ensure
that the correct reagents are used for the correct protocol, mitigating
potential tracking errors.
Part of an Integrated Sequencing Solution
Samples processed with the TruSeq Sample Preparation Kits can be
amplified on either the cBot Automated Cluster Generation System
or the Cluster Station and used with any of Illumina’s next-generation
sequencing instruments, including HiSeq™ 2000, HiSeq 1000,
HiScan™SQ, Genome AnalyzerIIx (Figure 5).
Summary
Illumina’s new TruSeq Sample Preparation Kits enable simplic-
ity, convenience, and affordability for library preparation. Enhanced
multiplexing with 24 unique indexes allows efficient high-throughput
processing. The pre-configured reagents, streamlined workflow, and
automation-friendly protocol save researchers time and effort in their
next-generation sequencing pursuits, ultimately leading to faster dis-
covery and publication.
Learn more about Illumina’s next-generation sequencing solutions at
www.illumina.com/sequencing.
Figure 4: Adapter Ligation Results in Sequence-Ready
Constructs without PCR
Library construction begins with either fragmented genomic DNA or double-
stranded cDNA produced from total RNA (Figure 4A). Blunt-end fragments
are created (Figure 4B) and an A-base is then added (Figure 4C) to prepare
for indexed adapter ligation (Figure 4D). Final product is created (Figure 4E),
which is ready for amplification on either the cBot or the Cluster Station.
E. Denature and amplify for final product
Rd1 SPP5 IndexDNA Insert
Rd2 SP’
D. Ligate index adapter
Rd1 SP
P5
P7
Index Rd2 SP
Ai. Fragment genomic DNA
C. A-tailing
P
P
A
A
P
P
B. End repair and phosphorylate
+
P
A
T
P
Rd1 SP
P5 P7
Index
Rd2 SP
Rd1 SP
P5P7
Index
Rd2 SP
P7’
5’
5’
A
P
Aii. Double-stranded cDNA
(from figure 2B)
P
P

137
Sequencing
Illumina, Inc.
FOR RESEARCH USE ONLY
© 2011 Illumina, Inc. All rights reserved.
Illumina, illuminaDx, BeadArray, BeadXpress, cBot, CSPro, DASL, Eco, Genetic Energy, GAIIx, Genome Analyzer, GenomeStudio, GoldenGate,
HiScan, HiSeq, Infinium, iSelect, MiSeq, Nextera, Sentrix, Solexa, TruSeq, VeraCode, the pumpkin orange color, and the Genetic Energy
streaming bases design are trademarks or registered trademarks of Illumina, Inc. All other brands and names contained herein are the property of
their respective owners.
Pub. No. 970-2009-039 Current as of 27 April 2011
Figure 5. Illumina’s Complete Sequencing Solution
Cluster Station cBot
TruSeq Sample Preparation
Genome AnalyzerIIx HiSeq 2000/1000, HiScanSQ
Genome AnalyzerIIx
The TruSeq Sample Preparation Kits readily fit in with Illumina’s advanced
next-generation sequencing solutions.
Ordering Information
Product Catalog No.
For RNA Preparation
TruSeq RNA Sample Preparation Kit v2, Set A
(12 indexes, 48 samples)
RS-122-2001
TruSeq RNA Sample Preparation Kit v2, Set B
RS-122-2002
For DNA Preparation
TruSeq DNA Sample Preparation Kit v2, Set A
FC-121-2001
TruSeq DNA Sample Preparation Kit v2, Set B
FC-121-2002
For Cluster Generation on cBot and Sequencing on the
HiSeq 2000/1000 and HiScanSQ
TruSeq Paired-End Cluster Kit v3—cBot—HS
(1 flow cell)
PE-401-3001
TruSeq Single-Read Cluster Kit v3—cBot—HS
(1 flow cell)
GD-401-3001
Genome AnalyzerIIx
TruSeq Paired-End Cluster Kit v2—cBot—GA
(1 flow cell)
PE-300-2001
TruSeq Single-Read Cluster Kit v2—cBot—GA
(1 flow cell)
GD-300-2001
For Cluster Generation on the Cluster Station and Sequencing
on the Genome AnalyzerIIx
TruSeq Paired-End Cluster Kit v5—CS—GA
(1 flow cell)
PE-203-5001
TruSeq Single-Read Cluster Kit v5—CS—GA
(1 flow cell)
GD-203-5001

138
Data Sheet: Sequencing
Highlights
• Superior Coverage
Elimination of PCR-induced bias and fewer coverage gaps
provide greater access to the genome
• Faster Sample Preparation
PCR-Free protocol accelerates the most widely adopted
sample preparation chemistry
• Unprecedented Flexibility
PCR-Free kits are optimized to support a variety of read
lengths and applications
• Inclusive Solution
Reliable solution includes master-mixed reagents,
size-selection beads, and up to 96 indices for the highest
operational efficiency
Introduction
The TruSeq DNA PCR-Free Sample Preparation Kit offers numerous
enhancements to the industry’s most widely adopted sample
preparation workflow, providing an optimized, all-inclusive sample
preparation for whole-genome sequencing applications. By eliminating
PCR amplification steps, the PCR-Free protocol removes typical
PCR-induced bias and streamlines the proven TruSeq workflow. This
results in excellent data quality and detailed sequence information
for traditionally challenging regions of the genome. Two kit types are
available to accommodate a range of study designs: the TruSeq DNA
PCR-Free LT Sample Preparation Kit for low-throughput studies and
the TruSeq DNA PCR-Free HT Sample Preparation Kit for high-
throughput studies (Figure 1).
Accelerated Sample Preparation
The TruSeq DNA sample preparation workflow has been streamlined
further by removing the PCR step and replacing gel-based size
selection with bead-based selection (Figure 2). This kit offers
unprecendented flexibility with two protocol options for generating
either large (550 bp) or small (350 bp) insert sizes to support a variety
of applications, matching the ever-increasing read lengths of Illumina
sequencing instruments. Master-mixed reagents, provided sample
purification beads, and optimized protocols contribute to the simplified
library construction workflow, requiring minimal hands-on time and
few cleanup steps for processing large sample numbers. TruSeq DNA
PCR-Free sample preparation decreases library preparation time,
empowering applications from microbial sequencing to whole human
genome sequencing.1
Innovative Sample Preparation Chemistry
TruSeq DNA PCR-Free Sample Preparation kits are used to prepare
DNA libraries for single, paired-end, and indexed sequencing. The
protocol supports shearing by Covaris ultrasonication, requiring
1 µg of input DNA for an average insert size of 350 bp or 2 µg
for an average insert size of 550 bp. Library construction begins
wtih fragmented gDNA (Figure 2A). Blunt-end DNA fragments are
generated using a combination of fill-in reactions and exonuclease
activity (Figure 2B), and size selection is performed with provided
sample purification beads (Figure 2C). An A-base is then added to the
blunt ends of each strand, preparing them for ligation to the indexed
adapters (Figure 2D). Each adapter contains a T-base overhang for
ligating the adapter to the A-tailed fragmented DNA. These adapters
for single, paired-end, and indexed reads. With no need for additional
PCR amplification, single or dual-index adapters are ligated to the
fragments and samples are ready for cluster generation (Figure 2E).
Superior Coverage
The TruSeq DNA PCR-Free Sample Preparation Kit optimizes
sequencing data to provide greater insight into the genome, including
coding, regulatory, and intronic regions. PCR-Free sample preparation
generates reduced library bias and gaps (Figure 3). Exceptional data
quality delivers base-pair resolution of somatic and de novo mutations,
supporting accurate identification of causative variants. The removal
of PCR amplification from the TruSeq workflow removes amplification
biases to improve coverage uniformity across the genome (Figure 4).
TruSeq®
DNA PCR-Free Sample Preparation Kit
Setting new standards for unbiased data quality and superior coverage.
Figure 1: TruSeq DNA PCR-Free Sample
Preparation Kit
TruSeq DNA PCR-Free kits are an efficient solution for preparing and
indexing sample libraries. The TruSeq DNA PCR-Free LT kit provides up
to 24 indices for low-throughput studies (with both Sets A and B), while
the TruSeq DNA PCR-Free HT kit includes 96 dual-index combinations for
high-throughput studies.

139
Figure 2: Adapter Ligation Results in
Sequence-Ready Constructs without PCR
*The TruSeq DNA PCR-Free LT indexing solution features a single-index
adapter at this step.
The PCR-Free kit also provides superior coverage of traditionally
challenging genomic content, including GC-rich regions, promoters,
and repetitive regions (Figure 5), allowing researchers to access more
genomic information from each sequencing run (Figure 6).
Efficient Sample Multiplexing
TruSeq DNA PCR-Free Sample Preparation kits provide an innovative
solution for sample multiplexing. Indices are added to sample gDNA
fragments using a simple PCR-Free procedure. For the greatest
operational efficiency, up to 96 pre-plated, uniquely indexed samples
can be pooled and sequenced together in a single flow cell lane
on any Illumina sequencing platform. After sequencing, the indices
are used to demultiplex the data and accurately assign reads to the
proper sample in the pool. The TruSeq DNA PCR-Free LT kit uses a
single index for demultiplexing, while the TruSeq DNA PCR-Free HT kit
employs a dual-indexing strategy, using a unique combination of two
indices to demultiplex.
Figure 3: Fewer Gaps in Coverage
TruSeq DNA PCR-Free libraries show significant reduction in the number
and total size of gaps when compared to libraries prepared using the
TruSeq DNA (with PCR) protocol. A gap is defined as a region ≥ 10 bp in
length, where an accurate genotype cannot be determined due to low
depth, low alignment scores, or low base quality.
Figure 4: Greater Coverage Uniformity
TruSeq DNA PCR-Free libraries provide greater coverage uniformity across
the genome when compared to those generated using the TruSeq
DNA protocol.
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 20 40 60 80
%ReferenceBases
Mapped Depth
TruSeq DNA PCR-Free
TruSeq DNA (with PCR)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Number of Gaps Total Gap Size Average Gap Size
%ImprovementRelativetoTruSeqDNA
(withPCR)
A
P
P
B
DNA InsertRd1 SP
P5
P7
Index 2
Index 1 Rd2 SP
+
P
A
T
P
A
P
E
DNA InsertRd1 SP
P5 P7
Index 2Index 1
Index 1Index 2
Rd2 SP
5’ 5’
P
P
A
A
P
P
D
C
Library construction begins with genomic DNA that is subsequently fragmented.
Fragments are narrowly size selected with sample purification beads.
A-base is added.
Dual-index adapters are ligated to the fragments* and final product is ready
for cluster generation.
Blunt-end fragments are created.

140
The TruSeq LT kit includes up to 24 indices with two sets of 12each,
and the TruSeq HT kit offers 96 indices for efficient experimental
design.
Multi-sample studies can be conveniently managed using the Illumina
Experiment Manager, a freely available software tool that provides
easy reaction setup for plate-based processing. It allows researchers
to quickly configure the index sample sheet (i.e., sample multiplexing
matrix) for the instrument run, enabling automatic demultiplexing.
Flexible and Inclusive Sample Preparation
The TruSeq family of sample preparation solutions offers several kits
for sequencing applications, compatible with a range of research
needs and study designs (Table 1). All TruSeq kits support high- and
low-throughput studies. The TruSeq DNA PCR-Free kit provides
superior coverage quality and drastically reduces library bias and
coverage gaps, without requiring PCR amplification. These kits
enhance the industry’s most widely adopted DNA sample preparation
method, empowering next-generation sequencing applications.
Simplified Solution
The comprehensive solution includes sample preparation reagents,
sample purification beads, and robust TruSeq barcodes for sample
multiplexing, providing a complete preparation method optimized for
the highest performance on all Illumina sequencing platforms. The
TruSeq DNA PCR-Free kit leverages the flexibility of two kit options,
24-sample and 96-sample, for a scalable experimental approach.
With a simplified workflow and multiplexing options, the TruSeq DNA
PCR-Free protocol offers the fastest library preparation method for the
highest data quality.
Figure 5: Increased Coverage of Challenging Regions
When compared to libraries generated by PCR-based workflows, such as TruSeq DNA Sample Preparation, PCR-Free libraries show improved coverage for
challenging regions of the genome. These regions include known human protein coding and non-protein coding exons and genes defined in the RefSeq Genes
track in the UCSC Genome Browser.2
G-Rich regions denote 30 bases with ≥ 80% G. High GC regions are defined as 100 bases with ≥ 75% GC content. Huge GC
regions are defined as 100 bases with ≥ 85% GC content. “Difficult” promoters denote the set of 100 promoter regions that are insufficiently covered, which have
been empirically defined by the Broad Institute of MIT and Harvard.3
AT dinucleotides indicate 30 bases of repeated AT dinucleotide.
Figure 6: PCR-Free Protocol Eliminates Coverage
Gaps in GC-Rich Content
A
B
Increased coverage of TruSeq DNA PCR-Free libraries results in fewer
coverage gaps, demonstrated here in the GC-rich coding regions of the
RNPEPL1 promoter (A) and the CREBBP promoter (B). PCR-Free
sequence information is shown in the top panels of A and B, while
sequence data generated using TruSeq DNA protocol (with PCR) are
shown in the lower panels.
0
Genes Exons G-Rich High GC Huge GC
“Difficult”
Promoters
AT
Dinucleotides
50
100
150
200
250
300
350
450
400
%CoverageImprovementRelativeto
TruSeqDNA(withPCR)
TruSeqDNAPCR-FreeTruSeqDNATruSeqDNAPCR-FreeTruSeqDNA

141
Illumina • 1.800.809.4566 toll-free (U.S.) • +1.858.202.4566 tel • techsupport@illumina.com • www.illumina.com
FoR RESEARCH USE oNLy
Illumina, IlluminaDx, BaseSpace, BeadArray, BeadXpress, cBot, CSPro, DASL, DesignStudio, Eco, GAIIx, Genetic Energy,
Genome Analyzer, GenomeStudio, GoldenGate, HiScan, HiSeq, Infinium, iSelect, MiSeq, Nextera, NuPCR, SeqMonitor, Solexa,
TruSeq, TruSight, VeraCode, the pumpkin orange color, and the Genetic Energy streaming bases design are trademarks or registered
trademarks of Illumina, Inc. All other brands and names contained herein are the property of their respective owners.
Pub. No. 770-2013-001 Current as of 16 May 2013
ordering Information
Product Catalog No.
TruSeq DNA PCR-Free LT Sample Preparation Kit
Set A (24 samples)
FC-121-3001
TruSeq DNA PCR-Free LT Sample Preparation Kit
Set B (24 samples)
FC-121-3002
TruSeq DNA PCR-Free HT Sample Preparation Kit
(96 samples)
FC-121-3003
Summary
The TruSeq DNA PCR-Free Sample Preparation Kit optimizes the
TruSeq workflow to deliver a faster sample preparation method for
any species. The choice between protocol options provides greater
flexibility to support a variety of applications and genomic studies. The
PCR-Free kit also removes PCR-induced bias to facilitate detailed and
accurate insight into the genome. By leveraging a faster workflow and
superior data quality, the TruSeq DNA PCR-Free Sample Preparation
Kit enables researchers to obtain high-quality genomic data, faster.
References
1. Saunders CJ, Miller NA, Soden SE, Dinwiddie DL, Noll A, et al. (2012)
Rapid whole-genome sequencing for genetic disease diagnosis in neonatal
intensive care units. Science Translational Medicine 4(154): 154ra135.
2. genome.ucsc.edu
3. www.broadinstitute.org
Table 1: TruSeq DNA Sample Preparation Kits
Specification TruSeq Nano DNA TruSeq DNA PCR-Free TruSeq DNA
Description
Based upon widely adopted TruSeq
sample prep, with lower input and
improved data quality
Superior genomic coverage
with radically reduced library
bias and gaps
Original TruSeq
next-generation sequencing
sample preparation method
Input quantity 100–200 ng 1–2 μg 1 μg
Includes PCR Yes No Yes
Assay time ~6 hours ~5 hours 1–2 days
Hands-on time ~5 hours ~4 hours ~8 hours
Target insert size 350 bp or 550 bp 350 bp or 550 bp 300 bp
Gel-Free Yes Yes No
Number of samples supported 24 (LT) or 96 (HT) samples 24 (LT) or 96 (HT) samples 48 (LT) or 96 (HT) samples
Supports enrichment No* No* Yes
Size-selection beads Included Included Not included
Applications
Whole-genome sequencing applications, including whole-genome resequencing,
de novo assembly, and metagenomics studies
Sample multiplexing 24 single indices or 96 dual-index combinations
Compatible Illumina sequencers HiSeq®
, HiScanSQTM
, Genome AnalyzerTM
, and MiSeq®
systems
*Nextera Rapid Capture products support a variety of enrichment applications. For more information, visit www.illumina.com/NRC.

142
Highlights
• Low Sample Input
Excellent data quality from as little as 100 ng input empowers
interrogation of samples with limited available DNA
• Excellent Coverage Quality
Significantly reduced library bias and gaps in coverage
provide greater insight into the genome
• Unprecedented Flexibility
Streamlined TruSeq workflow enables library preparation in
less than one day, while supporting a variety of read lengths
• Inclusive Solution
Reliable solution includes master-mixed reagents,
size-selection beads, and up to 96 indices for the highest
operational efficiency
Introduction
By offering a low-input method based on the industry’s most widely
adopted sample preparation workflow, the TruSeq Nano DNA
Sample Preparation Kit enables efficient interrogation of samples
that have limited available DNA. This kit significantly reduces typical
PCR-induced bias and provides detailed sequence information for
traditionally challenging regions of the genome. Two kit types are
available to accommodate a range of study designs: the TruSeq Nano
DNA LT Sample Preparation Kit for low-throughput studies and the
TruSeq Nano DNA HT Sample Preparation Kit for high-throughput
studies (Figure 1).
Low Sample Input
The TruSeq Nano DNA protocol eliminates the typical requirement
for micrograms of DNA, enabling researchers to study samples
with limited available DNA (e.g., tumor samples) and supporting
preservation of samples for use in future or alternate studies. This kit
offers the flexibility of two protocols for generating large (550 bp) or
small (350 bp) insert sizes to support a diverse range of applications.
In addition to accelerating the workflow, simple bead-based size
selection avoids typical sample loss associated with gel-based
selection. TruSeq Nano DNA kits are validated for high-quality genomic
coverage for virtually any whole-genome sequencing application.
Accelerated Sample Preparation
The TruSeq DNA sample preparation workflow has been streamlined
by replacing gel-based size selection with bead-based selection
(Figure 2), enabling researchers to prepare high-quality libraries in less
than a day. Optimized for a variety of read lengths, from 2 × 101 bp to
2 × 151 bp, the TruSeq Nano DNA kit is designed to match the
ever-increasing read lengths of Illumina sequencing instruments.
Master-mixed reagents, provided sample purification beads for
cleanup and size selection, robust TruSeq indices, and optimized
protocols contribute to the simplified workflow, requiring minimal
hands-on time and few cleanup steps for processing large
sample numbers.
Innovative Sample Preparation Chemistry
These kits are used to prepare DNA libraries for single-read,
paired-end, and indexed sequencing. The TruSeq Nano DNA protocol
supports shearing by Covaris ultrasonication, requiring 100 ng
of input DNA for an average insert size of 350 bp or 200 ng DNA
for an average insert size of 550 bp. Library construction begins
with fragmented gDNA (Figure 2A). Blunt-end DNA fragments are
generated using a combination of fill-in reactions and exonuclease
activity (Figure 2B), and size selection is performed with provided
sample purification beads (Figure 2C). An A-base is then added to the
blunt ends of each strand, preparing them for ligation to the indexed
adapters (Figure 2D). Each adapter contains a T-base overhang for
ligating the adapter to the A-tailed fragmented DNA. These adapters
for single, paired-end, and indexed reads. Single- or dual-index
adapters are ligated to the fragments (Figure 2E) and the ligated
products are amplified with reduced-bias PCR (Figure 2F).
TruSeq®
Nano DNA Sample Preparation Kit
A low-input method that delivers a high-confidence, comprehensive view of the genome for
virtually any sequencing application.
Figure 1: TruSeq Nano DNA Sample Preparation Kit
TruSeq Nano DNA Sample Preparation Kits offer a low-input solution for
preparing and indexing sample libraries. The TruSeq Nano DNA LT kit
provides up to 24 indices for low-throughput studies (with both Sets A and
B), while the TruSeq Nano DNA HT kit includes 96 dual-index combinations
for high-throughput studies.

143
Figure 2: TruSeq Nano DNA Workflow
The TruSeq Nano DNA LT indexing solution features a single-index adapter
at Step E.
Figure 3: Fewer Gaps in Coverage
TruSeq Nano DNA libraries show significant reduction in the number and
total size of gaps when compared to libraries prepared using the TruSeq
DNA protocol. A gap is defined as a region ≥ 10 bp in length, where an
accurate genotype cannot be determined due to low depth, low alignment
scores, or low base quality.
Figure 4: Greater Coverage Uniformity
TruSeq Nano DNA libraries provide greater coverage uniformity across
the genome when compared to those generated using the TruSeq
DNA protocol.
A
P
P
B
DNA InsertRd1 SP
P5
P7
Index 2
Index 1 Rd2 SP
+
P
A
T
P
A
P
E
DNA InsertRd1 SP
P5 P7
Index 2Index 1
Index 1Index 2
Rd2 SP
5’ 5’
DNA InsertRd1 SPP5 P7
Index 1Index 2
Rd2 SP5’
5’
F
P
P
A
A
P
P
D
C
Library construction begins with genomic DNA that is subsequently fragmented.
Fragments are narrowly size selected with sample purification beads.
A-base is added.
Dual-index adapters are ligated to the fragments.
Blunt-end fragments are created.
Ligated product is amplified and ready for cluster generation.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Number of Gaps Total Gap Size Average Gap Size
%ImprovementRelativetoTruSeqDNA
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 20 40 60 80
%ReferenceBases
Mapped Depth
TruSeq Nano DNA
TruSeq DNA

144
Excellent Coverage Quality
TruSeq Nano DNA kits reduce the number and average size of typical
PCR-induced gaps in coverage (Figure 3), delivering exceptional data
quality. The enhanced workflow reduces library bias and improves
coverage uniformity across the genome (Figure 4). These kits also
provide excellent coverage of traditionally challenging genomic
content, including GC-rich regions, promoters, and repetitive regions
(Figure 5). High data quality delivers base-pair resolution, providing
a detailed view of somatic and de novo mutations and supporting
accurate identification of causative variants. TruSeq Nano DNA kits
provide a comprehensive view of the genome, including coding,
regulatory, and intronic regions, enabling researchers to access more
information from each sequencing run (Figure 6).
Flexible and Inclusive Sample Preparation
The TruSeq family of sample preparation solutions offers several kits
for sequencing applications, compatible with a range of research
needs and study designs (Table 1). All TruSeq kits support high-
and low-throughput studies. The TruSeq Nano DNA kit supports
whole-genome sequencing and is ideal for sequencing applications
that require sparsely available DNA. These kits provide numerous
enhancements to the industry’s most widely adopted DNA sample
preparation method, empowering all sequencing applications.
Table 1: TruSeq DNA Sample Preparation Kits
Specification TruSeq Nano DNA TruSeq DNA PCR-Free TruSeq DNA
Description
Based upon widely adopted TruSeq
sample prep, with lower input and
improved data quality
Superior genomic coverage
with radically reduced library
bias and gaps
Original TruSeq
next-generation sequencing
sample preparation method
Input quantity 100–200 ng 1–2 μg 1 μg
Includes PCR Yes No Yes
Assay time ~6 hours ~5 hours 1–2 days
Hands-on time ~5 hours ~4 hours ~8 hours
Target insert size 350 bp or 550 bp 350 bp or 550 bp 300 bp
Gel-Free Yes Yes No
Number of samples supported 24 (LT) or 96 (HT) samples 24 (LT) or 96 (HT) samples 48 (LT) or 96 (HT) samples
Supports enrichment No* No* Yes
Size-selection beads Included Included Not included
Applications
Whole-genome sequencing applications, including whole-genome resequencing,
de novo assembly, and metagenomics studies
Sample multiplexing 24 single indices or 96 dual-index combinations
Compatible Illumina sequencers HiSeq®
, HiScanSQTM
, Genome AnalyzerTM
, and MiSeq®
systems
*Nextera Rapid Capture products support a variety of enrichment applications. For more information, visit www.illumina.com/NRC.
Figure 5: Increased Coverage of Challenging Regions
TruSeq Nano DNA libraries demonstrate improved coverage of challenging
genomic content. These regions include known human protein coding
and non-protein coding exons and genes defined in the RefSeq Genes
track in the UCSC Genome Browser.1
G-Rich regions denote 30 bases
with ≥ 80% G. High GC regions are defined as 100 bases with ≥ 75%
GC content. Huge GC regions are defined as 100 bases with ≥ 85% GC
content. “Difficult” promoters denote the set of 100 promoter regions
that are insufficiently covered, which have been empirically defined by the
Broad Institute of MIT and Harvard.2
AT dinucleotides indicate 30 bases of
repeated AT dinucleotide.
0
Genes Exons G-Rich High GC Huge GC
“Difficult”
Promoters
AT
Dinucleotides
50
100
150
200
250
300
350
400
%CoverageImprovement
RelativetoTruSeqDNA

145
FoR RESEARCH USE oNLy
Pub. No. 770-2013-012 Current as of 16 May 2013
Efficient Sample Multiplexing
Using a simple procedure, indices are added to sample genomic DNA
fragments to provide an innovative solution for sample multiplexing.
For the greatest operational efficiency, up to 96 pre-plated, uniquely
indexed samples can be pooled and sequenced together in a single
flow cell lane on any Illumina sequencing platform. After sequencing,
the indices are used to demultiplex the data and accurately assign
reads to the proper samples in the pool.
The TruSeq LT kit uses a single index for demultiplexing, while the
TruSeq HT kit employs a dual-indexing strategy, using a unique
combination of two indices to demultiplex. The LT kit includes up to 24
indices with two sets of 12 each, and the HT kit offers 96 indices.
Streamlined Solution
This inclusive kit contains sample preparation reagents, sample
purification beads, and robust TruSeq indices for multiplexing,
providing a complete preparation method optimized for the highest
performance on all Illumina sequencing platforms. The TruSeq
Nano DNA kit leverages the flexibility of two kit options, 24-sample
and 96-sample, for scalable experimental design. With a simplified
workflow and flexible multiplexing options, the TruSeq Nano DNA
protocol offers a streamlined library preparation method that delivers
high-quality sequencing data.
Summary
The TruSeq Nano DNA Sample Preparation Kit optimizes the TruSeq
workflow to deliver a low-input sample preparation method for any
sequencing application. Low- and high-throughput options and varied
insert sizes provide greater flexibility to support a variety of applications
and genomic studies. Workflow innovations reduce PCR-induced
bias to facilitate detailed and accurate insight into the genome. By
leveraging a faster workflow and enhanced data quality, the TruSeq
Nano DNA Sample Preparation Kit provides an all-inclusive sample
preparation method for genome sequencing applications.
References
1. genome.ucsc.edu
2. www.broadinstitute.org
Product Catalog No.
TruSeq Nano DNA LT Sample Preparation Kit
Set A (24 samples)
FC-121-4001
TruSeq Nano DNA LT Sample Preparation Kit
Set B (24 samples)
FC-121-4002
TruSeq Nano DNA HT Sample Preparation Kit
(96 samples)
FC-121-4003
Figure 6: TruSeq Nano DNA Protocol Reduces
Number of Coverage Gaps
A
B
Increased coverage of TruSeq Nano DNA libraries results in fewer coverage
gaps, demonstrated here in the GC-rich coding regions of the RNPEPLI1
promoter (A) and the ZBTB34 promoter (B). Sequence information
generated by TruSeq Nano DNA prep is shown in the top panels of A and
B, while sequence data generated using TruSeq DNA protocol are shown
in the lower panels.
TruSeqNanoDNATruSeqDNATruSeqNanoDNATruSeqDNA

146
Data Sheet: DNA Sequencing
Highlights
• Fastest Time to Results
Go from DNA to data in less than 8 hours with MiSeq®
System
• Easiest to Use
Prepare sequencing-ready samples in 1.5 hours
with 15 minutes hands-on time
• Lowest DNA Input
Use just 50 ng DNA per sample, enabling use
with samples in limited supply
• Highest Throughput
Index up to 96 samples and use master-mixed
reagents to process 500 samples per week
DNA to Data in Record Time
Nextera DNA Sample Preparation Kits provide the fastest and easiest
workflow, enabling sequencing-ready libraries to be generated in less
than 90 minutes, with less than 15 minutes of hands-on time. DNA is
simultaneously fragmented and tagged with sequencing adapters in
a single step, using standard lab equipment. Libraries prepared with
Nextera kits are compatible with Illumina sequencers (Table 1).
Breakthrough Chemistry
Nextera technology employs a single “tagmentation” reaction to
simultaneously fragment and tag DNA with adapters (Figure 2).
This process occurs in a single step using master-mixed reagents to
provide PCR-ready templates in as little as 15 minutes. Sequencing
adapters and indexes are then added to the gDNA fragment by PCR.
The optimized Nextera PCR protocol leads to improved performance
with GC regions. From start to finish, the complete Nextera sample
preparation protocol is over 80% faster than any other method available.
Improved Multiplexing
Nextera DNA Sample Preparation Kits feature an innovative indexing
solution for processing and uniquely barcoding up to 96 samples.
Multisample studies can be conveniently managed using the Illumina
Experiment Manager, a freely available software tool that provides
easy reaction setup for plate-based processing.
Following the addition of two indexes to each gDNA fragment, up to
96 uniquely indexed samples can be pooled and sequenced together
in a single lane on an Illumina sequencer. After sequencing, the unique
combination of the two indexes is used to demultiplex the data and
assign reads to the proper sample in the pool. Using this dual barcode
approach, Nextera Index Kits only require 20 unique index oligos to
process up to 96 samples, providing an easily scalable approach for
sample indexing.
Nextera®
DNA Sample Preparation Kits
Sequencing’s fastest and easiest sample preparation workflow, delivering libraries in 90 minutes.
Table 1: Nextera DNA Sample Prep Specifications
Specification Value
Input DNA 50 ng
Available indexes Up to 96
Compatible
sequencers
HiSeq®
NextSeqTM
, MiSeq, Genome
Analyzer IIx, and HiScanSQ Systems
Read lengths
supported
Supports all read lengths on any
Illumina sequencing system
Typical median
insert size
~250 bp
Sample DNA
input type
Genomic DNA and PCR amplicons
Figure 1: Nextera DNA Sample Preparation Kit
The Nextera DNA Sample Preparation Kit (96 samples) provides a fast and
easy sample preparation workflow, delivering libraries in 90 minutes.

147
Accelerated Applications
Nextera DNA Sample Preparation Kits are ideal for experiments where
speed and ease are paramount. The low 50 ng DNA input also makes
this method amenable to precious samples available in limited quantity.
This sample preparation workflow can shorten the overall sequenc-
ing workflow time for a wide variety of established applications1-7
and
can be automated for even greater throughput. The combination of
the MiSeq System and Nextera DNA Sample Preparation Kits provide
rapid DNA to data in as little as 8 hours. These kits enable rapid
applications such as small genome and amplicon sequencing, as well
as large genome sequencing on any Illumina platform (Table 2).
Summary
The Nextera DNA Sample Preparation Kit provides sequencing’s
fastest and easiest sample preparation workflow, delivering completed
libraries in 90 minutes that are compatible with all Illumina sequencing
systems. Nextera enables high‐throughput studies with a built‐in
solution for indexing up to 96 samples with ultra low DNA input.
Combined with the MiSeq System, Nextera DNA Sample Preparation
Kits enable the fastest DNA to data—all in a single day.
References
1. Ramirez MS, Adams MD, Bonomo RA, Centrón D, et al. (2011) Genomic
analysis of Acinetobacter baumannii A118 by comparison of optical maps:
Identification of structures related to its susceptibility phenotype.
Antimicrob Agents Chemother, 55(4): 1520–6.
2. Adey A, Morrison HG, Asan, Xun X, Kitzman JO, et al. (2010) Rapid,
low‐input, low‐bias construction of shotgun fragment libraries by
high‐density in vitro transposition. Genome Biol 11: R119.
3. Bimber BN, Dudley DM, Lauck M, Becker EA, Chin EN, et al. (2010)
Whole‐genome characterization of human and simian immunodeficiency
virus intrahost diversity by ultradeep pyrosequencing. J Virol 84: 12087–92.
4. Kitzman JO, Mackenzie AP, Adey A, Hiatt JB, Patwardhan RP, et al. (2010)
Haplotype‐resolved genome sequencing of a Gujarati Indian individual.
Nat Biotechnol 29: 59–63.
5. Linnarsson, S. (2010) Recent advances in DNA sequencing methods ‐
General principles of sample preparation. Exp Cell Res 316: 1339–43.
6. Sudmant PH, Kitzman JO, Antonacci F, Alkan C, Malig M, et al. (2010)
Diversity of human copy number variation and multicopy genes.
Science 330: 641–646.
7. Voelkerding KV, Dames S, and JD Durtschi (2010) Next generation
sequencing for clinical diagnostics‐Principles and application to targeted
resequencing for hypertrophic cardiomyopathy. J Mol Diagn 12: 539–551.
Figure 2: Nextera Sample Preparation Biochemistry
Sequencing-Ready Fragment
Tagmentation
PCR Amplification
Transposomes
Genomic DNA
~ 300 bp
~ 300 bp
P5
P7
Index 1
Index 2
Read 1 Sequencing Primer
p5 Index1 Rd1 SP p7Index2Rd2 SP
Nextera chemistry simultaneously fragments and tags DNA in a single step.
A simple PCR amplification then appends sequencing adapters and sample
indexes to each fragment.
Product Catalog No.
Nextera DNA Sample Preparation Kit
(96 samples)
FC-121-1031
Nextera DNA Sample Preparation Kit
(24 samples)
FC-121-1030
Nextera Index Kit (96 indexes, 384 samples) FC-121-1012
Nextera Index Kit (24 indexes, 96 samples) FC-121-1011
TruSeq Dual Index Sequencing Primer Kit,
Single Read (single-use kit)
FC-121-1003
Paired-End Read (single-use kit)
PE-121-1003
Table 2: Representative Nextera Applications
Examples of Nextera Applications
Large-genome resequencing
Small-genome resequencing
Amplicon resequencing
Clone or plasmid sequencing
Illumina, Inc. • 1.800.809.4566 toll-free (U.S.) • +1.858.202.4566 tel • techsupport@illumina.com • www.illumina.com
© 2011–2014 Illumina, Inc. All rights reserved.
Illumina, Genome Analyzer, HiSeq, MiSeq, Nextera, NextSeq, the pumpkin orange color, and the Genetic Energy streaming bases
design are trademarks of Illumina, Inc. in the U.S. and/or other countries. All other names, logos, and other trademarks are the
property of their respective owners.
Pub. No. 770-2011-021 Current as of 11 March 2014

148
Highlights
• Rapid Sample Preparation
Complete sample prep in as little as 90 minutes with only 15
minutes of hands-on time
• Fastest Time to Results
Go from DNA to data in 8 hours with the MiSeq®
System
• Optimized for Small Genomes, PCR Amplicons,
and Plasmids
One sample prep kit for many applications
• Innovative Sample Normalization
Eliminates the need for library quantification before sample
pooling and sequencing
Introduction
The Nextera XT DNA Sample Preparation Kit enables researchers
to prepare sequencing-ready libraries for small genomes (bacteria,
archaea, and viruses), PCR amplicons, and plasmids in 90 minutes,
with only 15 minutes of hands-on time. The combination of the
MiSeq System and Nextera XT DNA Sample Preparation Kits
enable you to go from DNA to data in 8 hours (Figure 1). The
low amount (1 ng) of input DNA makes this method amenable to
precious samples available in limited quantity. Compatible with all
Illumina sequencers, Nextera sample preparation can shorten the
overall sequencing workflow time for a wide variety of established
applications1-9
and can be automated easily for greater throughput.
Fastest and Easiest Sample Prep Workflow
Using a single “tagmentation” enzymatic reaction, sample DNA is
simultaneously fragmented and tagged with adapters. An optimized,
limited-cycle PCR protocol amplifies tagged DNA and adds
sequencing indexes (Figure 1). From start to finish, the complete
Nextera XT protocol is over 80% faster than other available sample
preparation methods, and requires the least amount of hands-on time.
Innovative Sample Normalization
Sample preparation kits for next-generation sequencing result in
libraries of varying concentration. To pool samples equally and achieve
target cluster densities, time-intensive quantitation methods are often
used, followed by dilution and pooling of barcoded samples. The
Nextera XT DNA Sample Preparation Kit eliminates the need for library
quantification before sample pooling and sequencing by employing
a simple bead-based sample normalization step (Figure 2). Prepared
libraries are produced at equivalent concentrations enabling pooling by
volume—simply pool 5 μl of each library to be sequenced.
Flexible Multiplexing
The Nextera XT Sample Preparation Kit features an innovative indexing
solution for processing and uniquely barcoding up to 384 samples in a
single experiment. Following the addition of two indexes to each DNA
fragment, up to 384 uniquely indexed samples can be pooled and
sequenced together. After sequencing, the unique combination of the
two indexes is used to demultiplex the data and assign reads to the
proper sample. Using this dual-barcode approach, Nextera XT Index
Kits only require 40 unique index oligos to process up to 384 samples
Nextera®
XT DNA Sample Preparation Kit
The fastest and easiest sample prep workflow for small genomes, PCR amplicons, and plasmids.
Figure 1: Nextera XT Sample Preparation Workflow
Prepare Input DNA (1 ng)
Forensic PCR Amplicons,
Small Genomes, Plasmids
Nextera XT Sample Prep Automated Sequencing
and Allele Calling
Nextera Tagmentation Sequencing and Analysis
The combination of Nextera XT and rapid sequencing with the MiSeq System provides a complete DNA to data workflow in only 8 hours.

149
for a scalable approach. Multisample studies can be conveniently
managed using the Illumina Experiment Manager, a freely available
software tool that provides easy reaction setup for plate-based
processing.
Simple User Interface for Analysis
MiSeq Reporter provides automated on-instrument analysis for various
applications including small genome de novo or resequencing, PCR
amplicon, and plasmid sequencing. Sequencing results and analysis
are easy to view and interpret. For example, using the PCR Amplicon
workflow in the MiSeq Reporter software, sequence data are
automatically categorized into intuitive tabs: Samples, Regions,
and Variants (Figure 3). Within each of these tabs, the variant score,
quality (Q) score, and sequencing coverage levels can be determined
down to single bases, allowing easy analysis of variants of interest.
High Coverage, Accurate Calls
To illustrate the power of amplicon sequencing with Nextera XT
and the MiSeq System, nine PCR amplicons of varying sizes were
prepared from two different samples of human DNA. Amplicons from
each sample were pooled and 1 ng of DNA from each pool was
prepared using the Nextera XT kit. Libraries from the two sample
pools were combined, sequenced with paired-end 2 × 150 reads
on the MiSeq System, and analyzed with MiSeq Reporter using
the PCR Amplicon workflow. The approximate mean sequencing
coverage values per amplicon and number of variants called (variant
score 99) identified within the amplicons in one of the two samples
are shown in Table 1. The output of the MiSeq System supported
sequencing of these amplicons to a depth of 12,000×, enabling
Figure 3: PCR Amplicon Workflow in MiSeq Reporter
MiSeq Reporter provides automated on-instrument analysis for various applications, including PCR amplicon shown here. Samples, regions, and variants
are easily accessible, and variant scores, quality (Q) scores, and coverage plots are shown at single nucleotide resolution.
Figure 2: Innovative Sample Normalization
Libraries with varying concentrations
Bead-based normalization: Bind, Wash, Elute
Ready for sequencing
Libraries with equal concentrations
The Nextera XT Sample Preparation kit eliminates the need for library
quantification before sample pooling and sequencing. Libraries of
equivalent concentrations are created by employing bead-based
sample normalization, as simple as pipetting 5 μl of each library
to be sequenced.

150
Table 2: De Novo Assembly of E. coli
Parameter Value
Percent of genome covered 98%
Number of contigs 314
Maximum contig length 221,108
Base count 4,548,900
N50 111,546
Average coverage per base 184.9
Table 1: Amplicon Coverage and Variants Called
Amplicon
Length (bp)
Mean Coverage
(thousands of reads)
Variants Called
(SNVs/Indels)
953 15.1 4 SNVs
1083 27.4 4 SNVs
1099 22.1 1 SNV
1800 22.4 7 SNVs
1809 17.8 1 SNV
2166 17.6 7 SNVs
3064 12.5 4 SNVs
3064 13.3 1 SNV
3072 14.8 K 1 SNV + 1 indel
Figure 4: Coverage of Large Amplicons
Panel A: High sequencing coverage (1,000×) across a 5.1 kb amplicon
Panel B: Within the same amplicon, the position of 16 variants passing filter
(14 SNVs in blue + 2 indels in red) is shown, plotted against variant score
(a Phred-scaled measure of variant calling accuracy, maximum = 99).
Of the 16 variants, 13 are present in dbSNP.
confident variant calling. Of the 31 total variants called in this example,
94% are confirmed within the dbSNP database. These results show
that coverage is high and even across a range of amplicon sizes,
and that variant calls are accurate.
Even Coverage Across Large Amplicons
Large amplicons ( 1 kb) produced by long-range PCR can be easily
prepared with the Nextera XT kit and sequenced on any Illumina
sequencer. In Figure 4, coverage along amplicon length and position of
called variants is shown for a single 5.1 kb amplicon in a highly variable
non-coding region of the human genome. The 5.1 kb amplicon was
part of a pool of 24 amplicons from human DNA ranging in size from
~300 bp up to 10 kb. Amplicon pools were generated from
five different samples, and Nextera XT libraries were made using
1 ng of DNA from each pool. Libraries were combined and single-read
sequencing was performed using 1 × 150 bp cycles on MiSeq and
analyzed using MiSeq Reporter with the PCR Amplicon workflow.
De Novo Assembly of Small Genomes
To show the utility of Nextera XT for preparing microbial genomes,
1 ng of genomic DNA from Escherichia coli reference strain MG1655
was prepared using the Nextera XT kit and sequenced using
paired-end 2 × 150 bp reads on the MiSeq System. The data were
analyzed using the Assembly workflow on the MiSeq Reporter. Total
post-run analysis time for this sample was 28 minutes. Assembly
metrics are shown in Table 2. A high-quality assembly was produced,
with excellent N50 scores and coverage. This data set is available for
analysis in BaseSpace®
, the Illumina cloud computing environment10
.
A
B
0
25
50
75
100
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
VariantQualityScore
Position along Amplicon
0
500
1000
1500
2000
2500
3000
0 1000 2000 3000 4000 5000
FoldCoverage
Position along Amplicon

151
Illumina, BaseSpace, HiSeq, MiSeq, Nextera, NextSeq, TruSeq, the pumpkin orange color, and the Genetic Energy streaming bases
design are trademarks of Illumina, Inc. in the U.S. and/or other countries. All other names, logos, and other trademarks are the
property of their respective owners.
Product Catalog No.
Nextera XT DNA Sample Preparation Kit
(24 samples)
FC-131-1024
Nextera XT DNA Sample Preparation Kit
(96 samples)
FC-131-1096
Nextera XT Index Kit
FC-131-1001
Nextera XT Index Kit
FC-131-1002
TruSeq®
Dual Index Sequencing Primer Kit,
Single Read (single-use kit)*
FC-121-1003
Paired-End Read (single-use kit)*
PE-121-1003
Nextera XT Index Kit v2, Set A
FC-131-2001
Nextera XT Index Kit v2, Set B
FC-131-2002
Nextera XT Index Kit v2, Set C
FC-131-2003
Nextera XT Index Kit v2, Set D
FC-131-2004
*Sequencing primer kits are required for all sequencers except the MiSeq System.
Summary
Nextera XT DNA Sample Preparation Kits are ideal for experiments
where speed and ease are of paramount importance. Providing the
fastest and easiest sample preparation workflow, the Nextera XT DNA
Sample Preparation Kit enables rapid sequencing of small genomes,
PCR amplicons, and plasmids. Combined with the MiSeq and
NextSeqTM
Systems, Nextera XT DNA Sample Preparation Kits enable
you to go from DNA to data—all in a single day.
References
1. Loman N, Misra RJ, Dallman TJ, Constantinidou C, Gharbia SE, et al.
(2012) Performance comparison of benchtop high-throughput sequencing
platforms. Nat Biotechnol 22 Apr.
2. Gertz J, Varley KE, Davis NS, Baas BJ, Goryshin IY, et al. (2012)
Transposase mediated construction of RNA-Seq libraries.
Genome Res 22(1): 134–41.
3. Parkinson NJ, Maslau S, Ferneyhough B, Zhang G, Gregory L, et al. (2012)
Preparation of high-quality next-generation sequencing libraries from
picogram quantities of target DNA. Genome Res 22(1): 125–33.
4. Toprak E, Veres A, Michel J-B, Chait R, Hartl D, et al. (2012) Evolutionary
paths to antibiotic resistance under dynamically sustained drug selection.
Nat Genet 1(44): 101–106.
5. Raychaudhuri S, Iartchouk O, Chin K, Tan PL, Tai AK, et al. (2011)
A rare penetrant mutation in CFH confers high risk of age-related macular
degeneration. Nat Genet 43(12): 1176–7.
6. Depledge DP, Palser AL, Watson SJ, Lai I Y-C, Gray E, et al. (2011)
Specific capture and whole-genome sequencing of viruses from clinical
samples. PLoS One 6(11): e27805.
7. Lieberman TD, Michel J-B , Aingaran M, Potter-Bynoe G, Roux D, et al.
(2011) Parallel bacterial evolution within multiple patients identifies candidate
pathogenicity genes. Nat Genet 43(12): 1275–80.
8. Young TS, Walsh CT (2011) Identification of the thiazolyl peptide GE37468
gene cluster from Streptomyces ATCC 55365 and heterologous expression
in Streptomyces lividans. Proc Natl Acad Sci USA 108(32): 13053–8.
9. Adey A, Morrison HG, Asan, Xun X, Kitzman JO, Turner EH, et al. (2010)
Rapid, low-input, low-bias construction of shotgun fragment libraries by
high-density in vitro transposition. Genome Biol 2010;11(12):R119.
10. http://basespace.illumina.com
Nextera XT DNA Sample Prep Kit Specifications
Specification Value
Sample DNA
input type
Genomic DNA, PCR amplicons, plasmids
Input DNA 1 ng
Typical median
insert size
300 bp
Available
indexes
Up to 384
Compatible
sequencers
MiSeq, NextSeq, and HiSeq®
Systems
Read lengths
supported
Supports all read lengths on any
Illumina sequencing system

152
Nextera Mate Pair Sample Prep Kit
Nextera®
Mate Pair Sample Preparation Kit
An optimized sample preparation method for long-insert libraries, empowering de novo
sequencing and structural variant detection.
Highlights
• Fast and Simple Mate Pair Preparation
A simple tagmentation reaction and low DNA input enable
library preparation in less than 2 days
• Dual Protocol Flexibility
Gel-free and gel-plus protocols enable a range
of applications, including de novo assembly and structural
variation detection
• High Data Quality
Highly diverse libraries maximize data yield
• End-to-End Mate Pair Solution
Conveniently bundled kit includes reagents and indexes for
efficient mate pair preparation
Introduction
Mate pair library preparation generates long-insert paired-end libraries
for sequencing. The Nextera Mate Pair Sample Preparation Kit offers
two methods, gel-free and gel-plus, to support various applications
and input requirements. The robust, low-input, gel-free protocol
yields high-diversity libraries that enable deeper sequencing. The
size-selection step in the gel-plus protocol generates fragments with
a narrow size distribution for structural variation detection. Libraries
prepared with the gel-plus protocol also provide sequence information
for larger repeat regions, empowering de novo genome assembly.
Simplified Mate Pair Workflow
The Nextera Mate Pair protocol provides a simple mate pair workflow
for preparing sequencing-ready libraries in less than 2 days (Figure 1).
Master-mixed TruSeq®
DNA Sample Preparation reagents minimize the
number of assay steps, reducing hands-on time to as little as 3 hours.
The Nextera “tagmentation” reaction utilizes a specially engineered
transposome, the Mate Pair Tagment Enzyme, to simultaneously
fragment and tag the DNA sample. This simplified method only
biotinylates DNA molecules at the sites of fragmentation, avoiding
troublesome internal biotinylation.
Dual Protocol Flexibility
The flexibility of the Nextera Mate Pair Sample Preparation Kit stems
from the availability of two different size-selection options (Table 1).
The gel-free protocol, which requires only 1 μg DNA, provides highly
diverse mate pair libraries with a broad range of fragment sizes
(Figure 2A). This protocol is ideal for routine de novo assembly of small
bacterial genomes, or for the robust generation of mate pair data
for samples with limited DNA. The gel-free protocol offers a faster,
simplified option with a lower DNA input requirement to streamline
mate pair studies.
The gel-plus protocol, which requires 4 μg DNA and standard agarose
gels or Sage Pippin Prep gels1
, offers a more stringent size selection
process. The gel-plus protocol produces libraries with narrower size
distributions to facilitate structural variation detection (Figure 2B and
Figure 3). However, creating gel-plus libraries becomes more difficult
as the fragment lengths increase. Greater control over fragment sizes
is ideal for more challenging mate pair applications, such as de novo
assembly of complex genomes and structural variation detection.
Figure 1: Nextera Mate Pair Workflow
The Nextera Mate Pair Sample Preparation Kit has a simple workflow
that enables library preparation in less than 2 days. It supports a range
of fragment sizes ~2–12 kb in length, though 2–5 kb fragments are
observed at higher frequencies.
BB
B
B
B
B
B
B
B
B
B
B
B
B B
B
B
B
B
B
+
BB
Genomic DNA (blue) is tagmented with a Mate Pair Tagment Enzyme, which attaches
a biotinylated junction adapter (green) to both ends of the tagmented molecule.
The tagmented DNA molecules are then circularized and the ends of the genomic
fragment are linked by two copies of the biotin junction adapter.
Circularized molecules are then fragmented again, yielding smaller fragments.
Sub-fragments containing the original junction are enriched via the biotin tag (B)
in the junction adapter.
After End Repair and A-Tailing, TruSeq DNA adapters (gray and purple) are
then added, enabling amplification and sequencing.

153
Highly Diverse Libraries
The Nextera tagmentation reaction drives the creation of highly diverse
libraries (Table 2) that are compatible with all Illumina sequencing
systems. Library diversity is defined as the number of unique
fragments in a given library. The Nextera Mate Pair protocol allows for
the creation of millions of unique fragments. Such high library diversity
generates fewer duplicate reads and yields larger volumes of data.
The Nextera Mate Pair Sample Preparation Kit also provides
identifiable junction sequences that mark fragment ends, drastically
simplifying data analysis. The presence of searchable junction
sequences allows for accurate fragment identification and enables
sequencing of longer read lengths, as mate pair junctions can be
precisely identified and trimmed accordingly.
Mate Pair Preparation Solution
In addition to Nextera Mate Pair reagents, the comprehensive Nextera
kit contains TruSeq DNA sample preparation reagents and indexes.
TruSeq on-bead reactions follow the tagmentation and circularization
steps (Figure 1), simplifying the purification workflow and reducing
sample loss. This integrated solution streamlines the sample
preparation workflow, maximizing sequencing efficiency with more
samples per lane and enabling rapid multiplexed sequencing of small
genomes. The Nextera Mate Pair Sample Preparation Kit is compatible
with TruSeq DNA Sample Preparation adapter indexing, supporting
12 indexes per kit for a scalable experimental approach. With all
necessary reagents included in one convenient, cost-effective bundle,
the Nextera Mate Pair Sample Preparation Kit is an all-in-one solution
for fast and simple mate pair library preparation.
Figure 2: Fragment Size Distribution with Dual Protocols
Panel A shows the fragment size distribution of an E. coli mate pair library prepared using the Nextera Mate Pair gel-free protocol, resulting in a broad fragment size
distribution. Panel B shows the narrow fragment size distribution of an E. coli mate pair library generated with the Nextera Mate Pair gel-plus protocol with
automated size selection using the Pippin Prep platform.
Figure 3: Fragment Size Distribution
This figure shows fragment size distributions of three E. coli mate pair
libraries (3 kb, 5 kb, and 8 kb) created from the same tagmentation reaction.
These distributions were generated following the Nextera Mate Pair gel-plus
protocol with agarose gel size selection. Though 8 kb fragments are
possible with this protocol, 2–5 kb fragments generate libraries with the
highest yield and diversity.
0
0.2
0.4
0.6
0.8
1
1.2
0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 10,000 11,000
FrequencyFrequency
Fragment Size (bp)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
3 kb 5 kb 8 kb
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0 1,000 3,000 5,000 7,000 9,000 11,000 13,000 15,000
0 2,000 4,000 6,000 8,000 10,000 12,000
Fragment Size (bp)
A B
Fragment Size (bp)
0
0.2
0.4
0.6
0.8
1
1.2
0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000
FrequencyFrequency
Fragment Size (bp)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
3 kb 5 kb 8 kb
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0 1,000 3,000 5,000 7,000 9,000 11,000 13,000 15,000
0 2,000 4,000 6,000 8,000 10,000 12,000
Fragment Size (bp)
A B
Fragment Size (bp)

154
Summary
With a fast and easy workflow, the Nextera Mate Pair Sample
Preparation Kit allows the construction of high-quality sequencing
libraries in less than 2 days. The gel-free and gel-plus options
provide flexibility for various applications. Transposome-mediated
tagmentation, identifiable junction sequences, and indexing capability
make the Nextera Mate Pair Sample Preparation Kit a simple and easy
solution for mate pair applications.
References
1. www.sagescience.com/products/pippin-prep
2. Lander ES, Waterman MS (1988) Genomic mapping by fingerprinting
random clones: a mathematical analysis. Genomics 2: 231–9.
Illumina, Nextera, TruSeq, the pumpkin orange color, and the Genetic Energy streaming bases design are trademarks of Illumina, Inc.
in the U.S. and/or other countries. All other names, logos, and other trademarks are the property of their respective owners.
Product Catalog No.
Nextera Mate Pair Sample Preparation Kit FC-132-1001
This kit contains Nextera Mate Pair reagents and TruSeq reagents and indexes.
Table 1: Nextera Mate Pair Protocols
Protocol
DNA
Input
Number
of
Samples
Size
Selections
Per Sample
Number of
Libraries
Gel-Free 1 μg 48 N/A 48
Gel-Plus with
Pippin Prep
size selection
4 μg 12 1 12
Gel-Plus
with agarose
size selection
4 μg 12 Up to 4 Up to 48
Table 2: Nextera Mate Pair Library Diversity*
Preparation Input DNA Fragment Size Diversity†
Nextera
Mate Pair
Gel-Free
1 μg ~2–12 kb 860 million
Nextera
Mate Pair
Gel-Plus
Nextera
Mate Pair
Gel-Plus
Nextera
Mate Pair
Gel-Plus
* This table demonstrates example diversity values, with diversity reported
in number of unique fragments. Actual diversities achieved with this kit
may vary and depend on several factors, including DNA input quantity,
DNA quality, and precise execution of the protocol.
†
Library diversity was calculated from the number of unique read
pairs observed in a data set, using a method based on the
Lander-Waterman equation2
.

155
Nextera Rapid Capture Exomes Kit
Highlights
• Rapid exome preparation and enrichment
Prep and enrich 96 exomes in only 1.5 days with less than
5 hours hands-on time
• Comprehensive exome coverage
Two different exome designs are available to access core
exonic content or expanded content
• Kit configurations designed to fit your needs
Choose the optimal fit for your system, samples, and study,
with more flexible options than ever before
• Complete support for entire process from
sample preparation to sequencing
All-in-one kit for prep and enrichment from the world’s leading
sequencing provider
Overview
Nextera Rapid Capture Exomes are all-in-one kits for sample
preparation and exome enrichment that allow researchers to identify
coding variants up to 70% faster than other methods. Nextera Rapid
Capture Exome delivers 37 Mb of expertly selected exonic content,
including challenging regions excluded from other exome designs.
Rapid Exome Prep and Enrichment
Nextera Rapid Capture Exomes provide sample prep and exome
enrichment in only 1.5 days. Sequencing with the HiSeq®
2500 or
NextSeq™ 500 system enables experiments to go from DNA sample
to data in as little as 2.5 days. The speed of Nextera Rapid Capture
Exomes enables you to complete projects faster, return results faster,
and ultimately publish faster.
Focused Exonic Content
Nextera Rapid Capture Exome has been optimized to provide uniform
and specific coverage of 37 Mb of expert-selected exonic content.
The probe set was designed to enrich 214,405 exons (Table 1). This
focused design, paired with uniform and specific enrichment, enables
the most comprehensive exome sequencing available and reliable
identification of true, coding variants (Table 2).
Nextera®
Rapid Capture Exomes
A rapid workflow and comprehensive exome content, with unparalleled flexibility.
Table 1: Coverage Details
Nextera Rapid
Capture Exome
Nextera Rapid Capture
Expanded Exome
Coverage Specifications
Number of
target exons
214,405 201,121
Target content Coding exons
Exons, UTRs,
and miRNA
Percent of Exome Covered (by Database)
Refseq 98.3% 95.3%
CCDS 98.6% 96.0%
ENSEMBL 97.8% 90.6%
GENCODE v12 98.1% 91.6%
Table 2: Comparison of Rapid Capture Exomes
Specification
Nextera Rapid
Capture Exome
Nextera Rapid Capture
Expanded Exome
Target size 37 Mb 62 Mb
Genomic DNA
input
50 ng
Hands-on time 5 hours
Total time 1.5 days
Batch size 1–96 exomes

156
Greater Coverage with Expanded Exome
Nextera Rapid Capture Expanded Exome features a highly optimized
probe set that delivers broad coverage of exons as well as expanded
content, such as UTRs and miRNA binding sites. Genome-wide
association studies suggest that 80% of disease-associated
variants fall outside coding regions1
. Analysis of these regions enables
researchers to discover variants that affect gene function, at a more
affordable price than whole-genome sequencing. The kit includes
340,000 95mer probes, each constructed against the human
NCBI37/hg19 reference genome (Table 1). Nextera Rapid Capture
Expanded Exome targets a genomic footprint of 62 Mb.
Unmatched Ease
Nextera Rapid Capture Exomes allows researchers to maximize the
productivity of their lab personnel and Illumina sequencing technology.
The simplicity and speed of the Nextera Rapid Capture assay enables
a single technician to prepare and enrich 96 samples in only 1.5 days.
The process starts with rapid Nextera-based sample prep to convert
input genomic DNA into adapter-tagged libraries (Figure 1A). This rapid
prep requires only 50 ng of input DNA and takes less than 3 hours for
a plate of 96 samples. Nextera tagmentation of DNA simultaneously
fragments and tags DNA without the need for mechanical shearing.
Figure 1: Nextera Rapid Capture Workflow
B. Denature double-stranded DNA library (for simplicity, adapters
and indexes not shown)
A. Prepare sample
Pooled Sample Library
Biotin probes
D. Enrich using streptavidin beads
C. Hybridize biotinylated probes to targeted regions
Streptavidin beads
E. Elute from beads
Enrichment-Ready Fragment
Tagmentation
PCR Amplification
Transposomes Genomic DNA
P5
P7
Index 1
Index 2
~ 300 bp
~ 300 bp
The Nextera Rapid Capture Exome Kit provides a fast, simple method for isolating the human exome. The streamlined, automation-friendly workflow
combines library preparation and exome enrichment steps, and can be completed in 1.5 days with minimum hands-on time.

157
Integrated sample barcodes then allow the pooling of up to
12 samples for a single exome Rapid Capture pull down. Next,
libraries are denatured into single-stranded DNA (Figure 1B) and biotin-
labeled probes specific to the targeted region are used for the Rapid
Capture hybridization (Figure 1C).
The pool is enriched for the desired regions by adding streptavidin
beads that bind to the biotinylated probes (Figure 1D). Biotinylated
DNA fragments bound to the streptavidin beads are magnetically
pulled down from the solution (Figure 1E). The enriched DNA
fragments are then eluted from the beads and hybridized for a
second Rapid Capture. This entire process is completed in only
1.5 days, enabling a single researcher to efficiently process up to
96 exomes at one time—all without automation.
Summary
Nextera Rapid Capture Exomes provide a fully integrated, rapid
solution for exome library prep and enrichment. Available in a wide
range of kit configurations (Table 3), as well as two unique designs,
Nextera Rapid Capture Exomes provide unparalleled flexibility to
optimally align with your specific needs.
References
1. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, et al. (2009)
Finding the missing heritability of complex diseases. Nature 4618: 747–753.
Table 3: Nextera Rapid Capture Throughput by Illumina Sequencing Systems
Pooling Plexity
Exome Samples per Run
MiSeq
NextSeq 500—
Mid Output
NextSeq 500—
High Output
HiSeq 2500—
Rapid Run Mode
HiSeq 2500—
High Output
1 Up to 1 – – – –
3 – Up to 3 – – –
6 – – Up to 6 Up to 24 Up to 96
9 – – Up to 9 Up to 24 Up to 115
12 – – Up to 12 Up to 24 Up to 115
Table 3 helps identify which options provide optimal alignment across three vital study design considerations: sequencing instrument, number of exome samples sequenced per run,
and the number of exome samples pooled together before enrichment (pooling plexity).
Kit Description Catalog No.
Nextera Rapid Capture Exome (8 rxn x 1 plex) FC-140-1000
Nextera Rapid Capture Expanded Exome
(2 rxn x 12 plex)
FC-140-1004
(4 rxn x 12 plex)
FC-140-1005
(8 rxn x 12 plex)
FC-140-1006

158
Nextera Rapid Capture Custom Enrichment Kit
Highlights
• Integrated sample preparation and enrichment workflow
Nextera tagmentation and optimized hybridization reduce
workflow duration and generate data faster
• Target your regions of interest
Choose 0.5-15 Mb of custom content, and pool up to
12 samples per enrichment reaction
• Evolve your design with add-on content
Supplement existing panels and keep adding on as your
research needs expand
Introduction
Nextera Rapid Capture Custom Enrichment is an all-in-one assay
for sample preparation and custom target enrichment. Nextera
tagmentation coupled with optimized target capture ensures the
fastest enrichment workflow time for your custom content. The
flexible, fully customizable design accommodates up to 15 Mb of
custom content so you can focus on the regions of the genome that
you care about. The new add-on feature in DesignStudio allows you to
iteratively expand your content as new discoveries are made.
Custom Probe Design
The first step in developing any Nextera Rapid Capture Custom
Enrichment assay is to design your custom probe set. DesignStudio
is a free online user-friendly tool accessed through your MyIllumina
account. Designate your regions of interest, refine your custom probe
set and place an order for your custom design. DesignStudio uses a
complex algorithm to optimize probe set design and alert you to any
potential coverage gaps or challenging regions. Desired targets can
be added individually or in batches by chromosomal coordinate or
gene name.
Unmatched Ease of Workflow
Nextera Rapid Capture Enrichment allows researchers to maximize the
productivity of their lab personnel and Illumina sequencing technology.
The simplicity and speed of the Nextera Rapid Capture assay enables
a single technician to prepare and enrich 12 samples in only 1.5 days.
Nextera-based sample preparation generates adapter-tagged libraries
from 50 ng input genomic DNA (Figure 2A). Nextera tagmentation of
DNA simultaneously fragments and tags DNA without the need for
mechanical shearing. Integrated sample barcodes allow the pooling
of up to 12 of these adapter ligated sample libraries into a single,
hybridization-based, pull down reaction. The pooled libraries are then
denatured into single-stranded DNA (Figure 2B) and biotin-labeled
probes complementary to the targeted region are used for the Rapid
Capture hybridization (Figure 2C). Streptavidin beads are added, which
bind to the biotinylated probes that are hybridized to the targeted
regions of interest (Figure 2D). Magnetic pull down of the streptavidin
beads enriches the targeted regions that are hybridized to biotinylated
probes. (Figure 2E). The enriched DNA fragments are then eluted
from the beads and a second round of Rapid Capture is completed
to increase enrichment specificity. The entire process is completed in
only 1.5 days, enabling a single researcher to efficiently process up to
12 samples at one time—all without automation.
Data Analysis
Sequence data generated from custom enrichment samples on
HiSeq®
and NextSeq™ systems are analyzed using the Enrichment
Workflow in the HiSeq Analysis Software (HAS). HAS analysis can
be accessed directly via a linux kernel or by using the optional
Analysis Visual Controller (AVC) interface1
.
Custom pools sequenced on MiSeq®
are analyzed using MiSeq
Reporter (MSR). The Enrichment Workflow from both HAS and MSR
generates aligned sequence reads in the .bam format using the
BWA algorithm and performs indel realignment using the GATK
indel realignment tool. Variant calling occurs in the target regions
specified in the manifest file. The GATK variant caller generates .vcf
Nextera®
Rapid Capture Custom Enrichment
Leverage a superior sample preparation and enrichment workflow for unparalleled access to your
regions of interest.
Figure 1: Overview of Nextera Rapid Capture
Custom Enrichment
Access DesignStudio through MyIllumina to
create custom probes and place order
Rapid capture target regions
using custom probes
Perform cluster generation sequencing
on any Illumina sequencing instrument
Analyze data
Perform sample prep using 50 ng of input DNA;
pool up to 12 samples
The Nextera Rapid Capture Custom Enrichment Kit is an integral part of a
complete and fully supported solution for targeted resequencing.

159
files that contain genotype, annotation and other information across
all sites in the specified target region. Coverage files containing
coverage depth in the genome and within gaps is also generated
(.CoverageHistogram.txt, .gaps.csv). Additionally, enrichment summary
statistics are provided via the.enrichment_summary.csv file or through
the CalculateHSMetrics.jar tool within the Picard Suite (.HSmetrics.txt).
The enrichment files contain a summary of the on-target and off-target
reads/base, average coverage in the target region, % reads that are
present at 1×, 10×, 20×, and 50× coverage, read/base enrichment
and variant calls information including number of variants (SNP and
Indel), Het/Hom and Ts/Tv ratios and the overlap with a standard
curated database.
Data Examples
Four different Nextera Rapid Capture Enrichment experiments were
performed following the workflow described in Figure 2. Each project
included different target regions and coverage depths (Table 1).
Representative enrichment and coverage data are shown in Figure 3.
In all multiplexed projects, high percent enrichment was achieved,
and mean normalized coverage plots show that 85% of bases
are covered at 0.2× of the mean coverage. Figure 4 shows that
supplementing an existing design (Nextera Rapid Capture Exome) with
custom add-on content does not notably decrease coverage uniformity.
Figure 2: Nextera Rapid Capture Workflow
B. Denature double-stranded DNA library (for simplicity, adapters
and indexes not shown)
A. Prepare sample
Pooled Sample Library
Biotin probes
D. Enrich using streptavidin beads
C. Hybridize biotinylated probes to targeted regions
Streptavidin beads
E. Elute from beads
Enrichment-Ready Fragment
Tagmentation
PCR Amplification
Transposomes Genomic DNA
P5
P7
Index 1
Index 2
~ 230 bp
~ 230 bp
Nextera Rapid Capture Custom Enrichment provides a simple and streamlined in-solution method for isolating and enriching targeted regions of interest.
The workflow combines library preparation and exome enrichment steps, and can be completed in 1.5 days with minimum hands-on time.

160
Summary
Nextera Rapid Capture Custom Enrichment leverages a superior
integrated sample prep and enrichment workflow to provide
unparalleled access to your genomic regions of interest. Not only will
you be able to perform targeted sequencing using only 50 ng of input
DNA, you’ll do so faster and more efficiently than ever before. Take
advantage of robust add-on functionality to refine your content over
time, or add regions of unique interest to established panels such as
Nextera Rapid Capture Exome or other TruSight™ content sets.
Figure 3: High Coverage Uniformity Across Custom 12-plex Pools
0
10
20
30
40
50
60
70
80
90
100
%Basescoveredat0.2xmeancoverage
Project 1
Project 2
Project 3
Project 4
1 2 3 4 5 6 7 8 9 10 11 12
0
10
20
30
40
50
60
70
80
90
100
%Basescoveredat0.2xmeancoverage
A B
Nextera Rapid Capture Custom Enrichment provides uniform target enrichment across different custom probe sets and individual samples within a 12-plex
pool. A. Coverage uniformity is shown as % of targeted bases that are represented by 0.2× mean coverage. Mean coverage for these custom probe sets
can be found in Table 1. Error bars show SD of uniformity across the 12 pooled samples for each project. B. Coverage uniformity for each of 12 pooled
samples within Project 3 is shown. Mean coverage for this run was 300×, and % of targeted bases that were covered at 60× are shown.
Figure 4: Add-On Content Retains High Coverage
Exome Add-On Exome + Add-On
91.9% 88.6% 89.0%
0
10
20
30
40
50
60
70
80
90
100
%Basesat0.2xmeancoverage
High coverage uniformity is maintained when 3.5 Mb of add-on content
is added to the Nextera Rapid Capture Exome. All samples were run as
12-plex pools.
Table 1: Sequencing Details for Example Projects
Project Content
Mean
Coverage
% On Target
Bases*
1** 0.5 Mb 1500× 88.6
2** 0.5 Mb 146× 79.5
3†
3.5 Mb 300× 80.1
4** 7 Mb 152× 72.5
*Calculated using Picard Hybrid Selection tool with 250 bp padding2
**Sequenced on HiSeq
†
Sequenced on MiSeq

161
Genome Analyzer, GenomeStudio, GoldenGate, HiScan, HiSeq, Infinium, iSelect, MiSeq, Nextera, NextSeq, NuPCR, SeqMonitor,
Solexa, TruSeq, TruSight, VeraCode, the pumpkin orange color, and the Genetic Energy streaming bases design are trademarks
or registered trademarks of Illumina, Inc. All other brands and names contained herein are the property of their respective owners.
Pub. No. 770-2013-025 Current as of 21 December 2013
Learn More
To learn more about complete solutions for targeted resequencing, visit
www.illumina.com/applications/sequencing/targeted_resequencing.ilmn.
References
1. http://support.illumina.com/sequencing/sequencing_software/analysis_
visual_controller_avc.ilmn
2. http://picard.sourceforge.net
Nextera Rapid Capture Custom Enrichment Details
Enrichment Efficiency* 70%
Coverage Uniformity (0.2x mean) 85%
Content Range 0.5–15 Mb
Samples in Pre-Enrichment Pooling Up to 12
Sample Input 50 ng
Library Insert Size 230
*Target values will vary due to custom designs.
Product Catalog No.
Nextera Rapid Capture Custom (48 samples)
Compatible with designs of 3,000-10,000
custom enrichment probes
FC-140-1007
FC-140-1008
FC-140-1009

162
EpiGnome™
Methyl-Seq Kit
www.epicentre.com • epicentral.blogspot.com • Toll-Free U.S. (800) 284-8474 • Fax (608) 258-3089 PDS 009a
Methyl-Seq
EpiGnome™ Methyl-Seq Kit
Unlock limited samples (50-100ng DNA input) to discover
methylation patterns of all CpG, CHH CHG regions.
„ Unlock small samples (50-100ng DNA input)
„ Pre-library bisulfite conversion
„ Comprehensive, whole genome results
„ 5 hour method
„ Informatics app note demystifies analysis
„ Capture full sample diversity
Bisulfite Conversion EpiGnome™MasterPure™ DNASample
2 Hrs – Purification ~3 Hrs – Conversion 5 Hrs – Library Prep
Workflow
Figure 1. EpiGnome is sensitive to CpG methylation patterns.
CpG methylation patterns across region of chr1 show variable CpG methylation (red) from 50 ng input of GM12878 lymphoblastoid gDNA treated
with bisulfite. Comparison to coverage patterns from non-bisulfite treated (green) gDNA shows the methylated regions of chromosome 1.
Sequence the entire sample–no loss of information!
The process of bisulfite treatment denatures genomic DNA
into single stranded DNA. EpiGnome converts single stranded
DNA into an Illumina® sequencing library. All ssDNA fragments
are captured into an Illumina sequencing library during the
EpiGnome procedure, therefore eliminating sample loss
associated with other methods.
Calico cats are domestic cats with a spotted or parti-colored
coat that is predominantly white, with patches of two other
colors. Calico cats are almost always female because the X
chromosome determines the coat color. During embryonic
development, one X chromosome is hypermethylated and
inactivated. The remaining X chromosome determines coat
color.

163
Cat. # Quantity
EpiGnome™ Methyl-Seq Kit
EGMK81312 12 reactions
EpiGnome™ Index PCR Primers
EGIDX81312 12 indexes,
10 reactions each
FailSafe™ PCR Enzyme Mix
FSE51100 100 units
The FailSafe™ PCR Enzyme Mix is required for EpiGnome
Methyl-Seq Kit.
Figure 2. Deep coverage of genes of interest.
EpiGnome WGBS method yields high coverage of genes of interest for Cancer genes and those that have been defined as medically relevant by the
American College of Medical Genetics.
Deep coverage of critical genomic regions
Depth of coverage is enhanced in genomic areas with
biological utility (Figure 2). EpiGnome captures full sample
diversity of critical areas including:
• Coding region start and end for exons from the canonical
transcript of protein coding genes for genes known to be
involved in cancer, taken from SOMA and CRUK panels as
well as literature derived Cancer genes.
• Genes defined by the American College of Medical Genetics
as being medically relevant (ACMG_genes)
• Exonic coding regions from Ensemble 70 (exons_
ensemble70)
• List of 100 promoters defined by the Broad Institute as
being of high interest and difficult to sequence (fosmid_
promoters)
Coverage was obtained from 125.4 million reads in a single
lane of a HiSeq. Increasing throughput of the HiSeq Systems
enables complete methylation information to be captured
from a growing number of samples.
Success begins with purification
MasterPure™ DNA Purification Kit
Purification is an important step to prepare your sample.
MasterPure safely removes unwanted material to give you
pure DNA.
MasterPure offers unique benefits:
„ Very high yields
„ Recover 90% of theoretical yield
„ Safe and nontoxic
„ Available for all sample sizes
Cat. # Quantity
MasterPure™ Complete DNA and RNA Purification Kit
MC85200 200 Purifications
12
10
8
6
4
2
0
200
160
120
80
40
0
180
140
100
60
20
coverage (fold) normalized coverage
genome ACMG_
genes
Cancer
Genes
exons_
ensemble70
fosmid_
promoters
foldcoverage(x)
normalizedcoverage(%)
ACMG_genes: designated as
medically relevant.
Cancer genes: protein coding
genes known to be involved in
cancer.
fosmid_promoters: of high
interest and difficult to sequence.

164
TruSeq ChIP Sample Prep Kit
Highlights
• Proven TruSeq Data Quality
Most complete and accurate profile of target protein:
DNA interactions
• Low DNA Input Requirement
Robust results from just 5 ng DNA from
a range of sample sources
• Simple, Streamlined Workflow
Enhanced scalability with an easy-to-use, simplified workflow
• Multiplexed Sequencing with 24 Available Indexes
Optimize sequencing output distribution across samples,
reducing cost per sample
Introduction
Determining how protein–DNA interactions regulate gene expression
is essential for fully understanding many biological processes and
disease states. This epigenetic information is complementary to DNA
sequencing, genotyping, gene expression, and other forms of genomic
analysis. Chromatin immunoprecipitation sequencing (ChIP-Seq)
leverages next-generation sequencing (NGS) to quickly and efficiently
determine the distribution and abundance of DNA-bound protein
targets of interest across the genome. ChIP-Seq has become one of
the most widely applied NGS-based applications, enabling researchers
to reliably identify binding sites of a broad range of targets across the
entire genome with high resolution and without constraints.
As the output of NGS systems has increased, ChIP-Seq researchers
increasingly require a combination of highly multiplexed sequencing
and simple, streamlined workflows. TruSeq ChIP Sample Preparation
Kits meet those demands, offering a simple, cost-effective solution
for obtaining visibility into the mechanics of gene regulation. Library
generation from ChIP-derived DNA includes the addition of indexed
adapters, enabling the optimal distribution of sequencing output based
on coverage needs. An optimized, highly scalable sample preparation
workflow and master-mixed reagents reduce hands-on time and support
an automation-friendly format for parallel processing of up to 48 samples.
Samples with different indices can be mixed and matched to maximize
experimental throughput. A low sample input requirement (5 ng) ensures
robust results even when input DNA availability is limited, providing
flexibility in the choice of sample source and target proteins for analysis.
Simple, Streamlined Workflow
TruSeq ChIP Sample Preparation Kits provide a significantly improved
library preparation workflow compared to other methods. The TruSeq
workflow reduces the number of purification, sample transfer, pipet-
ting, and clean-up steps. A universal adapter design incorporates
an index sequence at the initial ligation step for improved workflow
efficiency and more robust multiplex sequencing (Figure 1).
TruSeq®
ChIP Sample Preparation Kit
Proven TruSeq data quality delivers the most complete and accurate profile of
target protein–DNA interactions.
Figure 1: ChIP-Seq Workflow
F. Denature and amplify to produce final product for sequencing
Rd2 SP’
E. Ligate TruSeq index adapter
Rd1 SP
P5
P7
Index Rd2 SP
D. A-tailing
P
P
A
A
P
P
C. End repair and phosphorylate
+
P
A
T
P
Rd1 SP
P5 P7
Index
Rd2 SP
Rd1 SP
P5P7
Index
Rd2 SP
P7’
5’
5’
A
P
P
P
B. ChIP: Enriched DNA binding sites*
A. Crosslink and fractionate chromatin*
Nucleus
The simple, streamlined TruSeq ChIP Sample Preparation Kit workflow
(Steps C–F), reduces hands-on time and speeds analysis.TruSeq
universal adapters improve workflow efficiency and enable robust
multiplex sequencing.
*Steps A and B are performed prior to the TruSeq ChIP Sample Prep workflow.

165
The TruSeq ChIP process begins with the enrichment of specific
cross-linked DNA-protein complexes using an antibody against a
protein of interest (Figure 1A-B). The stretches of DNA bound to the
target protein are then isolated and used as input DNA for library
generation. DNA fragments are end-repaired and an ‘A’-base added
to the blunt ends of each strand, preparing them for ligation to the
sequencing adapters (Figure 1C-D). Each TruSeq adapter contains a
‘T’-base overhang on the 3'-end providing a complementary overhang
for ligating the adapter to the A-tailed fragmented DNA (Figure 1E).
Final product is created (Figure 1F) and after size selection, all of the
ChIP DNA fragments are simultaneously sequenced.
For maximum flexibility, TruSeq ChIP Sample Preparation Kits can be
used to prepare samples for single-read or paired-end sequencing,
and are compatible with any Illumina sequencing instrument, including
MiSeq®
and all instruments in the HiSeq®
system family.
TruSeq Data Quality
Proven TruSeq data quality delivers the most complete and accurate
profile of target protein–DNA interactions, enabling an optimal percent-
age of passing filter reads, percent alignable reads, and coverage
uniformity, as well as high sensitivity to detect low-abundance hits.
Robust Multiplex Performance
The TruSeq ChIP Sample Preparation Kits provide up to 24
total indexes to increase throughput and consistency without
compromising results. The TruSeq universal adapters ligate to
sample fragments during library construction, allowing samples to
be pooled and individually identified during downstream analysis.
This indexing capability improves workflow efficiency and enables
robust multiplex sequencing. By enhancing study design flexibility,
indexing aids researchers in deriving the most value from each run
by efficiently distributing read output based on optimal per-sample
read depth requirements.
Figure 2: Bioanalyzer Trace of MafK Library
70
60
50
40
30
20
10
35 100 200 300 400 600 1000 10380
Base Pairs
FluorescentUnits
35 264
10380
-10
0
Bioanalyzer trace data for a library generated for transcription factor target
MafK using the TruSeq ChiP Sample Preparation Kit with 5 ng of input DNA.
The center peak indicates robust yield within the desired insert size range.
Figure 3: Peak Finding Output for MafK
Scale chr10
10 kb hg19
121,340,000 121,345,000 121,350,000 121,355,000
Extended tag pileup from MACS version 1.4.2 20120305 for every 10 bp
RefSeq Genes
HEPG2 MafK SC477 IgG-rab ChIP-Seq Signal from ENCODE/SYDH
TIAL1
TIAL1
IgControl_run1_treat_chr10
1 _
MAFK_run1_treat_chr10
30 _
30 _
1 _
HEPG MafK IgR
428 _
1 _
TruSeq ChIP Sample Preparation Kits enable the generation of libraries across a broad range of study designs. Above is peak data for a negative Ig control, the
transcription factor target MafK, and a reference peak for MafK from the ENCODE database.
Table 1: Motif-Finder Analysis of Peaks Identified
using TruSeq Sample Preparation Kits Compared
to ENCODE Reference Peak Data
Name % Top Peaks with MafK Motif
TruSeq ChIP 95%
ENCODE HELA 92%
ENCODE HES 86%

166
Figure 4: Peak Finding Output for H3K4me3
Scale chr1
10 kb hg19
179,855,000 179,860,000 179,865,000 179,870,000 179,875,000 179,880,000 179,885,000
RefSeq Genes
H3K4Me3 Mark (Often Found Near Promoters) on 7 cell lines from ENCODE
TOR1AIP1
TOR1AIP1
H3K4me3
57 _
1 _
Layered H3K4Me3
410.6 _
0.04 _
The peak results for the H3K4me3 target compare favorably with the ENCODE annotation data for this well characterized target, with a representative peak for the
histone mark target H3K4me3 and a corresponding ENCODE reference peak.
Flexible Range of Targets
TruSeq ChIP Sample Preparation Kits enable libraries to be generated
using as little as 5 ng input DNA and provide a high-quality, cost-
efficient, and high-throughput solution across a broad array of ChIP
study designs. ChIP-Seq is an extremely versatile application that has
been successfully applied against a wide range of protein targets,
including transcription factors and histones, the building blocks of
chromatin. ChIP studies targeting transcription factors are useful in
elucidating the specific modulators and signal transduction pathways
contributing to disease states, stages of development, or across other
conditions, while histone “marks” can be used to better understand
how chromatin modifications and local structural changes impact local
gene expression activity.
Detecting Peaks Across the Genome
Using the TruSeq ChIP Sample Preparation Kit, a library was
generated for transcription factor MafK using 5 ng of input DNA
(Figure 2) derived from a ChIP performed in HELA cells. Sequencing
data were generated using a single MiSeq run. Quality-filtered, BAM
output files were then entered into the MACS peak finder software,
with the identified peaks then screened for enrichment using MEME
motif finder software. Figure 3 illustrates the sensitivity to reliably
detect DNA-protein interactions, with a representative, identified
peak corresponding to an MafK binding site included in the ENCODE
project database. Enrichment for the known, MafK binding motif
was detected as expected (Table 1), again in concordance with data
generated using MafK peak data available through ENCODE. The
ability to robustly detect peaks across the genome with low starting
input amounts is critical to ensuring successful ChIP studies.
TruSeq ChIP Sample Preparation Kits provide the flexibility to target
any protein target of interest, offering a streamlined, cost-efficient
solution for studies requiring a broad range of reads per sample
including transcription factors (Figure 3), and histone marks, such as
H3K4Me3 (Figure 4).
Illumina Sequencing Solutions
TruSeq ChIP Sample Preparation Kits are compatible with all
Illumina sequencing by synthesis (SBS)–based systems, including
the MiSeq and the HiSeq platforms. Offering a revolutionary
workflow and unmatched accuracy, MiSeq goes from DNA to data
in less than eight hours to support smaller studies. Innovative
engineering enables HiSeq systems to process larger numbers of
samples quickly and cost-effectively. Data compatibility is ensured
whichever system is chosen.

167
Product Catalog No.
TruSeq ChIP Sample Preparation Kit, Set A
IP-202-1012
TruSeq ChIP Sample Preparation Kit, Set B
IP-202-1024
Illumina, Inc. • 1.800.809.4566 toll-free (U.S.) • +1.858.202.4566 tel • techsupport@illumina.com • www.illumina.com
Illumina, illuminaDx, BaseSpace, BeadArray, BeadXpress, cBot, CSPro, DASL, DesignStudio, Eco, GAIIx, Genetic Energy,
TruSeq, VeraCode, the pumpkin orange color, and the Genetic Energy streaming bases design are trademarks or registered
Pub. No. 770-2012-029 Current as of 22 August 2012
Summary
TruSeq ChIP Sample Preparation Kits offer proven TruSeq
accuracy, and a simple, streamlined workflow, enabling highly-
multiplexed, cost-effective ChIP sequencing. Supporting analysis of
a broad range of targets across the genome even from low sample
input, the kits provide a complete, accurate profile of DNA-protein
binding interactions and enhanced visibility to the mechanics of
gene regulation.
References
1. Johnson DS, Mortazavi A, Myers RM, Wold B (2007) Genome-wide
mapping of in vivo protein-DNA interactions. Science 316: 1497–1502.
2. Barski A, Cuddapah S, Cui K, Roh TY, Schones DE et al. (2007)
High-resolution profiling of histone methylations in the human genome.
Cell 129: 823–837.
3. Marban C, Su T, Ferrari R, Li B, Vatakis D, et al. (2011) Genome-
wide binding map of the HIV-1 Tat protein to the human genome.
PLoS One 6: e26894.
4. Fujiki R, Hashiba W, Sekine H, Yokoyama A, Chikanishi T, et al. (2011)
GlcNAcylation of histone H2B facilitates its monoubiquitination.
Nature 480: 557–560.
5. Botti E, Spallone G, Moretti F, Marinari B, Pinetti V, et al. (2011)
Developmental factor IRF6 exhibits tumor suppressor activity in squamous
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MLL-rearranged leukemia is dependent on aberrant H3K79 methylation by
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8. Wu H, D’Alessio AC, Ito S, Xia K, Wang Z, et al. (2011) Dual functions
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9. ENCODE Project Consortium, Myers RM, Stamatoyannopoulos J, Snyder
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PMCID: PMC3079585.

168
RNA SEQUENCING
DNA-Sequencing
MasterPure™
MasterPure™
DNA Purification Kit MCD85201
TruSeq DNA PCR-Free LT Sample Preparation Kit - Set A FC-121-3001
TruSeq DNA PCR-Free LT Sample Preparation Kit - Set B FC-121-3002
TruSeq DNA PCR-Free HT Sample Preparation Kit FC-121-3003
TruSeq Nano DNA LT Sample Preparation Kit - Set A FC-121-4001
TruSeq Nano DNA LT Sample Preparation Kit - Set B FC-121-4002
TruSeq Nano DNA HT Sample Preparation Kit FC-121-4003
EpiGnome™
Methyl-Seq Kit EGMK81312
ChIP
TruSeq ChIP Sample Preparation Kit - Set A IP-202-1012
TruSeq ChIP Sample Preparation Kit - Set B IP-202-1024
Methylation Arrays

169
RNA-sequencing
MasterPure™
TotalScript™
RNA-Seq Kit TSRNA 12924
ScriptSeq™
Complete Gold Kit (Blood) BGGB1306
ScriptSeq™
Complete Gold Kit (Blood) - Low Input SCL24GBL
Ribo-Zero Magnetic Gold Kit (Yeast) MRZY1324
ScriptSeq™
Complete Gold Kit (Yeast) BGY1324
ScriptSeq™
Complete Gold Kit (Yeast) - Low Input SCGL6Y
ARTseq™
Ribosome Profiling Kit - Mammalian RPHMR12126
ARTseq™
Ribosome Profiling Kit - Yeast RPYSC12116
Any species
TruSeq®
Stranded mRNA LT Set A RS-122-2101
TruSeq®
Stranded mRNA LT - Set B RS-122-2102
TruSeq®
Stranded mRNA HT RS-122-2103
TruSeq™
RNA Sample Prep Kit v2 -Set A (48rxn) RS-122-2001
TruSeq™
RNA Sample Prep Kit v2 -Set B (48rxn) RS-122-2002
Human/Mouse/Rat
TruSeq®
Strnd Total RNA LT(w/Ribo-Zero™
Human/Mouse/Rat)Set A RS-122-2201
TruSeq®
Strnd Total RNA LT(w/Ribo-Zero™
Human/Mouse/Rat)Set B RS-122-2202
TruSeq®
StrndTotal RNA HT (w/ Ribo-Zero™
Human/Mouse/Rat) RS-122-2203
TruSeq®
Stranded Total RNA LT (w/ Ribo-Zero™
Gold) Set A RS-122-2301
TruSeq®
Gold) Set B RS-122-2302
TruSeq®
Stranded Total RNA HT (w/ Ribo-Zero™
Gold) RS-122-2303
Human/Mouse/Rat (Blood-derived)
TruSeq®
Globin) Set A RS-122-2501
TruSeq®
Globin) Set B RS-122-2502
TruSeq®
Globin) RS-122-2503
Plant
TruSeq®
Plant) Set A RS-122-2401
TruSeq®
Plant) Set B RS-122-2402
TruSeq®
Plant) RS-122-2403

170
Small RNA-sequencing
TruSeq®
Small RNA Sample Prep Kit -Set A RS-200-0012
TruSeq®
Small RNA Sample Prep Kit -Set B RS-200-0024
TruSeq®
Small RNA Sample Prep Kit -Set C RS-200-0036
TruSeq®
Small RNA Sample Prep Kit -Set D RS-200-0048
Targeted RNA-Sequencing
TruSeq Targeted RNA Expression Custom Components
TruSeq Targeted RNA Custom Kit (48 Samples) RT-101-1001
TruSeq Targeted RNA Custom Kit (96 Samples) RT-102-1001
TruSeq Targeted RNA supplemental content (48 Samples) RT-801-1001
TruSeq Targeted RNA supplemental content (96 Samples) RT-802-1001
TruSeq Targeted RNA Index Kit RT-401-1001
TruSeq Targeted RNA Expression Fixed Panels
TruSeq Targeted RNA Apoptosis Panel Kit (48 Samples) RT-201-1010
TruSeq Targeted RNA Apoptosis Panel Kit (96 Samples) RT-202-1010
TruSeq Targeted RNA Cardiotoxicity Panel Kit (48 Samples) RT-201-1009
TruSeq Targeted RNA Cardiotoxicity Panel Kit (96 Samples) RT-202-1009
TruSeq Targeted RNA Cell Cycle Panel Kit (48 Samples) RT-201-1003
TruSeq Targeted RNA Cell Cycle Panel Kit (96 Samples) RT-202-1003
TruSeq Targeted RNA Cytochrome p450 Panel Kit (48 Samples) RT-201-1006
TruSeq Targeted RNA Cytochrome p450 Panel Kit (96 Samples) RT-202-1006
TruSeq Targeted RNA HedgeHog Panel Kit (48 Samples) RT-201-1002
TruSeq Targeted RNA HedgeHog Panel Kit (96 Samples) RT-202-1002
TruSeq Targeted RNA Neurodegeneration Panel Kit (48 Samples) RT-201-1001
TruSeq Targeted RNA Neurodegeneration Panel Kit (96 Samples) RT-202-1001
TruSeq Targeted RNA NFkB Panel Kit (48 Samples) RT-201-1008
TruSeq Targeted RNA NFkB Panel Kit (96 Samples) RT-202-1008
TruSeq Targeted RNA Stem Cell Panel Kit (48 Samples) RT-201-1005
TruSeq Targeted RNA Stem Cell Panel Kit (96 Samples) RT-202-1005
TruSeq Targeted RNA TP53 Pathway Panel Kit (48 Samples) RT-201-1007
TruSeq Targeted RNA TP53 Pathway Panel Kit (96 Samples) RT-202-1007
TruSeq Targeted RNA Wnt Pathway Panel Kit (48 Samples) RT-201-1004
TruSeq Targeted RNA Wnt Pathway Panel Kit (96 Samples) RT-202-1004

171
TotalScript™
RNA-Seq Kit
Powered by Nextera! TotalScript RNA-Seq Kit is designed
for RNA-Seq of precious samples, and only 1-5 ng of intact
total RNA is needed for each sample. No need for poly(A)
enrichment or rRNA removal. Sequencing data is similar to
data from libraries using much more RNA.
Retain more sample
Prevent transcript loss
TotalScript produces consistent results
from small amounts of sample (Fig. 1).
1 ng or 5 ng of total RNA was prepared
with TotalScript. Results show similiar
amounts of coding and non-coding
coverage between samples. The sample
was Universal Human Reference RNA (UHR)
total RNA.
Figure 1. Consistent results from TotalScript.
RNA-Seq without rRNA Depletion
TotalScript™ RNA-Seq Kits
„ Powered by Nextera™
„ 1-5 ng Input RNA
„ Designed for precious samples
„ Rapid method with 5 hr workflow
„ rRNA removal not required, begin with total RNA
„ Directional libraries
„ 12 Indexes available
33%
33%
13%
21%
Intronic
Intergenic
rRNA
mRNA
1 ng 5 ng
30%
17%
20%
33%
Workflow
5 Hrs – Library Prep2 Hrs – Purification
TotalScript™MasterPure™Sample
New kinds of samples can now be sequenced, including:
„ Cancer samples
„ Stem cells
„ Other low input samples

172
Figure 2. You choose the coverage profile.
Cat. # Quantity
TotalScript™ RNA-Seq Kit
TSRNA12924 24 Reactions
TSRNA1296 12 Reactions
TotalScript™ Index Kit
TSIDX12910 11 indexes
Method
Total RNA
Input (ng) % rRNA Coverage
Random
Priming
1-5 40% Even
Mixed
Priming
1-5 25% Slight 3′ Bias
dT Priming 1-5 5% 3′ Bias
You choose the desired rRNA content and transcript
coverage with TotalScript™
Three options are included in every TotalScript kit (Fig. 2).
All options produce directional libraries from very small
amounts of total RNA.
1. Random Hexamer Primer option produces even transcript
coverage with 40% of reads mapping to rRNA.
2. Mixed Primer option produces good transcript coverage
with 25% rRNA mapped reads.
3. Oligo(dT) Primer option produces 5% rRNA reads with
transcript coverage strongest at the 3′ end.
Different sources of RNA may produce different levels of
rRNA contamination.
TotalScript RNA-Seq libraries shown were made from 5 ng
of total UHR RNA using the Optimized Buffer included with
TotalScript (Fig 2).
MasterPure™ RNA Purification Kit
pure, intact total RNA.
„ Keep RNA intact (does not degrade RNA)
„ Retain RNA diversity (including small RNA)
„ Maximize genes discovered
Cat. # Quantity
MasterPure™ RNA Purification Kit (for isolating RNA only)
MCR85102 100 Purifications

173
TruSeq RNA and DNA Sample Prep Kits
Sequencing
Highlights
Simple Workflow for RNA and DNA:
Master-mixed reagents and minimal hands-on steps.
Scalable and Cost-Effective Solution:
Optimized formulations and plate-based processing
enables large-scale studies at a lower cost.
Enhanced Multiplex Performance:
Twenty-four adaptor-embedded indexes enable high-
throughput processing and greater application flexibility.
High-Throughput Gene Expression Studies:
Gel-free, automation-friendly RNA sample preparation
for rapid expression profiling.
Introduction
Illumina next-generation sequencing (NGS) technologies continue to
evolve, offering increasingly higher output in less time. Keeping pace
with these developments requires improvements in sample prepara-
tion. To maximize the benefits of NGS and enable delivery of the high-
est data accuracy, Illumina offers the TruSeq RNA and DNA Sample
Preparation Kits (Figure 1).
The TruSeq RNA and DNA Sample Preparation Kits provide a simple,
cost-effective solution for generating libraries from total RNA or genomic
DNA that are compatible with Illumina’s unparalleled sequencing output.
Master-mixed reagents eliminate the majority of pipetting steps and
reduce the amount of clean-up, as compared to previous methods,
minimizing hands-on time. New automation-friendly workflow formats
enable parallel processing of up to 96 samples. This results in economi-
cal, high-throughput RNA or DNA sequencing studies achieved with the
easiest-to-use sample preparation workflow offered by any NGS platform.
Simple and Cost-Effective Solution
Whether processing samples for RNA-Seq, genomic sequencing, or
exome enrichment, the TruSeq kits provide significantly improved library
preparation over previously used methods. New protocols reduce the
number of purification, sample transfer, and pipetting steps. The new
universal, methylated adaptor design incorporates an index sequence at
the initial ligation step for improved workflow efficiency and more robust
multiplex sequencing. For maximum flexibility, the same TruSeq kit can
be used to prepare samples for single-read, paired-end, and multi-
plexed sequencing on all Illumina sequencing instruments.
TruSeq DNA and RNA Sample Prep kits include gel-free protocols
that eliminate the time-intensive gel purification step found in other
methods, making the process more consistent and fully automatable.
The gel-free protocol for TruSeq DNA sample preparation is available
for target enrichment using the TruSeq Exome Enrichment or TruSeq
Custom Enrichment kits.
TruSeq sample preparation makes RNA sequencing for high-through-
put experiments more affordable, enabling gene expression profiling
studies to be performed with NGS at a lower cost than arrays. It also
provides a cost-effective DNA sequencing solution for large-scale
whole-genome resequencing, targeted resequencing, de novo se-
quencing, metagenomics, and methlyation studies.
Enhanced Multiplex Performance
TruSeq kits take advantage of improved multiplexing capabilities to
increase throughput and consistency, without compromising results.
Both the RNA and DNA preparation kits include adapters containing
unique index sequences that are ligated to sample fragments at the
beginning of the library construction process. This allows the samples
to be pooled and then individually identified during downstream
analysis. The result is a more efficient, streamlined workflow that leads
directly into a superior multiplexing solution. There are no additional
PCR steps required for index incorporation, enabling a robust, easy-
to-follow procedure. With 24 unique indexes available, up to 384
samples can be processed in parallel on a single HiSeq 2000 run.
TruSeq RNA Sample Preparation
With TruSeq reagents, researchers can quickly and easily prepare
samples for next-generation sequencing (Figure 2). Improvements in
the RNA to cDNA conversion steps have significantly enhanced the
overall workflow and performance of the assay (Figure 3).
TruSeq™ RNA and DNA Sample Preparation Kits v2
Master-mixed reagents, optimized adapter design, and a flexible workflow provide a simple, cost-
effective method for preparing RNA and DNA samples for scalable next-generation sequencing.
Figure 1: TruSeq Sample Preparation Kits
TruSeq Sample Preparation Kits are available for both genomic DNA and
RNA samples.

174
Sequencing
Starting with total RNA, the messenger RNA is first purified using
polyA selection (Figure 2A), then chemically fragmented and converted
into single-stranded cDNA using random hexamer priming. Next, the
second strand is generated to create double-stranded cDNA (Figure
2B) that is ready for the TruSeq library construction workflow (Figure 4).
Efficiencies gained in the polyA selection process, including reduced
sample transfers, removal of precipitation steps, and combining of
elution and fragmentation into a single step, enable parallel processing
of up to 48 samples in approximately one hour. This represents a 75%
reduction in hands-on time for this portion of library construction. Im-
proving performance, the optimized random hexamer priming strategy
provides the most even coverage across transcripts, while allowing
user-defined adjustments for longer or shorter insert lengths.
Eliminating all column purification and gel selection steps from the
workflow removes the most time-intensive portions, while improving the
assay robustness. It also allows for decreased input levels of RNA—as
low as 100 ng— and maintains single copy per gene sensitivity.
TruSeq DNA Sample Preparation
The TruSeq DNA Sample Preparation Kits are used to prepare DNA
libraries with insert sizes from 300–500 bp for single, paired-end, and
multiplexed sequencing. The protocol supports shearing by either
sonication or nebulization with a low input requirement of 1 ug of DNA.
Sequence-Ready Libraries
Library construction begins with either double-stranded cDNA syn-
thesized from RNA or fragmented gDNA (Figure 4A). Blunt-end DNA
fragments are generated using a combination of fill-in reactions and
exonuclease activity (Figure 4B). An ‘A’- base is then added to the
blunt ends of each strand, preparing them for ligation to the sequenc-
ing adapters (Figures 4C). Each adapter contains a ‘T’-base overhang
on 3’-end providing a complementary overhang for ligating the adapter
50% of pipetting steps eliminated
50% of reagent tubes eliminated
75% of clean-up steps eliminated
50% of sample transfer steps eliminated
Compared to previous kits, processing multiple samples with the
new TruSeq Sample Preparation Kits provides significant reductions
in library construction costs, the number of steps, hands-on time,
and PCR dependency.
Figure 3: TruSeq RNA Sample Preparation Reagents
Provide Significant Savings in Time and Effort
Compared to current methods for preparing mRNA samples for sequencing,
use of the TruSeq reagents significantly reduces the number of steps and
hands-on time.
Figure 2: Optimized TruSeq RNA Sample Preparation
Starting with total RNA, mRNA is polyA-selected and fragmented. It then
undergoes first- and second-strand synthesis to produce products ready
for library construction (Figure 4).
Current
Methods
TruSeq
Methods Savings
No. of Steps 49 18 31
Time (hours) 16 12 25%
Bead cleanup
EtOH cleanup
Column cleanup
mRNA Isolation
22 Steps 10 Steps
Current New
Fragmentation
6 Steps 3 Steps
First Strand Synthesis
13 Steps 3 Steps
Second Strand Synthesis
8 Steps 2 Steps
A. Poly-A selection, fragmentation and random priming
AAAAAAA
TTTTTTT
B. First and second strand synthesis
Table 1: Savings When Processing 96 Samples

175
Sequencing
to the A-tailed fragmented DNA. These newly redesigned adapters
for single, paired-end, and multiplexed reads. This eliminates the need
for additional PCR steps to add the index tag and multiplex primer
sites (Figure 4D). Following the denaturation and amplification steps
(Figure 4E), libraries can be pooled with up to 12 samples per lane
(96 sample per flow cell) for cluster generation on either cBot or the
Cluster Station.
Master-mixed reagents and an optimized protocol improve the library
construction workflow, significantly decreasing hands-on time and
reducing the number of clean-up steps when processing samples for
large-scale studies (Table 1). The simple and scalable workflow allows
for high-throughput and automation-friendly solutions, as well as
simultaneous manual processing for up to 96 samples. In addition,
enhanced troubleshooting features are incorporated into each step
of the workflow, with quality control sequences supported by Illumina
RTA software.
Enhanced Quality Controls
Specific Quality Control (QC) sequences, consisting of double-
stranded DNA fragments, are present in each enzymatic reaction of
the TruSeq sample preparation protocol: end repair, A-tailing, and
ligation. During analysis, the QC sequences are recognized by the RTA
software (versions 1.8 and later) and isolated from the sample data.
The presence of these controls indicates that its corresponding step
was successful. If a step was unsuccessful, the control sequences will
be substantially reduced. QC controls assist in comparison between
experiments and greatly facilitate troubleshooting.
Designed For Automation
The TruSeq Sample Preparation Kits are compatible with high-
throughput, automated processing workflows. Sample preparation can
be performed in standard 96-well microplates with master-mixed re-
agent pipetting volumes optimized for liquid-handling robots. Barcodes
on reagents and plates allow end-to-end sample tracking and ensure
that the correct reagents are used for the correct protocol, mitigating
potential tracking errors.
Part of an Integrated Sequencing Solution
Samples processed with the TruSeq Sample Preparation Kits can be
amplified on either the cBot Automated Cluster Generation System
or the Cluster Station and used with any of Illumina’s next-generation
sequencing instruments, including HiSeq™ 2000, HiSeq 1000,
HiScan™SQ, Genome AnalyzerIIx (Figure 5).
Summary
Illumina’s new TruSeq Sample Preparation Kits enable simplic-
ity, convenience, and affordability for library preparation. Enhanced
multiplexing with 24 unique indexes allows efficient high-throughput
processing. The pre-configured reagents, streamlined workflow, and
automation-friendly protocol save researchers time and effort in their
next-generation sequencing pursuits, ultimately leading to faster dis-
covery and publication.
Learn more about Illumina’s next-generation sequencing solutions at
www.illumina.com/sequencing.
Figure 4: Adapter Ligation Results in Sequence-Ready
Constructs without PCR
Library construction begins with either fragmented genomic DNA or double-
stranded cDNA produced from total RNA (Figure 4A). Blunt-end fragments
are created (Figure 4B) and an A-base is then added (Figure 4C) to prepare
for indexed adapter ligation (Figure 4D). Final product is created (Figure 4E),
which is ready for amplification on either the cBot or the Cluster Station.
E. Denature and amplify for final product
Rd2 SP’
D. Ligate index adapter
Rd1 SP
P5
P7
Index Rd2 SP
Ai. Fragment genomic DNA
C. A-tailing
P
P
A
A
P
P
B. End repair and phosphorylate
+
P
A
T
P
Rd1 SP
P5 P7
Index
Rd2 SP
Rd1 SP
P5P7
Index
Rd2 SP
P7’
5’
5’
A
P
Aii. Double-stranded cDNA
(from figure 2B)
P
P

176
Sequencing
Illumina, Inc.
Illumina, illuminaDx, BeadArray, BeadXpress, cBot, CSPro, DASL, Eco, Genetic Energy, GAIIx, Genome Analyzer, GenomeStudio, GoldenGate,
HiScan, HiSeq, Infinium, iSelect, MiSeq, Nextera, Sentrix, Solexa, TruSeq, VeraCode, the pumpkin orange color, and the Genetic Energy
streaming bases design are trademarks or registered trademarks of Illumina, Inc. All other brands and names contained herein are the property of
their respective owners.
Pub. No. 970-2009-039 Current as of 27 April 2011
Figure 5. Illumina’s Complete Sequencing Solution
Cluster Station cBot
TruSeq Sample Preparation
Genome AnalyzerIIx HiSeq 2000/1000, HiScanSQ
Genome AnalyzerIIx
The TruSeq Sample Preparation Kits readily fit in with Illumina’s advanced
next-generation sequencing solutions.
Product Catalog No.
For RNA Preparation
TruSeq RNA Sample Preparation Kit v2, Set A
RS-122-2001
TruSeq RNA Sample Preparation Kit v2, Set B
RS-122-2002
For DNA Preparation
TruSeq DNA Sample Preparation Kit v2, Set A
FC-121-2001
TruSeq DNA Sample Preparation Kit v2, Set B
FC-121-2002
HiSeq 2000/1000 and HiScanSQ
TruSeq Paired-End Cluster Kit v3—cBot—HS
(1 flow cell)
PE-401-3001
TruSeq Single-Read Cluster Kit v3—cBot—HS
(1 flow cell)
GD-401-3001
Genome AnalyzerIIx
TruSeq Paired-End Cluster Kit v2—cBot—GA
(1 flow cell)
PE-300-2001
TruSeq Single-Read Cluster Kit v2—cBot—GA
(1 flow cell)
GD-300-2001
For Cluster Generation on the Cluster Station and Sequencing
on the Genome AnalyzerIIx
TruSeq Paired-End Cluster Kit v5—CS—GA
(1 flow cell)
PE-203-5001
TruSeq Single-Read Cluster Kit v5—CS—GA
(1 flow cell)
GD-203-5001

177
Highlights
• Precise Measurement of Strand Orientation
Enables detection of antisense transcription, enhances
transcript annotation, and increases alignment efficiency
• Unparalleled Coverage Quality
High coverage uniformity enables most accurate and
complete mapping of alternative transcripts and gene fusions
• Configurations Compatible with Many Sample Types
Including Low-Quality, FFPE, and Blood Samples
Leverage the power of RNA-Seq for previously
inaccessible samples
Introduction
RNA sequencing (RNA-Seq) is a powerful method for discovering,
profiling, and quantifying RNA transcripts. Using Illumina next generation
sequencing technology, RNA-Seq does not require species- or
transcript-specific probes, meaning the data are not biased by previous
assumptions about the transcriptome. RNA-Seq enables hypothesis-
free experimental designs of any species, including those with poor
or missing genomic annotation. Beyond the measurement of gene
expression changes, RNA-Seq can be used for discovery applications
such as identifying alternative splicing events, gene fusions, allele-
specific expression, and examining rare and novel transcripts.
As the complexities of gene regulation become better understood, a
need for capturing additional data has emerged. Stranded information
identifies from which of the two DNA strands a given RNA transcript
was derived. This information provides increased confidence in
transcript annotation, particularly for non-human samples. Identifying
strand origin increases the percentage of alignable reads, reducing
sequencing costs per sample. Maintaining strand orientation also
allows identification of antisense expression, an important mediator of
gene regulation1
. The ability to capture the relative abundance of sense
and antisense expression provides visibility to regulatory interactions
that might otherwise be missed.
As the important biological roles of noncoding RNA continue to be
recognized, whole-transcriptome analysis, or total RNA-Seq, provides
a broader picture of expression dynamics. Total RNA-Seq enabled
by ribosomal RNA (rRNA) reduction is compatible with formalin-fixed
paraffin embedded (FFPE) samples, which contain potentially critical
biological information. The family of TruSeq Stranded Total RNA
sample preparation kits provides a unique combination of unmatched
data quality for both mRNA and whole-transcriptome analyses, robust
interrogation of both standard and low-quality samples and workflows
compatible with a wide range of study designs (Figure 1).
Effective Ribosomal Reduction
TruSeq Stranded Total RNA kits couple proven ribosomal reduction
and sample preparation chemistries into a single, streamlined workflow.
Unlike polyA-based capture methods, Ribo-Zero kits remove ribosomal
RNA (rRNA) using biotinylated probes that selectively bind rRNA
species. The probe:rRNA hybrid is then captured by magnetic beads
and removed, leaving the desired rRNA-depleted RNA in solution.
This process minimizes ribosomal contamination and maximizes the
percentage of uniquely mapped reads covering both mRNA and a broad
range of non-coding RNA species of interest, including long intergenic
noncoding RNA (lincRNA), small nuclear (snRNA), small nucleolar
(snoRNA), and other RNA species2
.
High Quality Stranded Information
TruSeq Stranded RNA kits deliver unmatched data quality. The stranded
measurement, or the percentage of uniquely mapped reads that return
accurate strand origin information based on well-characterized universal
human reference (UHR) RNA, is ≥ 99% using Stranded mRNA and
≥ 98% using Stranded Total RNA. This highly accurate information
serves to increase the percentage of uniquely alignable reads in the
assembly of poorly annotated transcriptomes and provides sensitivity
to detect antisense expression. Consistent, precise measurement of
RNA abundance is reflected by high reproducibility between technical
replicates (Figure 2, R2
= 0. 9873).
TruSeq®
Stranded mRNA and Total RNA
Sample Preparation Kits
The clearest and most complete view of the transcriptome with a streamlined, cost efficient,
and scalable solution for mRNA or whole-transcriptome analyses.
Figure 1: TruSeq Stranded RNA Sample
Preparation Kits
The TruSeq Stranded mRNA and Total RNA Kits allow robust interroga-
tion of both standard and low-quality samples, and include workflows
compatible with a wide range of study designs.

178
TruSeq Total RNA for Low-Quality Samples
TruSeq Total RNA enables robust and efficient interrogation of FFPE
and other low-quality RNA samples. As shown in Figure 3, coverage
across transcripts is high and even in both fresh-frozen (FF) and
FFPE samples prepared with the TruSeq Stranded Total RNA kit. The
optimized Ribo-Zero™ rRNA removal workflow provides a viable,
highly scalable solution for efficient whole transcriptome analysis
across samples that have been historically difficult to analyze.
RNA Analysis of Blood Samples
TruSeq Stranded Total RNA kits with Ribo-Zero Globin enable the
efficient, robust interrogation of coding and noncoding RNA isolated
from blood samples. A streamlined, automation-friendly workflow
applies Ribo-Zero chemistry to simultaneously remove globin mRNA
along with both cytoplasmic and mitochondrial rRNA in a single, rapid
step (Table 1). In comparison to library preparation after ribosomal
RNA reduction only, TruSeq Stranded Total RNA kits with Ribo-Zero
Globin reduced globin mRNA levels generated from commercially
obtained, blood-derived RNA from 28% to only 0.3% of aligned
reads. These kits combine globin mRNA removal, rRNA removal, and
library preparation to optimize sequencing output while reducing total
assay time, eliminating the need for additional removal chemistry and
reducing costs per sample.
Differential Expression of Noncoding RNA
Maintaining strand information of RNA transcripts is important
for many reasons. The example in Figure 4 shows a differentially-
expressed transcript of the ATP5H gene in breast tumor and normal
tissue prepared using the TruSeq RNA with Ribo-Zero compared to a
standard polyA-based method. Both TruSeq Stranded Total RNA and
polyA-prepared samples detect the differential expression of ATP5H
between tumor and normal samples. However, using the Stranded
Total RNA sample preparation kit, differential expression in reverse
orientation at the position of pseudogene transcript AC087651.1
is also detected in the expected, opposite strand orientation.
The example in Figure 5 shows that TruSeq Stranded Total RNA
enables reliable detection of differential expression across multiple
forms of ncRNA, including lincRNA, snRNA, snoRNA, and other
RNA species.
Figure 2: Technical Replicates
FFPE Normal 2
0.9783
FFPENormal1
0 2 4 6 8 10
0
2
4
6
8
10
Technical replicates of FFPE tissue show high concordance, indicating
robust sample prep performance. Axes are log2(FPKM). R2
value is shown.
Figure 3: Even Coverage Across Transcripts
0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
0 10 20 30 40 50 60 70 80 90 100
0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
0 10 20 30 40 50 60 70 80 90 100
Tumor Normal
Coverage%Coverage%
Position
FF Sample
FFPE Sample
Position
TruSeq Stranded Total RNA gives excellent coverage across the top 1,000
expressed transcripts in both fresh-frozen (FF, top) and FFPE (bottom)
tumor and matched normal breast tissue, with 98% aligned stranded
reads. X-axis: position along transcript, Y-axis = percent coverage of
combined reads.

179
Figure 4: Differential Expression of ncRNA Transcripts
Chromosome 17
73.038 mb 73.0385 mb 73.039 mb
ATP5H
ATP5H
RefSeq
Ensembl
0
500
1000
1500
RZNormal
0
500
1000
1500
RZTumor
0
500
1000
1500
PolyANormal
0
500
1000
1500
PolyATumor
AC087651.1
ATP5H expression from chromosome 17 is differentially expressed in breast tumor vs. normal tissue. Using two different sample preparation methods
(RZ; Ribo-Zero for total RNA or PolyA-based mRNA) shows differential expression in tumor vs. normal tissues in both preps (Blue). However, only Total RNA
with Ribo-Zero reveals differential expression at the locus of a pseudogene (Red, AC087651.1), for which reads are detected in the opposite orientation, as
expected. This stranded information would have been lost in a standard mRNA prep.

180
Figure 5: Detection of ncRNA Expression
−5 0 5 10 15
−5051015
Ribo-Zero Normal FPKM
Ribo-ZeroTumorFPKM
lincRNA
misc RNA
snoRNA
snRNA
With TruSeq Stranded Total RNA sample preparation, differential
expression across a range of non-coding RNA species, including long
intergenic noncoding RNA (lincRNA), small nuclear (snRNA) and small
nucleolar (snoRNA) and other species (misc RNA) can be detected
between tumor and normal tissues (four replicates per sample, false
discovery rate (FDR) = 0.05).
Table 1: Targeted RNA Species
Kit Name Cytoplasmic rRNA Mitochondrial rRNA Globin mRNA
TruSeq Stranded Total RNA Sample Preparation Kit
with Ribo-Zero Human/Mouse/Rat
Targeted Not targeted Not targeted
with Ribo-Zero Gold
Targeted Targeted Not targeted
with Ribo-Zero Globin
Targeted Targeted Targeted
Several TruSeq Stranded Total RNA with Ribo-Zero kit configurations are available to suit a range of study designs, providing highly efficient removal of cytoplasmic rRNA,
cytoplasmic and mitochondrial rRNA, or both forms of rRNA in addition to globin mRNA.
Flexible Workflow Configurations
The TruSeq Stranded mRNA and Total RNA kits offer solutions
optimized for your individual experimental needs. Each kit includes two
workflows: the high throughput protocol is ideally suited for projects
with ≥ 48 samples, and the low throughput protocol is best suited for
projects with ≤ 48 samples. Stranded Total RNA configurations are
available for targeting the removal of either cytoplasmic rRNA only, or
both cytoplasmic plus mitochondrial rRNA (Table 2). In a comparison
using Universal Human Reference RNA, TruSeq Stranded Total RNA kits
with Ribo-Zero Human/Mouse/Rat and Gold both reduced cytoplasmic
rRNA to 2% of aligned reads, whereas those with Ribo-Zero Gold
additionally reduced mitochondrial rRNA from 7% to only 0.02% of
aligned reads.
Conclusion
TruSeq Stranded mRNA sample prep kits provide the clearest,
most complete view of the transcriptome, providing precise
measurement of strand orientation, uniform coverage, and high-
confidence discovery of features such as alternative transcripts,
gene fusions, and allele-specific expression. TruSeq Stranded Total
RNA kits couple all of the benefits of TruSeq RNA preparation kits
with Ribo-Zero ribosomal reduction chemistry, providing a robust
and highly scalable end-to-end solution for whole-transcriptome
analysis compatible with a wide range of samples, including non-
human and FFPE.
References
1. Nagai K, Kohno K, Chiba M, Pak S, Murata S, et al. (2012) Differential
expression profiles of sense and antisense transcripts between HCV-associated
hepatocellular carcinoma and corresponding non-cancerous liver tissue.
Int J Oncol 40(6):1813–20.
2. Ribo-Zero Gold Kit: Improved RNA-Seq results after removal of cytoplasmic
and mitochondrial ribosomal RNA. Nature Methods Application Note, 2011.

181
TruSeq®
Targeted RNA Expression
Highly customizable and affordable mid-plex gene expression analysis for the MiSeq®
system.
Introduction
TruSeq Targeted RNA Expression leverages proven MiSeq sequencing
technology to deliver an accurate and powerful method for validating
gene expression arrays and RNA-Seq studies. TruSeq Targeted RNA
Expression (Figure 1) enables efficient, quantitative multiplexed gene
expression profiling for 12-1,000 targets per sample and up to 384
samples in a single MiSeq run. Requiring just 50 ng or less of starting
RNA, TruSeq Targeted RNA Expression is amenable to a wide range
of samples. Choose from over 400,000 pre-designed assays to create
a custom panel targeting genes, exons, splice junctions, cSNPs and
fusions. Fixed panels offer a wide variety of biological pathways and
disease-specific markers, or combine fixed and custom content for
the ultimate in flexibility. TruSeq Targeted RNA Expression offers a
fully integrated solution, including convenient online assay design and
ordering, a streamlined workflow, and automated, on-instrument
data analysis.
Choose Fixed Panels for Focused Studies
For pathway- or disease-focused expression or profiling studies,
TruSeq Targeted RNA Expression fixed panels offer ready-to-use
assays designed for commonly studied genes (Table 1). Validated,
fixed content panels are ideal for profiling many samples or screening
cell types quickly and economically, and providing base content that
can be expanded upon with custom content as needed.
Increase Your Flexibility with Custom Content
TruSeq Targeted RNA Expression assays are pre-designed assays
targeting exon junctions and non-junction sites, as well as target SNPs
within coding regions. Choose validated assays in DesignStudioTM
,
a free, user-friendly tool accessed through your MyIllumina account1
.
Highlights
• Content and flexibility with fixed and customizable
panels
Choose validated pathway, cell, or disease-specific fixed
panels, or add customized content
• Mid-plex gene expression at a complexity and scale not
previously possible
Examine 1,000 targets per sample, 384 samples per run
• Fast and simple workflow
Go from RNA to data in less than two days
Figure 1: TruSeq Targeted RNA Expression
TruSeq Targeted RNA Expression delivers fixed or customizable affordable
mid-plex gene expression that takes full advantage of the throughput and
flexibility of the MiSeq®
system.
Table 1: TruSeq RNA Expression Fixed Panels
Apoptosis Hedgehog Pathway TP53 Pathway
Cardiotoxicity Neurodegeneration Wnt Pathway
Cell Cycle NFκB Pathway
Cytochrome P450 Stem Cell
Create fully custom panels of 12–1,000 assays, or add specific
genes or regions to one of the fixed panels, or to a previously ordered
custom panel. Simply select the assays you need and add them to
your order, with no design time.
Streamlined, Targeted Assay Workflow
TruSeq Targeted RNA Expression for custom or fixed designs features
a simple method for generating indexed, sequence-ready libraries from
RNA regions of interest (Figure 2). Starting with as little as 50 ng of
total RNA, the small amplicon size allows successful target detection,
even on poor quality samples. All targets are amplified in a single
reaction, minimizing potential bias and workflow steps compared to
methods such as qPCR. From sample to data analysis, the entire
process takes less than two days.

182
Multiplexing at a Scale not Previously Possible
With TruSeq Targeted RNA Expression, you can run up to 384 dual-
index combinations to efficiently multiplex samples within a single
MiSeq run. With 25 million reads, the MiSeq system is capable of
generating 25,000 datapoints per run (at an average of 1,000 reads
per target), equivalent to 65 384-well plates. Compared to qPCR,
the number of runs and amount of processing time is significantly
decreased (Figure 3). For more information about read budget,
normalization, and getting the best results from your TruSeq Targeted
RNA Expression assays, refer to the technical note2
.
Accurate Confirmation Using TruSeq Targeted
RNA Expression
TruSeq Targeted RNA Expression was compared against the gold
standard RNA-Seq for fold-change in an experimental target set. As
shown in Figure 4, fold-change expression in 281 targets between
Universal Human Reference (UHR) RNA and total brain mRNA was
measured using TruSeq Targeted RNA Expression (X-axis) and
TruSeq Stranded RNA-Seq (Y-axis). Data show excellent correlation,
demonstrating that TruSeq Targeted RNA Expression provides
accurate validation. The assay is also highly reproducible, even over
a large dynamic range (Figure 5).
Figure 2: TruSeq Targeted RNA Expression Workflow
The TruSeq Targeted RNA Expression assay chemistry begins with
reverse transcribing cDNA from purified total RNA. Two custom-
designed oligonucleotide probes with adapter sequences hybridize up
and downstream of the region of interest. An extension-ligation reaction,
followed by amplification creates a new template strand. Templates are then
PCR amplified to add indices, creating sequence-ready libraries.
Figure 4: Fold-Change Correlation between RNA-Seq
and TruSeq Targeted RNA Expression
y = 0.9427x- -0.8679
R² = 0.9608
5
5-5
-5
10
10 15
15
-15 -10
-10
-15
TruSeq Stranded RNA UHR vs. Brain Log2 Fold-change
TruSeqRNAExpressionUHRvs.BrainLog2Fold-change
Comparison of fold change expression between Universal Human
Reference (UHR) and brain mRNAs for 281 targets, using TruSeq
Stranded RNA-Seq (X-axis) and TruSeq RNA Expression (Y-axis).
Figure 3: TruSeq Targeted RNA vs qPCR Workflow
1 run, 2 days
1 run, 2 days
13 runs, 4 days
63 runs, ~16 days
0 25 50
100 targets
500 targets
Days
180 9
Runs
TruSeq RNA Expression, 48 samples
qPCR, 48 samples
75
With TruSeq RNA Expression, run 500 targets on 48 samples in one run in
less than two days, compared to 63 runs in ~16 days with qPCR methods.

183
Simple Data Analysis
After a sequencing run on the MiSeq system, data are automatically
aligned and can be viewed using the MiSeq Reporter. As shown
in Figure 5, pairwise comparisons for relative expression between
samples or groups of samples is simple and intuitive. Customizable
significance thresholds allow you to quickly identify differentially
expressed targets. The TruSeq Targeted RNA Expression user
experience is customized and streamlined, and keeps project data
highly accessible.
Summary
Designed for the MiSeq system, TruSeq Targeted RNA Expression
provides rapid and economical RNA profiling and validation for your
gene expression studies. Go from sample to answer in less than
two days with a simple, streamlined workflow and automated data
visualization. Choose validated, pre-designed panels or add custom
content to your existing assays for the ultimate flexibility to evolve
your research.
References
1. https://icom.illumina.com/
2. Considerations for Designing a Successful TruSeq Targeted RNA Expression
Experiment Technical Note, 2013.
Product Specifications
Specification Value
Database content
400,000 designs
(mouse, human, rat)
Target types
Gene, transcript, exon,
splice junction, cSNP, fusion
Dynamic range 5 orders of magnitude
Time to answer 1.5 days
Hands-on time 4 hours
RNA quality 200 bp unfixed or FFPE
Figure 5: Visualization of TruSeq Targeted RNA Expression Data using MiSeq Reporter
Data visualization with MiSeq Reporter allows easy comparison of data sets.

184
© 2012-2013 Illumina, Inc. All rights reserved.
Genome Analyzer, GenomeStudio, GoldenGate, HiScan, HiSeq, Infinium, iSelect, MiSeq, Nextera, Sentrix, SeqMonitor, Solexa,
Pub. No. 470-2012-004 Current as of 14 August 2013
Product Name
Number of
Samples Catalog No.
TruSeq Targeted RNA Expression Custom Components
TruSeq Targeted RNA Custom Kit
48 RT-101-1001
96 RT-102-1001
TruSeq Targeted RNA Supplemental Content
48 RT-801-1001
96 RT-802-1001
TruSeq Targeted RNA Expression Fixed Panels
TruSeq Targeted RNA Apoptosis Panel Kit
48 RT-201-1010
96 RT-202-1010
TruSeq Targeted RNA Cardiotoxicity Panel Kit
48 RT-201-1009
96 RT-202-1009
TruSeq Targeted RNA Cell Cycle Panel Kit
48 RT-201-1003
96 RT-202-1003
TruSeq Targeted RNA Cytochrome p450 Panel Kit
48 RT-201-1006
96 RT-202-1006
TruSeq Targeted RNA Hedgehog Panel Kit
48 RT-201-1002
96 RT-202-1002
TruSeq Targeted RNA Neurodegeneration Panel Kit
48 RT-201-1001
96 RT-202-1001
TruSeq Targeted RNA NFκB Panel Kit
48 RT-201-1008
96 RT-202-1008
TruSeq Targeted RNA Stem Cell Panel Kit
48 RT-201-1005
96 RT-202-1005
TruSeq Targeted RNA TP53 Pathway Panel Kit
48 RT-201-1007
96 RT-202-1007
TruSeq Targeted RNA Wnt Pathway Panel Kit
48 RT-201-1004
96 RT-202-1004
TruSeq Targeted RNA Expression Index Kits
TruSeq Targeted RNA Index Kit 48 RT-401-1001
TruSeq Targeted RNA Index Kit A 96 RT-402-1001
TruSeq Targeted RNA Index Kit B 96 RT-402-1002
TruSeq Targeted RNA Index Kit C 96 RT-402-1003
TruSeq Targeted RNA Index Kit D 96 RT-402-1004

185
ScriptSeq™
Complete Gold Kit (Blood)
Blood is an important sample for research into 6,000 rare
diseases and 12,000 disease groups. The data from samples
treated with ScriptSeq Complete Gold (Blood) is focused
on valuable RNA. Finding new genes, splice variants and
isoforms is important to disease and health research.
Find more coding and non-coding RNA
ScriptSeq Complete Gold (Blood) libraries were prepared
from 5 µg of RNA isolated from human whole blood and
sequenced on an Illumina® sequencer. Greater than 98% of
all reads contain useful information.
RNA-Seq data is very useful to study disease (or health).
Figure 1 shows an example in which 47% of the sequencing
reads contain coding RNA and 52% contain non-coding RNA.
ScriptSeq™ Complete Gold (Blood)
ScriptSeq Complete (Blood) offers the most informative
sequencing results by removing unwanted globin mRNA
and ribosomal RNA prior to sequencing.
Figure 1. RNA-Seq libraries contain coding
and non-coding RNA.
47.26%
55.94%
Cytoplasmic rRNA
0.21% Mitochondrial rRNA
0.02%
Globin mRNA
0.002%
Cytoplasmic rRNA
0.03%
Globin mRNA
0.004%
40.26%
25.54%
12.26%
19.23%
Intronic
Intergenic
mRNA (coding + UTR)
Cytoplasmic rRNA
Mitochondrial rRNA
Intronic Intergenic mRNA (coding + UTR) Cytoplasmic rRNA Mitochondrial rRNA
47.26%
55.94%
Cytoplasmic rRNA
0.02%
Globin mRNA
0.002%
Cytoplasmic rRNA
0.03%
Globin mRNA
0.004%
40.26%
25.54%
12.26%
19.23%
Intronic
Intergenic
mRNA (coding + UTR)
Cytoplasmic rRNA
Mitochondrial rRNA
Intronic Intergenic mRNA (coding + UTR) Cytoplasmic rRNA Mitochondrial rRNA
RNA-Seq of Blood
„ Removes globin mRNA and ribosomal RNA
„ Creates an Illumina® sequencing library
„ The data contains high amounts of coding and
non-coding information
„ Find more genes
„ Find more coding and non-coding RNA’s
„ Good for small samples
„ All phases of research
Workflow
6 Hrs – Depletion + Library Prep2 Hrs – Purification
ScriptSeq Complete™MasterPure™Blood

186
Figure 2. Gene coverage from large (5 μg) or small (100 ng) of RNA.
Available for all sample sizes
ScriptSeq Complete Gold (Blood) is available for 100 ng + of
total RNA. Results from small amounts of total RNA are very
similar to results from high amounts of total RNA. Figure 2
shows coverage of the COX5B gene when either a small
amount (100 ng) of total RNA or large amount (5 μg) of total
RNA was treated with ScriptSeq Complete Gold (Blood).
Strong gene coverage
In figure 2, the height of the blue bars show how many reads
align to that sequence. Taller bars show more reads and
deeper (better) coverage. Coding (thick blue bars) regions in
both the small and large input ranges is similar.
MasterPure RNA purification kit
pure, intact total RNA.
Cat. # Quantity
Total RNA
Total RNA
Cat. # Quantity
ScriptSeq™ Complete Gold Kit (Blood)—Low Input
SCL24GBL 24 Reactions
SCL6GBL 6 Reactions
For 100 ng – 1 µg total blood RNA.
ScriptSeq™ Complete Gold Kit (Blood)
BGGB1306 6 Reactions
BGGB1324 24 Reactions
For 1 µg – 5 µg total blood RNA.
FSE51100 100 Units
Patents: www.illumina.com/patents

187
ScriptSeq™
Complete Gold Kit (Blood)
The yeast transcriptome is more complex than previously
thought. RNA-Seq of yeast is a valuable approach for
mapping the transcriptome and characterizing novel and
Find more coding and non-coding RNA
ScriptSeq Complete Gold Kit (Yeast):
„ Find more coding RNA
„ Removes ribosomal RNA
„ Creates Illumina® sequencing libraries
„ Data contains high amounts of coding information
RNA-Seq data is very useful to study yeast gene expression.
Figure 1 shows an example in which 95.6 % of the
sequencing reads contain coding RNA and 4.4 % contain
non-coding RNA.
low abundance transcripts. The ScriptSeq Complete Gold
Kit (Yeast) offers the most informative sequencing results by
removing unwanted ribosomal RNA prior to sequencing.
Figure 1. RNA-Seq libraries contain coding and
non-coding RNA.
RNA-Seq of Yeast
„ Removes ribosomal RNA with Ribo-Zero™
„ Creates an Illumina® sequencing library with ScriptSeq v2
„ Results contain coding and non-coding RNA
„ One day method
„ Find more genes
„ Good for small samples
Library composition of ScriptSeq™ Complete Gold Kit (Yeast) samples.
ScriptSeq libraries were constructed from 1 µg of S. cereviseae total
RNA samples and sequenced on an Illumina® MiSeq™.
Coding
UTR
Intergenic
Other
86.8%
8.8%
4.4%
6 Hrs – Depletion + Library Prep2 Hrs – Purification
ScriptSeq Complete™MasterPure™Yeast
ScriptSeq™ Complete Gold Kit (Yeast)
Workflow

188
Figure 2. Enhanced coverage with ScriptSeq™ Complete Gold Kit (Yeast).
Cat. # Quantity
Ribo-Zero™ Magnetic Gold Kit (Yeast)
Suitable for 1-5 μg of total RNA.
MRZY1306 6 Reactions
MRZY1324 24 Reactions
ScriptSeq™ Complete Gold Kit (Yeast)
Includes Ribo-Zero Gold (Yeast). Suitable for 1-5 μg of total RNA.
BGY1306 6 Reactions
BGY1324 24 Reactions
ScriptSeq™ Complete Gold Kit (Yeast)- Low Input
Includes Ribo-Zero Gold (Yeast). Suitable for 100 ng - 1 μg of total RNA.
SCGL6Y 6 Reactions
SCGL6Y 24 Reactions
FSE51100 100 Units
The FailSafe PCR Enzyme Mix is required for ScriptSeq Complete
Gold (Yeast) RNA-Seq library preparation.
MasterPure™ RNA Purification Kit
Purification is the first critical step to prepare samples for
sequencing. MasterPure produces sequencer-ready RNA
safely and easily.
Cat. # Quantity
Enhanced coverage with ScriptSeq Complete
Gold Kit (Yeast)
ScriptSeq Complete Gold (Yeast) contains Ribo-Zero Gold
(Yeast) for depletion of yeast rRNA. Gene coverage of the
TEF2 gene (Figure 2) shows that rRNA depletion reveals more
reads. In the figure, the height of the blue bars shows how
many reads align to that sequence. Taller bars show more
reads and deeper (better) coverage.
ScriptSeq Yeast is a powerful tool to study:
„ Transcriptome mapping
„ Gene structure
„ Characterization of novel and low abundance transcripts

189
ARTseq™
Sequencing actively translated transcripts
Sequence mRNA fragments undergoing translation by
ribosomes. These mRNA fragments are called“footprinted”or
ribosome protected mRNA fragments.
Sequence only the protein coding
regions
Samples prepared with ARTseq are
enriched for ORF and devoid of UTR
sequences. The start and stop codons
are easily seen. Sequences are focused
on protein coding regions.
Ribosome Profiling
ARTseq™ Ribosome Profiling Kits
ARTseq (Active mRNA Translation) Ribosome profiling is a
powerful technique to study translation.
„ Sequence ribosome protected mRNA
„ Rapid, scalable spin-column method
„ No ultracentrifuge required!
„ Compatible with yeast and mammalian samples
„ Predict protein abundance
„ Investigate translational control
„ Measure gene expression
Figure 1. Identify actively translated RNA’s with ARTseq.
Library Prep Depletion 2 hrs
ARTseq™ARTseq™ Ribo-Zero™
2-Day Simple Method
Yeast or Mammalian Sample
Workflow
ARTseq sequences ribosome-protected mRNA fragments
to provide a“snapshot”of the active ribosomes in a cell.You
can identify proteins being actively translated from samples
prepared with ARTseq. Samples collected at different times
often show changes in translation. Samples treated with
different drugs often show different translation patterns.

Data Sheet: Epigenetics
HumanMethylation450 BeadChip Highlights
• Unique Combination of Genome-Wide Coverage,
High-Throughput, and Low Cost
Over 450,000 methylation sites per sample at single-
nucleotide resolution
• Unrivaled Assay Reproducibility
98% reproducibility for technical replicates
• Simple Workflow
PCR-free protocol with the powerful Infinium HD Assay
• Compatibile with FFPE Samples
Protocol available for methylation studies on FFPE samples
Introduction
DNA methylation plays an important and dynamic role in regulating
gene expression. It allows cells to become specialized and stably
maintain those unique characteristics throughout the life of the
organism, suppresses the deleterious expression of viral genes
and other non-host DNA elements, and provides a mechanism for
response to environmental stimuli. Aberrant DNA methylation (hyper-
or hypomethylation) and its impact on gene expression have been
implicated in many disease processes, including cancer1
.
To enable cost-effective DNA methylation analysis for a variety of applica-
tions, Illumina offers a robust methylation profiling platform consisting of
proven chemistries and the iScan and HiScan®
SQ systems. The Human-
Methylation450 BeadChip (Figure 1) offers a unique combination of
comprehensive, expert-selected coverage and high throughput at a
low price, making it ideal for screening large sample populations such
as those used in genome-wide association study (GWAS) cohorts. By
providing quantitative methylation measurement at the single-CpG–site
level for normal and formalin-fixed parafin-embedded (FFPE) samples,
this assay offers powerful resolution for understanding epigenetic
changes.
Comprehensive Genome-Wide Coverage
The Infinium HumanMethylation450 BeadChip provides unparalleled,
genome-wide coverage featuring comprehensive gene region and
CpG island coverage, plus additional high-value content selected
with the guidance of methylation experts. Infinium HD technology
enables content selection independent of bias-associated limitations
often associated with methylated DNA capture methods. As a result,
99% of RefSeq genes are covered, including those in regions of low
CpG island density and at risk for being missed by commonly used
capture methods.
Importantly, coverage was targeted across gene regions with sites in
the promoter region, 5'UTR, first exon, gene body, and 3'UTR in order
to provide the broadest, most comprehensive view of methylation
state possible (Figure 2). This multiple-site approach was extended
to CpG islands/CpG island regions for which 96% of islands were
covered overall, with multiple sites within islands and island shores, as
well as those regions flanking island shores (island shelves). Beyond
gene and CpG island regions, multiple additional content categories
requested by methylation experts were also included:
• CpG sites outside of CpG islands
• Non-CpG methylated sites identified in human stem cells
• Differentially methylated sites identified in tumor versus normal
(multiple forms of cancer) and across several tissue types
• FANTOM 4 promoters
• DNase hypersensitive sites
• miRNA promoter regions
• ~ 90% of content contained on the Illumina HumanMethylation27
BeadChip
Streamlined Workflow
The HumanMethylation450 BeadChip follows a user-friendly,
streamlined workflow that does not require PCR. Its low sample input
requirement (as low as 500 ng), enables analysis of valuable samples
Infinium®
HumanMethylation450 BeadChip
The ideal solution for affordable, large sample–size genome-wide DNA methylation studies.
Figure 1: Infinium HumanMethylation450 BeadChip
The Infinium HumanMethylation450 BeadChip features more than 450,000
methylation sites, within and outside of CpG islands.
4305493023
®
190
ARRAYS
Infinium HumanMethylation450 BeadChip

derived from limited DNA sources. HumanMethylation450 BeadChip
kits contain all required reagents for performing methylation analyses
(except for the bisulfite conversion kit, which is available separately).
Data Integration
Of all the genes represented on the HumanMethylation450 BeadChip, more
than 20,000 are also present on the HumanHT-12 v4 Expression BeadChip2
,
permitting combined analysis of global methylation status and gene
expression levels. In addition, investigators may integrate methylation
data with genotyping data from GWAS studies to better understand
the interplay between genotype and methylation state in driving phe-
notypes of interest.
High-Quality Data
The HumanMethylation450 BeadChip applies both Infinium I and II
assay chemistry technologies (Figure 3) to enhance the depth of cover-
age for methylation analysis. The addition of the Infinium II design allows
use of degenerate oligonucleotide probes for a single bead type, en-
abling each of up to three underlying CpG sites to be either methylated
or unmethylated with no impact on the result for the queried site.
Illumina scientists rigorously test every product to ensure strong and
reproducible performance, enabling researchers to achieve industry-
leading data quality.
Precision and Accuracy
Reproducibility has been determined based on the correlation of
results generated from technical replicates. The HumanMethylation450
BeadChip showed strong correlation between replicates (r0.98), as
well as with the HumanMethylation27 BeadChip and whole-genome
bisulfite sequencing (Figure 4).
Sensitivity
By comparing the results of replicate experiments (duplicates of
eight biological samples), Illumina scientists have shown that the
HumanMethylation450 BeadChip reliably detects a delta-beta value
of 0.2 with a lower than 1% false positive rate.
Internal Quality Controls
Infinium HD–based assays possess several sample-dependent and
sample-independent controls so researchers have confidence in pro-
ducing the highest quality data. The HumanMethylation450 BeadChip
includes 600 negative controls, which are particularly important in
methylation analysis assays since sequence complexity is decreased
after bisulfite conversion. The GenomeStudio®
Methylation Module
Software has an integrated Controls Dashboard where the perfor-
mance of all controls can be easily monitored.
Figure 3: Broader Coverage Using Infinium I and II
Assay Designs
The HumanMethylation450 BeadChip employs both Infinium I and
Infinium II assays, enhancing its breadth of coverage. Infinium I assay
design employs two bead types per CpG locus, one each for the
methylated and unmethylated states. The Infinium II design uses one
bead type, with the methylated state determined at the single base
extension step after hybridization.
Unmethylated locus
Infinium I
Infinium II
Methylated locus
Unmethylated locus Methylated locus
Bisulfite converted DNAUnmethylated bead type Methylated bead type CpG locus
CA
GT
CG
GC
CAx
GC
CGx
GT
U
U
MM
M
Bisulfite converted DNASingle bead type CpG locus
U
A
G
C
T
A
G
C
T
A
G
C
T
A
G
C
T
CG
GC
CA
GT
A
G
C
T
A
G
C
T
5’ 5’
5’5’
5’ 5’
Figure 2: HumanMethylation450 BeadChip Provides
Coverage Throughout Gene Regions
5’ UTR Gene body 3’ UTRTSS1500 TSS200 1st exon
Feature Type
Genes
Mapped
Percent Genes
Covered
Numberof
Loci on Array
NM_TSS200 14895 0.79 2.56
NM_TS1500 17820 0.94 3.41
NM_5'UTR 13865 0.78 3.34
NM_1stExon 15127 0.80 1.62
NM_3'UTR 13042 0.72 1.02
NM_GeneBody 17071 0.97 8.97
NR_TSS200 1967 0.65 1.84
NR_TSS1500 2672 0.88 2.92
NR_GeneBody 2345 0.77 5.34
N Shelf N Shore S Shore S ShelfCpG Island
Feature Type
Islands
Mapped
Percent
Islands
Covered
Average
Numberof
Loci on Array
Island 26153 0.94 5.08
N_Shore 25770 0.93 2.74
S_Shore 25614 0.92 2.66
N_Shelf 23896 0.86 1.97
S_Shelf 23968 0.86 1.94
The HumanMethylation450 BeadChip offers broad coverage across gene
regions, as well as CpG islands/CPG island regions, shelves, and shores for
the most comprehensive view of methylation state.
191

192
Figure 4: High Assay Reproducibility
A: HumanMethylation450 Replicate Correlation
R2 = 0.9969
HumanMethylation450BeadChip
B: HumanMethylation27 vs. HumanMethylation450 Correlation
HumanMethylation27BeadChip
C. HumanMethylation450 vs. Whole-Genome Bisulfite Sequencing
Array Array
Sequencing
Sequencing
Lung Normal Lung Tumor
R2= 0.92 R2= 0.93
Using the HumanMethylation450 BeadChip, users can be confident
of obtaining consistent, robust data. Representative plots from internal
testing show strong replicate correlation (A), as well as strong correlation
with the HumanMethylation27 BeadChip (B) and whole-genome bisulfite
sequencing (C).
Figure 5: Integrated Data Analysis with Illumina
GenomeStudio Software
H MU CancerNormal
GenomeStudio software supports DNA methylation analysis on any plat-
form. Data are displayed in intuitive graphics. Gene expression data can be
easily integrated with methylation projects (plotted on right).
Table 1: Comparative Infinium HumanMethylation450
Data Quality Metrics—Standard vs. FFPE
HumanMethylation450
BeadChip
Standard
Protocol
FFPE
Protocol
Reproducibility
(Technical replicates)
r2
≥ 98% r2
≥ 98%
Number of sites
detected*
≥ 99% ≥ 95%
*Based on non-cancer samples, recommended sample input amounts
of high-quality DNA as confirmed by PicoGreen and following all other
Illumina recommendations as per respective User Guides.
Integrated Analysis Software
HumanMethylation450 BeadChip data analysis is supported by the
powerful and intuitive GenomeStudio Methylation Module, enabling
researchers to effortlessly perform differential methylation analysis
(Figure 5). The GenomeStudio software features advanced visualiza-
tion tools that enable researchers to view vast amounts of data in a
single graph, such as heat maps, scatter plots, and line plots. These
tools and the GenomeStudio Genome Browser display valuable infor-
mation such as chromosomal coordinates, percent GC, location in a
CpG Island, and methylation β values.
Data generated by the Infinium HD methylation assay are easily
compatible with data from other Illumina applications, including gene
expression profiling. This enables researchers to perform cross-
application analysis such as the integration of gene expression data
with HumanMethylation450 BeadChip methylation data.
Methylation Studies with FFPE Samples
Researchers can perform methylation studies on FFPE samples by
using a special, modified version of the Infinium HumanMethylation450
BeadChip protocol3
that leverages the easy-to-use Infinium FFPE DNA
Restoration Solution4,
to produce robust, highly reproducible results
(Table 1). The FFPE DNA Restoration Solution includes the Illumina
FFPE QC and the Infinium HD FFPE DNA Restore Kits. Please note
that while the FFPE DNA Restoration Solution and HumanMethyl-
ation450 BeadChip kits are the same for normal and FFPE samples,
investigators running FFPE samples should only follow the workflow
described in the Infinium HD FFPE Methylation Assay protocol (manual
or automated)5,6
, as it includes important changes to the standard
protocols for each kit.

193
Illumina • +1.800.809.4566 toll-free • 1.858.202.4566 tel • techsupport@illumina.com • www.illumina.com
FoR RESEARCH USE onLy
Genome Analyzer, GenomeStudio, GoldenGate, HiScan, HiSeq, Infinium, iSelect, MiSeq, Nextera, Sentrix, SeqMonitor, Solexa,
TruSeq, VeraCode, the pumpkin orange color, and the Genetic Energy streaming bases design are trademarks or registered
Summary
The HumanMethylation450 BeadChip’s unique combination of
comprehensive, expert-selected coverage, high sample throughput
capacity, and affordable price makes it an ideal solution for large
sample–size, genome-wide DNA methylation studies.
References
1. Portela A, Esteller M (2010) Epigenetic modifications and human disease.
Nat Biotechnology 28: 1057–1068.
2. http://www.illumina.com/products/humanht_12_expression_beadchip_kits_
v4.ilmn
3. Infinium HD FFPE DNA Restoration Protocol
4. http://www.illumina.com/products/infinium_ffpe_dna_restoration_solution.
ilmn
5. Infinium HD FFPE Methylation Assay, Manual Protocol
6. Infinium HD FFPE Methylation Assay, Automated Protocol
7. Illumina FFPE QC Assay Protocol
Catalog No. Product Description
WG-314-1003 Infinium HumanMethylation450 BeadChip
Kit (24 samples)
Each package contains two BeadChips and reagents for analyzing DNA methylation
in 24 human DNA samples.
Kit (48 samples)
Each package contains four BeadChips and reagents for analyzing DNA methylation
Kit (96 samples)
Each package contains eight BeadChips and reagents for analyzing DNA methylation
Each HumanMethylation450 BeadChip can process 12 samples in parallel and assay 450,000 methylation sites per sample.

194
PCR AND ENZYME SOLUTIONS
FailSafe™
PCR System
The FailSafe PCR System uses the patented Epicentre PCR
enhancement technology to allow PCR reactions to work
the first time and every time. Twelve buffer options run at
the same time, allowing quick and easy optimization of your
PCR.
Works the first time, every time
Optimizing PCR is easy with FailSafe. Create a master mix of
FailSafe Enzyme blend, template DNA and primers. Add the
FailSafe PCR PreMix Selection Kit buffers to test which buffer
is optimal for your reaction.
Figure 1. Ensure successful PCR with FailSafe.
PCR Optimization
FailSafe™ PCR System
„ PCR of difficult or high GC templates
„ PCR Amplifications up to 20 kb
„ Works the first time, every time
„ 3-fold lower error rate than Taq DNA Polymerase
Workflow
FailSafe™
Enzyme Blend
Add your template and primers
FailSafe™
2X PreMixes
Never Fail . . .
F G H K LI JA B C D E
PCR to Select Optimum PreMix J
FailSafe™MasterPure™Sample
FailSafe has enabled many difficult samples to be used
successfully in PCR and published. FailSafe will ensure your
PCR is successful.

195
Figure 2. The FailSafe™ PCR System will work with nearly all DNA – animal, bacterial, plant or viral.
Amplify any sample
FailSafe will amplify DNA sequences from almost any source,
up to 20 kb in length in a single round.
FailSafe PCR products can be used in many applications,
including TA cloning and blunt-end cloning.
FailSafe components (Polymerase and Premixes) are
available in the FailSafe Premix Choice kits and as single
products.
Success begins with MasterPure™ DNA
Purification Kit
MasterPure produces DNA safely and easily to be used in
many applications.
„ Keep DNA intact (does not degrade DNA)
„ Scalable reaction sizes
„ Available for multiple sample types
Cat. # Quantity
MasterPure™ Complete DNA and RNA Purification Kit
Cat. # Quantity
FailSafe PCR PreMix Selection Kit
FS99060 (Contains all 12 Premixes and
FailSafe PCR Polymerase) – sufficient
reagent for 48 reactions
(four full template and primer optimizations)
FailSafe PCR System
FS99100 (100 units of FailSafe Polymerase and
1 PreMix of choice)
FS99250 (250 units of Failsafe Polymerase and
two Premixes of choice)
FS9901K (1000 U of FailSafe Polymerase and
eight PreMixes of choice)
FailSafe PCR Polymerase
FSE51100 (100 U)
FailSafe PCR Polymerase
FSE5101K (1000 U)
FailSafe PCR Premixes
FSP995A-L (A through L), 2.5 ml (100 reactions)
FailSafe™ PCR Premix Selection Kits: Purchase of this product includes an immunity from suit under
patents specified in the product insert to use only the amount purchased for the purchaser’s own internal
research. No other patent rights (such as 5′ Nuclease Process patent rights) are conveyed expressly, by
implication, or by estoppel. Further information on purchasing licenses may be obtained by contacting the
Director of Licensing, Applied Biosystems, 850 Lincoln Centre Drive, Foster City, California 944.
FailSafe PCR will amplify DNA from a range of
different sequences and sequence sizes. PCR
products shown are up to 20 kb for lambda DNA,
up to 21.5 kb for human DNA, and up to 18 kb for
E. coli DNA.

196
Enzyme Solutions
What Will You Create Today?
Innovative Enzyme Solutions
„ Stringent QC
„ No affinity tags
„ ISO 13485 compliant by end of 2013
Example applications:
„ Reverse transcriptases
„ RNA polymerases
„ RNase-free DNase
„ Much more...
Epicentre develops and manufactures the highest purity
enzymes for life science research. Epicentre specializes in
unique and bulk enzyme projects to meet your specific
needs. Since opening in 1987, Epicentre has a proven
track record in manufacturing high purity enzymes for life
sciences in our state-of-the-art facility in Madison, Wisconsin,
USA.
Standard enzymes for molecular biology research are
available. Unique, hard-to-find enzymes are available to your
specifications.
OEM opportunities available
„ Custom manufacturing for alternate size requirements or
specific concentrations
„ In-house technical expertise with over 25 years of
experience
„ Competitive pricing to meet your budget constraints
„ Flexible, quick turnaround time for OEM needs
Bulk availability
„ Standard or custom offerings
„ Flexible (custom) concentrations and package sizes
„ Bulk capabilities

197
Phosphatases/Kinases
APex™ Heat-Labile Alkaline Phosphatase
RNA 5′ Polyphosphatase
T4 Polynucleotide Kinase, Cloned
Browse the possibilities…
DNA Polymerases
Klenow DNA Polymerase
Exo-Minus Klenow DNA Polymerase (D355A, E357A)
RepliPHI™ Phi29 DNA Polymerase
Terminal deoxynucleotidyl Transferase, Recombinant
T4 DNA Polymerase
RNA Polymerases
T7 RNA Polymerase
T7 RDNA™ Polymerase
DNA Endonucleases
Baseline-ZERO™ DNase
Endonuclease IV, E. coli
T4 Endonuclease V
Lambda Terminase
RNase-Free DNase I
Pvu Rts1I Endonuclease
RNA Endonucleases
RNase A
RNase I, E. coli
RNase III, E. coli
RNase H, E. coli
Hybridase™ Thermostable RNase H
RNase T1, Aspergillus oryzae
RiboShredder™ RNase Blend
RNase A
DNA Exonucleases
Exonuclease I, E. coli
Exonuclease III, E. coli
Exonuclease VII
Plasmid-Safe™ ATP-Dependent
DNase
Lambda Exonuclease
RecBCD Nuclease, E. coli
Rec J Exonuclease
T5 Exonuclease
RNA Exonucleases
RNase R
Terminator™ 5′-Phosphate-
Dependent Exonuclease
DNA Ligases
T4 DNA Ligase, Cloned
Ampligase® Thermostable DNA Ligase
CircLigase™ ssDNA Ligase
CircLigase™ II ssDNA Ligase
E. coli DNA Ligase
RNA Ligases
T4 RNA Ligase
T4 RNA Ligase 2, Deletion Mutant
Thermostable RNA Ligase

198
INSTRUMENTS
Illumina, IlluminaDx, BaseSpace, BeadArray, BeadXpress, cBot, CSPro, DASL, DesignStudio, Eco, GAIIx, Genetic Energy, Genome Analyzer,
GenomeStudio, GoldenGate, HiScan, HiSeq, Infinium, iSelect, MiSeq, Nextera, NextSeq, NuPCR, SeqMonitor, Solexa, TruSeq, TruSight,
VeraCode, the pumpkin orange color, and the Genetic Energy streaming bases design are trademarks or registered trademarks of Illumina,
Inc. APex, ARTseq, EpiGnome, FailSafe, MasterPure, Ribo-Zero, ScriptSeq, and TotalScript are trademarks or registered trademarks of
Epicentre (an Illumina company). All other brands and names contained herein are the property of their respective owners.
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Genome Analyzer, GenomeStudio, GoldenGate, HiScan, HiSeq, Infinium, iSelect, MiSeq, Nextera, NuPCR, SeqMonitor,
Solexa, TruSeq, TruSight, VeraCode, the pumpkin orange color, and the Genetic Energy streaming bases design are trademarks
or registered trademarks of Illumina, Inc. All other brands and names contained herein are the property of their respective owners.
Pub No. 073-2014-001 Current as of 29 May 2014

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