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ChipSeq Data Analysis

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COST action BM1006

The document provides an overview of Chip-seq data analysis. It discusses the Chip-seq technology, visualization of genomic data, command line analysis including quality checking, alignment, peak calling, annotation, and motif finding. It also discusses downstream analysis such as comparing samples, analyzing region occupancy, and web resources for Chip-seq analysis.

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CHIP-SEQ DATA ANALYSIS Endre Barta, Hungary University of Debrecen, Center for Clinical Genomics barta.endre@unideb.hu Agricultural Biotechnology Center, Gödöllő, Agricultural Genomics and Bioinformatics Group barta@abc.hu
OUTLINE •  General introduction to the ChIP-seq technology •  Visualisation of genome data •  Command-line ChIP-seq analysis •  Quality checking of the reads •  Using SRA •  Alignment to the genome •  Peak calling •  Peak annotation •  denovo motif finding •  Downstream ChIP-seq analysis •  Comparing different samples •  Analyzing ChIP region occupancy •  Web pages, program packages helping in ChIP-seq analysis
Functional genomics (transcriptomics) analysis -A need to understand genome function on a global scale -Gene regulation shapes cellular function. To better understand gene-regulation genome-wide, we need to: •  Detect the openness and specific stage of the chromatin (histone modification ChIP-seq) •  Find transcription factor bound sites (TF ChIP-seq) •  Find co-regulators bound to transcription factors (ChIP-seq and other techniques) •  Find active enhancers (ChIP-seq, GRO-seq, RNA-seq) •  Determine transcript specific gene expression levels (GRO-seq, RNA- seq)
Traditional approaches Gel shift (EMSA) DNAse footprint to the problem -  Experimental determination of transcription factors binding sites. EMSA, DNAse footprinting in vitro - Computational prediction of binding motifs. Is a motif a real binding site? - DNA microarray (RNA-seq) analysis of target genes Is the target direct or indirect? -  Reporter gene expression assays of regulatory regions, motifs (deletion, point mutation) The natural environment of the given sequences can be completely different The need for a more direct (and genome-wide) in vivo method. Zsolt Szilágyi, University of Gothenburg
5 ChIP-seq technology •  Chromatine immunoprecipitation followed by next- generation sequencing •  Genome-wide detection of histone modifications or transcription factor binding sites (genome positions, where the TFs are bound to the DNS) Park P. Nat. Rev. Genetics. 2009; 10:669-680
Chromatin immunoprecipitation (ChIP) 1. Cross linking with FA Optimization is crucial. Zsolt Szilágyi, University of Gothenburg
2. Cell lysis and sonication (or DNase treatment) Zsolt Szilágyi, University of Gothenburg
Chromatin immunoprecipitation (ChIP) 3. Immunoprecipitation (IP) The protein of interest is immunoprecipitated together with the crosslinked DNA Zsolt Szilágyi, University of Gothenburg
Chromatin immunoprecipitation (ChIP) Reverse the FA 4.Decrosslinking and purification of the DNA crosslinking Zsolt Szilágyi, University of Gothenburg
5. Analysis of ChIP DNA Chromatin 6. Sequencing library preparation immunoprecipitation (ChIP) Identification of DNA regions associated with the protein/modification of interest -  real-time PCR -  DNA microarray (ChIP-chip) -  Sequencing (ChIP-seq) Zsolt Szilágyi, University of Gothenburg
ChIP-seq analysis steps in our lab •  Sequencing -> fastq files •  ChIP-seq analyze script (semi-automatic) •  BAM alignment files •  Bedgraph files – visualization of peaks •  Bed files - peak regions •  Annotation files - (peak positions relative to genes, motif occurrences of two chosen TFBS etc.) •  GO enrichment analysis •  denovo motif finding •  Known motif finding •  Downstream analysis •  Occupancy analysis •  Statistics •  Defining peak subsets, merging, intersecting peaks •  Comparison of peaksets, subsets •  Re-doing certain analysis on peaksets, subsets
2013.02.24. 12 SEQanswers wiki list of ChIP-seq softwares
FASTX toolkit
BAMTOOLS
BEDTOOLS
BWA
SAMTOOLS
MEME
MACS
HOMER
2013.02.24. Endre Barta 21 List of HOMER utilities
2012.05.16. 22 NGS software installed on ngsdeb Programs used for ChIP-seq, GRO-seq Other programs used and RNA-seq analysis for sequence analysis •  HOMER •  VCF-TOOLS •  VCFUTILS •  SAMTOOLS •  TABIX •  BOWTIE •  EMBOSS •  ChIPSEEQER •  BLAST •  BEDTOOLS •  BLAST+ •  BAMTOOLS •  CBUST •  LASTZ •  TOPHAT •  MULTIZ •  TRINITYRNASEQ •  BLAT •  BWA •  WEEDER •  MEME •  DIALIGN •  MACS •  SRMA •  FASTX-TOOLKITHead Node: 2x6 core, 144GB RAM, 20 Tbyte •  CLUSTALW disk •  GLAM2 •  PICARD 6x computing nodes: 2x6 core, 48GB RAM 600GB disk
ChIP-seq analyze script •  Aim: To have a simple script that can be used either to analyze local ChIP-seq sequencing data or to do meta-analysis of ChIP-seq experiments stored on the NCBI SRA database •  Command line tool •  Input: SRA (NCBI reads), fastq (reads), bam (alignments) •  Downloads SRA format files NCBI •  Maps fastq format reads •  Peak calling by HOMER and MACS •  Peak annotation by HOMER •  Known and denovo motif finding by HOMER •  GO enrichment analysis by HOMER •  Generates bedgraph and bed files for visualization Barta E Command line analysis of ChIP-seq results. EMBNET JOURNAL 17:(1) pp. 13-17. (2011)
Naming the samples •  hs_mq_STAT6_1 1.  Species: hs or mm 2.  Cell type 3.  Antibody 4.  Replica, other conditions etc. •  Should be consequent (e.g. parallels 1,2,3 not 1,2,a etc) – this will be the name through all the downstream analysis
2013.02.24. Barta Endre 25 Parameters that can be set for the run Inside the script •  Procno – for BWA and homer2 •  Motif1, motif2 – for explicitly annotating these motifs •  Motiflength – for HOMER (can be 6,10,16 for example) •  Minmotifl, maxmotifl – for MEME •  Nmotif – number of motifs to find for MEME •  Indexdir – for holding indexed genome files for BWA •  Barcode_length – for BWA As an argumentum •  Location of the listfile •  The analysis directory (where to put or where are already the sample directories •  Optionally a bigdir for storing SRA, fastq and sai files (optimally a local harddisk in a cluster environment)
26 Raw ChIP-seq sequence data available from the NCBI SRA database (~6000 human and ~5000 mouse)
Listfile for the analysis (SRA entries) Downloads and analyzes the reads from the listed experiments
SRA toolkit
Listfile with local sequencing samples Either a fastq file must exist in the sample/fastq directory or a bam file in the bam directory The script will use it and carry out the analysis
Processing local sequencing results Demultiplexing: Creating analysis directories and listfiles Running the analysis
2012.05.16. 31 Processing the raw sequence data •  Download the data in fastq format }  Check the quality with fastQC }  Overall quality of the reads }  Sequence composition }  GC bias }  Adaptor pollution }  Quality and adaptor trimming
2012.05.16. 32 Adaptor clipping •  5' TCGTATGCCGTCTTCTGCTTG 3' - reverse strand NEB RT Primer •  fastx_clipper –a TCGTATGCCGTCTTCTGCTTG DARWIN_4090_FC634H2AAXX:5:1:18775:1174#0 16 chr19 41311146 37 20M * 0 0 AAACACAACACCTACTTACT hghdgdehehdhhfdhhhdh
2012.05.16. 33 Quality checking with fastQC before and after clipping
34 Aligning reads to the reference genome •  BWA program (needs to be downloaded and compiled on a UNIX machine) •  BWA aln (-> sai internal format) •  BWA samse (sam multiple alignment format)
2012.05.16. 35 SAM file format SAM= TEXT file, BAM= SAM file compressed and indexed (binary) format
36 The IGV genome browser (for visualization of the genomic data)
2012.05.16. 37 The IGV application window Thorvaldsdóttir H et al. Brief Bioinform 2012;bib.bbs017 © The Author(s) 2012. Published by Oxford University Press.
Bedgraph and BAM files Coverage-1 Coverage-2 Strand Homer2* specific Merge Small Big Background MACS2 (merge) Coverage Coverage Merge BAM *normalized to 10 million reads no GC normalization
•  The number of peaks depends on 39 the methods used and the cutoff Finding peaks values applied. •  More reads doesn’t mean necessarily more peaks •  Different methods give only 60-80% similar peaks!
2013.02.24. Barta Endre 40 Peak finding with MACS •  MACS is a PYTHON script developed and maintained by Tao Liu •  It is the most widely used and cited method now •  There are a lot of switch to fine tune the analysis •  Single experiments and control-treated pairs can be analyzed as well •  It provides a BED format file for the peaks (ChIP regions) and for the summits and an XLS file for the peaks. •  It also provides bedgraph format coverage files
2013.02.24. Barta Endre 41 Peak finding using HOMER (findPeaks)
2013.02.24. 42 Quality control of the ChIP-seq experiments •  Clonal tag distribution •  Autocorrelation analysis
The Peak Read correlation is asymptotic to a horizontal line Peaks
versus
Reads
(Methyla2on
2
lot1) Peak
versus
Read
(Methyla2on
3
lot1) 15000 14000 Peak
number Detected
peaks 10000 7000 5000 0 0 0 5000000 10000000 15000000 20000000 0 4000000 8000000 12000000 16000000 Number
of
reads Read
number 1.Random removal of reads. 2.Repeat seven times. 3.Reanalyse by MACS. 4.Plot.
100% of reads visualized
50% of reads visualized
How does the read number affect the peak number Debrecen RXR No of reads in fastq   Total homer peaks   IP efficiency   Experiment   10000 Genoscope   Debrecen   Fold   Genoscope   Debrecen   Genoscope   Debrecen   Peak score 1000 mm_BMDM_RXR   60438603   10234482   5.91   18099   4852   2.57   2.60   100 10 2500 1 0 2000 4000 6000 Peak number Genoscope RXR 10000 Peak score 1000 100 1813 10 2500 1 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 Peak number
47 Which are the real peaks? It depends on the type of the ChIP and the number of the reads MACS2 scores of CTCF (C) MACS2 scores of p300 (LG) peaks peaks 1000 1000 MACS2 scores MACS2 score 100 100 30 10 10 5 1 1 0 10000 20000 30000 0 500 1000 1500 2000 2500 Number Number
48 How to define biologically meaningful peaks?
The effect of IgG control on the predicted peaks
The effect of IgG control on the predicted peaks 30 •  Mouse embrionic stem cell Number of tags 25 20 •  RXR antibody 15 10 5 •  No treatment applied 0 •  1006 peaks removed Distance from peak center IgG correction applied (n=3258) Without applying IgG correction (n=3674) Nr. of reads in fastq 7 825 951 Fragment length (bp) 189 Mean quality 37.15 Nr. of peaks 3 674 Mapped reads (%) 94.97 IP efficiency (%) 5.64  
2013.02.24. Barta Endre 51 Annotation of the peaks (annotatePeaks.pl) •  Genomic localization •  Closest TSS •  Motif occurrences •  Enrichment in different ontologies Method: Generate a list of genes and compare the list statistically with the list of genes present in a given ontology
2013.02.24. Barta Endre 52 Wikipathways enrichment in mouse PPARg adypocyte ChIPs
2012.05.16. 53 Gene Ontology enrichment analysis •  Peaks can be assigned to genes (not always to the right genes) •  This results in a gene list (like at the microarray analysis) •  The gene lists can be statistically analyzed against other gene list. •  We must usually consider four numbers •  The number of the whole gene set (usually the gene number) •  The number of the genes in the GO list •  The number of genes in the sample •  The number of the genes in the sample list, which can be found in the GO list •  HOMER can do GO analysis against different ontologies
54 HOMER GO analysis 2012.05.16. (sorted by P-value)
2012.05.16. 55 denovo motif finding •  Aim: Find motifs enriched in ChIP-seq peaks (and different peak subsets) •  Several algorithms exist (MEME, Gibbs sampler etc.) •  Homer works well with ChIP-seq regions •  It uses genomic intervals (bedfiles) as an input •  It selects at least the same number of random genomic region with the same sizes •  It masks out repetitive regions •  It throws away regions with too many Ns (masked bases)
56 Top peaks give better motif enrichment Peaks   w  mo*f   p-­‐value   target  %   bg  %   fold   10883   3266   1E-­‐1274   30.01   4.82   6.23   PU.1   5206   1632   1E-­‐643   31.34   5.01   6.26   1000   442   1E-­‐169   44.24   8.28   5.34   10883   3544   1E-­‐773   32.56   9.52   3.42   NRhalf   5206   2436   1E-­‐630   46.79   12.77   3.66   1000   592   1E-­‐258   59.23   10.39   5.70  
2013.02.24. Barta Endre 57 De novo motif finding (MEME, zoops, w=11) Published figure Lefterova et al Macrophage 521/1961 min: 500, max: 1500 Adypocyte 1622/2634 min:1500 max: 2000
2013.02.24. Barta Endre 58 De novo motif finding with HOMER •  How findMotifsGenome.pl works: 1.  Verify peak/BED file 2.  Extract sequences from the genome corresponding to the regions in the input file, filtering sequences that are >70% "N” 3.  Calculate GC/CpG content of peak sequences. 4.  Preparse the genomic sequences of the selected size to serve as background sequences 5.  Randomly select background regions for motif discovery. 6.  Auto normalization of sequence bias. 7.  Check enrichment of known motifs 8.  de novo motif finding
2012.05.16. 59 HOMER denovo motif finding result }  RXR peaks overlapping with GRO-seq paired peaks }  Enrichment = % of Targets / % of Background }  The P-value depends on the size of the sample (not comparable between different samples) }  Best match (HOMER has its own motif library coming from the JASPAR database and from ChIP- seq analyses) does not mean perfect match!
2012.05.16. 60 HOMER known motif enrichment analysis •  Enrichment = % of targets sequences with Motif / % of Background sequences with motif
Outputs of the primary analysis I. 1.  BAM format alignment files for visualization and for occupancy analysis name.bam 2.  Bedgraph files for visualization 1.  name.bedgraph.gz : normalized (10 million) extended reads from both strand 2.  name_small.bedgraph.gz: normalized (10 million), extended reads shown in the positive strand (summit shows the binding site) 3.  name_big.bedgraph.gz : same as above but with the highest resolution (and sometimes in a bigger size) 3.  Bed files for visualization and for further analysis 1.  ChIP regions from HOMER analysis (name_macs_peaks.bed). Summit +- 100 bp 2.  ChIP regions from MACS analysis (name-homerpeaks.bed) 3.  MACS peak summits (name_macs_summits.bed)
Outputs of the primary analysis II. 4.  Annotation file (name_homermotifsannot.txt), tab delimited, can be directly imported into the excel or other programs). There is an other file (name_macs- homermotifsannot.txt) for MACS peak annotation 5.  denovo motif finding, known motif enrichment and GO annotation enrichment for the best 1000 peaks from both the HOMER and MACS peak predictions. HTML format, which can be opened directly from any internet browser (homerResults.html) 6.  Overall statistics of the experiments (generated with a separate script)
Outputs of the primary analysis III/a.
Outputs of the primary analysis III/b. Approximate IP effeciency describes the fraction of tags found in peaks versus. genomic background. This provides an estimate of how well the ChIP worked. Certain antibodies like H3K4me3, ERa, or PU.1 will yield very high IP efficiencies (>20%), while most are in the 1-20% range. Once this number dips below 1% it's a good sign the ChIP didn't work very well and should probably be optimized.
Downstream analysis •  Comparing different samples •  Overlapping regions (intersectBed) •  Occupancy analysis (diffBind) •  Generating profiles •  Re-analyze peak subsets for motif occurrences
Generating profiles or metagenes, or histograms (normalized tag counts in bins) To compare different ChIP-seq samples, extensive normalization has to be carried out (HOMER’s annotatePeak can do this). The center (0 point) can be either the peak summit, the TSS or the TFBSs
67 intersectBed Switches: -  -a peakfile1.bed -b peakfile2.bed ((-abam => -bed)) -  -u -  -v -  -c (count b on a) 1 1 0 1 1 0 -  -wo (fusing beds in a “double bed” table) -  -f (minimum overlap %) – -u -f 0.6 -  -r (reciprocal overlap) – -u -f 0.6 -r -  -s (strand specific match)
Comparison of ChIP regions from different experiments C57B16 MCSF C57B16 1143 +IL4+DMSO MCSF +DMSO 13090 3554 2190 4265 STAT6 KO MCSF+ IL4+DMSO
Different subsets have different motifs C57B16 STAT6 KO C57B16 STAT6 KO MCSF MCSF+ MCSF MCSF+ +IL4+DMSO IL4+DMSO +IL4+DMSO IL4+DMSO peaks 7275 4001 peak s B_Cell_Receptor_Signaling_Pathway_WP23 TGF-beta_Receptor_Signaling_Pathway_WP366 Apoptotic_execution_phase_WP1784 EGFR1_Signaling_Pathway_WP437 BMP_signalling_and_regulation_WP1425 IL-5_Signaling_Pathway_WP127 TGF-beta_Receptor_Signaling_Pathway_WP366 B_Cell_Receptor_Signaling_Pathway_WP23 Wnt_Signaling_Pathway_NetPath_WP363 Toll-like_receptor_signaling_pathway_WP75 Kit_Receptor_Signaling_Pathway_WP304 T_Cell_Receptor_Signaling_Pathway_WP69 IL-6_Signaling_Pathway_WP364 IL-3_Signaling_Pathway_WP286 Integrin_alphaIIb_beta3_signaling_WP1832 Regulation_of_toll- IL-4_signaling_Pathway_WP395 like_receptor_signaling_pathway_WP1449 Type_II_interferon_signaling_(IFNG)_WP619
DiffBind R package
Input files §  1 input file §  Comma Separated Values (.csv) §  1 header line + 1 line for each sample §  Required fields §  Sample ID §  Replicate ID §  Tissue §  Read file (bam) §  Factor §  Control read file (bam) §  Condition §  Peak file
Analysis Pipeline §  Data used in the analysis §  Read files §  Peaksets (Homer, MACS) §  2 pipelines §  Occupancy Analysis §  No quality control §  Less strict §  Differential Binding Analysis §  Quality control §  (Less flexible)
Analysis Pipeline §  Creating a 'dba' object §  Output based on raw data §  Definition of consensus peakset §  Read counting §  Normalization §  Output based on the consensus peakset §  Contrast, blocking, masking, etc.
Analysis Pipeline §  Differential Binding Analysis §  Output based on differentially bound sites §  Heatmaps (correlation, expression) §  Plots (MA, PCA, boxplot) §  Venn diagrams §  Save the peaksets for later use
75 Control vs. LG268 Significant changes Significant changes DiffBind: Attila Horváth 813
76 Control vs. LG268 DiffBind: Attila Horváth
77 Motives are close to peak summits RXR ChIP peaks Width   p-­‐value  target  %   bg  %   fold   50   1E-­‐74   23.17   4.66   4.97   60   1E-­‐91   28.62   5.94   4.82   PU.1   80   1E-­‐142   34.02   5.32   6.39   100   1E-­‐169   44.24   8.28   5.34   50   1E-­‐151   39.27   6.96   5.64   60   1E-­‐184   41.05   6.02   6.82   NRhalf   80   1E-­‐254   52.78   7.63   6.92   100   1E-­‐258   59.23   10.39   5.70   p-­‐value   target  %   bg  %   fold   1E-­‐86   18.97   5.34   3.55   PU.1   1E-­‐135   21.85   4.62   4.73   1E-­‐169   44.24   8.28   5.34   1E-­‐18   0.64   0.01   64.00   NRhalf   1E-­‐52   9.52   2.18   4.37   1E-­‐258   59.23   10.39   5.70  
RXR enhancers from different tissues 78 around the Tgm2 gene Adipocytes Liver BMDM
79 Motives on RXR ChIP-seq peaks in different cells (HOMER de novo motif finding) Motives in BMDM: BMDM RXR •  PU.1 •  NR half •  AP-1 AP-1 •  CEPB PU.1 •  RUNX CEBP •  NR DR1 L1_4h RXR •  IRF Liver RXR AP1 CEBP NF1 half STAT L1_D6 RXR NF1 CTCF NF1 NF1 half CEBP
80 Motif matrix files and logos RGKKSANRGKKSA! DRGGTCARAGGTCARN! >Consensus sequence Name Score threshold >Consensus sequence Name >RGKKSANRGKKSA DR1 11.0523208315125 Score threshold R! 0.499 0.001 0.499 0.001 D! >DRGGTCARAGGTCARN 1-DRGGTCARAGGTCARN G! 0.001 0.001 0.997 0.001 R! 8.661417 * K! 0.001 0.001 0.499 0.499 G! 0.367 0.067 0.311 0.256 K! 0.001 0.001 0.499 0.499 G! 0.466 0.001 0.532 0.001 S! 0.001 0.499 0.499 0.001 T! 0.189 0.022 0.656 0.133 A! 0.997 0.001 0.001 0.001 C! 0.111 0.100 0.667 0.122 N! 0.25 0.25 0.25 0.25 A! 0.133 0.256 0.156 0.455 R! 0.499 0.001 0.499 0.001 R! 0.166 0.512 0.222 0.100 G! 0.001 0.001 0.997 0.001 A! 0.966 0.001 0.011 0.022 K! 0.001 0.001 0.499 0.499 G! 0.378 0.067 0.477 0.078 K! 0.001 0.001 0.499 0.499 G! 0.821 0.001 0.177 0.001 S! 0.001 0.499 0.499 0.001 T! 0.066 0.011 0.922 0.001 A! 0.997 0.001 0.001 0.001 C! 0.044 0.022 0.767 0.167 A! 0.111 0.089 0.134 0.666 R! 0.078 0.855 0.045 0.022 N! 0.900 0.001 0.022 0.077 0.278 0.211 0.378 0.134 0.300 0.211 0.222 0.267 Log P value Unused Match in Target and Background, P value * -226.958685 0 T:159.0(19.06%),B:970.0(2.08%),P:1e-98
81 RXR-PU.1 tethering Motif enrichment PU.1 1e-650 83.75% 7.20% AP-1 1e-29 19.81% 7.59% RUNX 1e-24 8.78% 2.04% c/EBP 1e-17 3.32% 0.35% Half 1e-15 4.74% 0.97% SREBP? 1e-14 3.91% 0.70% RGKKSA! <<<<<28.46% of peaks
82 ChIP-Seq Reads / tags Quality improvement: (fastq format) pipeline clippers/trimmers Burrows-Wheeler BWA Alignment Tool Alignment files (bam) Hypergeometric Homer2 Model Based Optimization of makeTagdirectory MACS2 Analysis for w/o input Normalizations, Motif makeUCSCfile* callpeak* ChIP-Seq data strand specificity EnRichment findPeaks *Genome coverage files (bedgraph) Peak files (bed) Peak files (bed) (no summit) Bash Contrasts DiffBind Differential Binding Analysis of ChIP- Bash sort, head edgeR, limma Seq peak data sort, head Significant peaks Significant peaks Significant peaks (top 1000 “summits”) (top 1000 summits) (top 1000 summits) Homer2 Directed motif finding Homer Tagdirectory because of masked out motives findMotifsGenome Motif matrices/logos Redundancy BEDTools Functional Homer2 beds intersectBed annotation of findMotifsGenome binding and Whatever infile modification loci Gene list PeakAnnotator beds lists VennMaster Meta-histogram files Motif files (bed) (txt) IP efficiency Overlaps, distances
Chipster (ngs tools from version 2.0) ChIP-seq chipster tutorial session with Eija Korpelainen COST conference Valencia, May, 2013
•  Regulatory Sequence Analysis Availability Tools (RSAT) •  http://rsat.ulb.ac.be/rsat/ •  Interfaces •  Stand-alone apps •  Web site •  Web services (SOAP/WSDL API) •  Web interface •  Simplicity of use (“one click” interface). •  Advanced options can be accessed optionally. •  Allows to analyze data set of realistic size (uploaded files).
Work flow for chip-seq analysis Reads + quality (fastq) •  ChIP-seq data can be retrieved from Read clean-up specialized databases such as Gene Expression Omnibus (GEO). Data retrieval Cleaned reads •  The GEO database allows to retrieve GEO sequences at various processing stages. Read mapping •  Read sequences: typically, several millions of short sequences (25bp). Alignments •  Read locations: chromosomal Peak calling coordinates of each read. •  Peak locations: several thousands of Peaks variable size regions (typically between 100bp and 10kb). Motif discovery •  A technological bottleneck lies in the next step: exploitation of full peak collections to Over-represented motifs discover motifs and predict binding sites. Pattern matching Binding sites
Local over-representation (program local- words) Windows •  The program local-words detects words that are over-represented in specific position windows. •  The result is thus more informative than for position-analysis: in addition to the global positional bias, we detect the precise window where each word is over-represented. C 6-mer (e.g.AACAAA ) Expected occurrences (from whole sequence length) Thomas-Chollier M, Darbo E, Herrmann C, Defrance M, Thieffry D, van Helden J. 2012. A complete workflow for the analysis of full-size ChIP-seq (and similar) data sets using peak-motifs. Nat Protoc 7(8): 1551-1568.
Network of motifs discovered in tissue-specific p300 binding regions Thomas-Chollier M, Herrmann C, Defrance M, Sand O, Thieffry D, van Helden J. 2012. RSAT peak-motifs: motif analysis in full-size ChIP-seq datasets. Nucleic Acids Res 40(4): e31.
An integrated work flow for analyzing ChIP-seq peaks •  The program peak-motifs is a work flow combining a series of RSAT tools optimized for discovered motifs in large sequence sets (tens of Mb) resulting from ChIP-seq experiments.. •  Multiple pattern discovery algorithms •  Global over-representation •  Positional biases •  Local over-representation •  Discovered motifs are compared with •  motif databases •  user-specified reference motifs. •  Prediction of binding sites, which can be uploaded as custom annotation tracks to genome browsers (e.g. UCSC) for visualization. •  Interfaces •  Stand-alone •  Web interface •  Web services (SOAP/WSDL) Thomas-Chollier M, Herrmann C, Defrance M, Sand O, Thieffry D, van Helden J. 2012. RSAT peak-motifs: motif analysis in full-size ChIP-seq datasets. Nucleic Acids Res 40(4): e31.
ChIP-seq analysis server @sib
SIB server, correlation tool
SIB server, ChIP- peak tool
ChIPseeqer http://physiology.med.cornell.edu/faculty/elemento/lab/chipseq.shtml
ChIPseeqer tasks (command line and GUI) •  Peak detection •  Gene-level annotation of peaks •  Pathways enrichment analysis •  Regulatory element analysis, using either a denovo approach, known or user-defined motifs •  Nongenic peak annotation (repeats, CpG island, duplications) •  Conservation analysis •  Clustering analysis •  Visualisation •  Integration and comparison across different ChIP-seq experiments
ChIPseeqer workflow Giannopoulou and Elemento BMC Bioinformatics 2011 12:277 doi: 10.1186/1471-2105-12 -277
ChIPseeqer GUI Giannopoulou and Elemento BMC Bioinformatics 2011 12:277 doi: 10.1186/1471-210 5-12-277
Factorbook.org (ENCODE ChIP-seq data)
Factorbook (ENCODE) RXR entry
A system-level overview of the core framework of ChIPBase. Yang J et al. Nucl. Acids Res. 2012;nar.gks1060 © The Author(s) 2012. Published by Oxford University Press.
Summary of ChIP-seq analysis •  Having a good ChIP is the most important for the analysis •  There are many alternatives for the analysis pipeline •  Analysis speed, percentage of mapped reads etc. are not the most important parameters during the analysis •  Read number influences strongly the peak number •  Our practice is that for a TF ChIP 6-10 million of reads (depends on the expected number of peaks) is a minimum but it could be sufficient (15-20 million of reads at the histone ChIPs) as well •  Having controls and parallels is good, but not always necessary •  The primary analysis can be run easily from scripts or with workflows •  The most difficult and time-consuming part of the process is the downstream analysis
Acknowledgments http://genomics.med.unideb.hu/ •  László Nagy director http://nlab.med.unideb.hu/ •  Bálint L Bálint head of the lab László Nagy lab leader Bálint L Bálint senior researcher Bioinformatics team: Zsuzsanna Nagy senior researcher •  Gergely Nagy PhD student Bence Dániel PhD student •  Attila Horváth System administrator, R programmer Zoltán Simándi PhD student •  Dávid Jónás Bioinformatician Péter Brázda PhD student •  László Steiner Mathematician Ixchelt Cuaranta PhD student •  Erik Czipa MSc student

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ChipSeq Data Analysis

  • 1. CHIP-SEQ DATA ANALYSIS Endre Barta, Hungary University of Debrecen, Center for Clinical Genomics barta.endre@unideb.hu Agricultural Biotechnology Center, Gödöllő, Agricultural Genomics and Bioinformatics Group barta@abc.hu
  • 2. OUTLINE •  General introduction to the ChIP-seq technology •  Visualisation of genome data •  Command-line ChIP-seq analysis •  Quality checking of the reads •  Using SRA •  Alignment to the genome •  Peak calling •  Peak annotation •  denovo motif finding •  Downstream ChIP-seq analysis •  Comparing different samples •  Analyzing ChIP region occupancy •  Web pages, program packages helping in ChIP-seq analysis
  • 3. Functional genomics (transcriptomics) analysis -A need to understand genome function on a global scale -Gene regulation shapes cellular function. To better understand gene-regulation genome-wide, we need to: •  Detect the openness and specific stage of the chromatin (histone modification ChIP-seq) •  Find transcription factor bound sites (TF ChIP-seq) •  Find co-regulators bound to transcription factors (ChIP-seq and other techniques) •  Find active enhancers (ChIP-seq, GRO-seq, RNA-seq) •  Determine transcript specific gene expression levels (GRO-seq, RNA- seq)
  • 4. Traditional approaches Gel shift (EMSA) DNAse footprint to the problem -  Experimental determination of transcription factors binding sites. EMSA, DNAse footprinting in vitro - Computational prediction of binding motifs. Is a motif a real binding site? - DNA microarray (RNA-seq) analysis of target genes Is the target direct or indirect? -  Reporter gene expression assays of regulatory regions, motifs (deletion, point mutation) The natural environment of the given sequences can be completely different The need for a more direct (and genome-wide) in vivo method. Zsolt Szilágyi, University of Gothenburg
  • 5. 5 ChIP-seq technology •  Chromatine immunoprecipitation followed by next- generation sequencing •  Genome-wide detection of histone modifications or transcription factor binding sites (genome positions, where the TFs are bound to the DNS) Park P. Nat. Rev. Genetics. 2009; 10:669-680
  • 6. Chromatin immunoprecipitation (ChIP) 1. Cross linking with FA Optimization is crucial. Zsolt Szilágyi, University of Gothenburg
  • 7. 2. Cell lysis and sonication (or DNase treatment) Zsolt Szilágyi, University of Gothenburg
  • 8. Chromatin immunoprecipitation (ChIP) 3. Immunoprecipitation (IP) The protein of interest is immunoprecipitated together with the crosslinked DNA Zsolt Szilágyi, University of Gothenburg
  • 9. Chromatin immunoprecipitation (ChIP) Reverse the FA 4.Decrosslinking and purification of the DNA crosslinking Zsolt Szilágyi, University of Gothenburg
  • 10. 5. Analysis of ChIP DNA Chromatin 6. Sequencing library preparation immunoprecipitation (ChIP) Identification of DNA regions associated with the protein/modification of interest -  real-time PCR -  DNA microarray (ChIP-chip) -  Sequencing (ChIP-seq) Zsolt Szilágyi, University of Gothenburg
  • 11. ChIP-seq analysis steps in our lab •  Sequencing -> fastq files •  ChIP-seq analyze script (semi-automatic) •  BAM alignment files •  Bedgraph files – visualization of peaks •  Bed files - peak regions •  Annotation files - (peak positions relative to genes, motif occurrences of two chosen TFBS etc.) •  GO enrichment analysis •  denovo motif finding •  Known motif finding •  Downstream analysis •  Occupancy analysis •  Statistics •  Defining peak subsets, merging, intersecting peaks •  Comparison of peaksets, subsets •  Re-doing certain analysis on peaksets, subsets
  • 12. 2013.02.24. 12 SEQanswers wiki list of ChIP-seq softwares
  • 16. BWA
  • 18. MEME
  • 19. MACS
  • 20. HOMER
  • 21. 2013.02.24. Endre Barta 21 List of HOMER utilities
  • 22. 2012.05.16. 22 NGS software installed on ngsdeb Programs used for ChIP-seq, GRO-seq Other programs used and RNA-seq analysis for sequence analysis •  HOMER •  VCF-TOOLS •  VCFUTILS •  SAMTOOLS •  TABIX •  BOWTIE •  EMBOSS •  ChIPSEEQER •  BLAST •  BEDTOOLS •  BLAST+ •  BAMTOOLS •  CBUST •  LASTZ •  TOPHAT •  MULTIZ •  TRINITYRNASEQ •  BLAT •  BWA •  WEEDER •  MEME •  DIALIGN •  MACS •  SRMA •  FASTX-TOOLKITHead Node: 2x6 core, 144GB RAM, 20 Tbyte •  CLUSTALW disk •  GLAM2 •  PICARD 6x computing nodes: 2x6 core, 48GB RAM 600GB disk
  • 23. ChIP-seq analyze script •  Aim: To have a simple script that can be used either to analyze local ChIP-seq sequencing data or to do meta-analysis of ChIP-seq experiments stored on the NCBI SRA database •  Command line tool •  Input: SRA (NCBI reads), fastq (reads), bam (alignments) •  Downloads SRA format files NCBI •  Maps fastq format reads •  Peak calling by HOMER and MACS •  Peak annotation by HOMER •  Known and denovo motif finding by HOMER •  GO enrichment analysis by HOMER •  Generates bedgraph and bed files for visualization Barta E Command line analysis of ChIP-seq results. EMBNET JOURNAL 17:(1) pp. 13-17. (2011)
  • 24. Naming the samples •  hs_mq_STAT6_1 1.  Species: hs or mm 2.  Cell type 3.  Antibody 4.  Replica, other conditions etc. •  Should be consequent (e.g. parallels 1,2,3 not 1,2,a etc) – this will be the name through all the downstream analysis
  • 25. 2013.02.24. Barta Endre 25 Parameters that can be set for the run Inside the script •  Procno – for BWA and homer2 •  Motif1, motif2 – for explicitly annotating these motifs •  Motiflength – for HOMER (can be 6,10,16 for example) •  Minmotifl, maxmotifl – for MEME •  Nmotif – number of motifs to find for MEME •  Indexdir – for holding indexed genome files for BWA •  Barcode_length – for BWA As an argumentum •  Location of the listfile •  The analysis directory (where to put or where are already the sample directories •  Optionally a bigdir for storing SRA, fastq and sai files (optimally a local harddisk in a cluster environment)
  • 26. 26 Raw ChIP-seq sequence data available from the NCBI SRA database (~6000 human and ~5000 mouse)
  • 27. Listfile for the analysis (SRA entries) Downloads and analyzes the reads from the listed experiments
  • 29. Listfile with local sequencing samples Either a fastq file must exist in the sample/fastq directory or a bam file in the bam directory The script will use it and carry out the analysis
  • 30. Processing local sequencing results Demultiplexing: Creating analysis directories and listfiles Running the analysis
  • 31. 2012.05.16. 31 Processing the raw sequence data •  Download the data in fastq format }  Check the quality with fastQC }  Overall quality of the reads }  Sequence composition }  GC bias }  Adaptor pollution }  Quality and adaptor trimming
  • 32. 2012.05.16. 32 Adaptor clipping •  5' TCGTATGCCGTCTTCTGCTTG 3' - reverse strand NEB RT Primer •  fastx_clipper –a TCGTATGCCGTCTTCTGCTTG DARWIN_4090_FC634H2AAXX:5:1:18775:1174#0 16 chr19 41311146 37 20M * 0 0 AAACACAACACCTACTTACT hghdgdehehdhhfdhhhdh
  • 33. 2012.05.16. 33 Quality checking with fastQC before and after clipping
  • 34. 34 Aligning reads to the reference genome •  BWA program (needs to be downloaded and compiled on a UNIX machine) •  BWA aln (-> sai internal format) •  BWA samse (sam multiple alignment format)
  • 35. 2012.05.16. 35 SAM file format SAM= TEXT file, BAM= SAM file compressed and indexed (binary) format
  • 37. 2012.05.16. 37 The IGV application window Thorvaldsdóttir H et al. Brief Bioinform 2012;bib.bbs017 © The Author(s) 2012. Published by Oxford University Press.
  • 38. Bedgraph and BAM files Coverage-1 Coverage-2 Strand Homer2* specific Merge Small Big Background MACS2 (merge) Coverage Coverage Merge BAM *normalized to 10 million reads no GC normalization
  • 39. •  The number of peaks depends on 39 the methods used and the cutoff Finding peaks values applied. •  More reads doesn’t mean necessarily more peaks •  Different methods give only 60-80% similar peaks!
  • 40. 2013.02.24. Barta Endre 40 Peak finding with MACS •  MACS is a PYTHON script developed and maintained by Tao Liu •  It is the most widely used and cited method now •  There are a lot of switch to fine tune the analysis •  Single experiments and control-treated pairs can be analyzed as well •  It provides a BED format file for the peaks (ChIP regions) and for the summits and an XLS file for the peaks. •  It also provides bedgraph format coverage files
  • 41. 2013.02.24. Barta Endre 41 Peak finding using HOMER (findPeaks)
  • 42. 2013.02.24. 42 Quality control of the ChIP-seq experiments •  Clonal tag distribution •  Autocorrelation analysis
  • 43. The Peak Read correlation is asymptotic to a horizontal line Peaks
versus
Reads
(Methyla2on
2
lot1) Peak
versus
Read
(Methyla2on
3
lot1) 15000 14000 Peak
number Detected
peaks 10000 7000 5000 0 0 0 5000000 10000000 15000000 20000000 0 4000000 8000000 12000000 16000000 Number
of
reads Read
number 1.Random removal of reads. 2.Repeat seven times. 3.Reanalyse by MACS. 4.Plot.
  • 44. 100% of reads visualized
  • 45. 50% of reads visualized
  • 46. How does the read number affect the peak number Debrecen RXR No of reads in fastq   Total homer peaks   IP efficiency   Experiment   10000 Genoscope   Debrecen   Fold   Genoscope   Debrecen   Genoscope   Debrecen   Peak score 1000 mm_BMDM_RXR   60438603   10234482   5.91   18099   4852   2.57   2.60   100 10 2500 1 0 2000 4000 6000 Peak number Genoscope RXR 10000 Peak score 1000 100 1813 10 2500 1 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 Peak number
  • 47. 47 Which are the real peaks? It depends on the type of the ChIP and the number of the reads MACS2 scores of CTCF (C) MACS2 scores of p300 (LG) peaks peaks 1000 1000 MACS2 scores MACS2 score 100 100 30 10 10 5 1 1 0 10000 20000 30000 0 500 1000 1500 2000 2500 Number Number
  • 48. 48 How to define biologically meaningful peaks?
  • 49. The effect of IgG control on the predicted peaks
  • 50. The effect of IgG control on the predicted peaks 30 •  Mouse embrionic stem cell Number of tags 25 20 •  RXR antibody 15 10 5 •  No treatment applied 0 •  1006 peaks removed Distance from peak center IgG correction applied (n=3258) Without applying IgG correction (n=3674) Nr. of reads in fastq 7 825 951 Fragment length (bp) 189 Mean quality 37.15 Nr. of peaks 3 674 Mapped reads (%) 94.97 IP efficiency (%) 5.64  
  • 51. 2013.02.24. Barta Endre 51 Annotation of the peaks (annotatePeaks.pl) •  Genomic localization •  Closest TSS •  Motif occurrences •  Enrichment in different ontologies Method: Generate a list of genes and compare the list statistically with the list of genes present in a given ontology
  • 52. 2013.02.24. Barta Endre 52 Wikipathways enrichment in mouse PPARg adypocyte ChIPs
  • 53. 2012.05.16. 53 Gene Ontology enrichment analysis •  Peaks can be assigned to genes (not always to the right genes) •  This results in a gene list (like at the microarray analysis) •  The gene lists can be statistically analyzed against other gene list. •  We must usually consider four numbers •  The number of the whole gene set (usually the gene number) •  The number of the genes in the GO list •  The number of genes in the sample •  The number of the genes in the sample list, which can be found in the GO list •  HOMER can do GO analysis against different ontologies
  • 54. 54 HOMER GO analysis 2012.05.16. (sorted by P-value)
  • 55. 2012.05.16. 55 denovo motif finding •  Aim: Find motifs enriched in ChIP-seq peaks (and different peak subsets) •  Several algorithms exist (MEME, Gibbs sampler etc.) •  Homer works well with ChIP-seq regions •  It uses genomic intervals (bedfiles) as an input •  It selects at least the same number of random genomic region with the same sizes •  It masks out repetitive regions •  It throws away regions with too many Ns (masked bases)
  • 56. 56 Top peaks give better motif enrichment Peaks   w  mo*f   p-­‐value   target  %   bg  %   fold   10883   3266   1E-­‐1274   30.01   4.82   6.23   PU.1   5206   1632   1E-­‐643   31.34   5.01   6.26   1000   442   1E-­‐169   44.24   8.28   5.34   10883   3544   1E-­‐773   32.56   9.52   3.42   NRhalf   5206   2436   1E-­‐630   46.79   12.77   3.66   1000   592   1E-­‐258   59.23   10.39   5.70  
  • 57. 2013.02.24. Barta Endre 57 De novo motif finding (MEME, zoops, w=11) Published figure Lefterova et al Macrophage 521/1961 min: 500, max: 1500 Adypocyte 1622/2634 min:1500 max: 2000
  • 58. 2013.02.24. Barta Endre 58 De novo motif finding with HOMER •  How findMotifsGenome.pl works: 1.  Verify peak/BED file 2.  Extract sequences from the genome corresponding to the regions in the input file, filtering sequences that are >70% "N” 3.  Calculate GC/CpG content of peak sequences. 4.  Preparse the genomic sequences of the selected size to serve as background sequences 5.  Randomly select background regions for motif discovery. 6.  Auto normalization of sequence bias. 7.  Check enrichment of known motifs 8.  de novo motif finding
  • 59. 2012.05.16. 59 HOMER denovo motif finding result }  RXR peaks overlapping with GRO-seq paired peaks }  Enrichment = % of Targets / % of Background }  The P-value depends on the size of the sample (not comparable between different samples) }  Best match (HOMER has its own motif library coming from the JASPAR database and from ChIP- seq analyses) does not mean perfect match!
  • 60. 2012.05.16. 60 HOMER known motif enrichment analysis •  Enrichment = % of targets sequences with Motif / % of Background sequences with motif
  • 61. Outputs of the primary analysis I. 1.  BAM format alignment files for visualization and for occupancy analysis name.bam 2.  Bedgraph files for visualization 1.  name.bedgraph.gz : normalized (10 million) extended reads from both strand 2.  name_small.bedgraph.gz: normalized (10 million), extended reads shown in the positive strand (summit shows the binding site) 3.  name_big.bedgraph.gz : same as above but with the highest resolution (and sometimes in a bigger size) 3.  Bed files for visualization and for further analysis 1.  ChIP regions from HOMER analysis (name_macs_peaks.bed). Summit +- 100 bp 2.  ChIP regions from MACS analysis (name-homerpeaks.bed) 3.  MACS peak summits (name_macs_summits.bed)
  • 62. Outputs of the primary analysis II. 4.  Annotation file (name_homermotifsannot.txt), tab delimited, can be directly imported into the excel or other programs). There is an other file (name_macs- homermotifsannot.txt) for MACS peak annotation 5.  denovo motif finding, known motif enrichment and GO annotation enrichment for the best 1000 peaks from both the HOMER and MACS peak predictions. HTML format, which can be opened directly from any internet browser (homerResults.html) 6.  Overall statistics of the experiments (generated with a separate script)
  • 63. Outputs of the primary analysis III/a.
  • 64. Outputs of the primary analysis III/b. Approximate IP effeciency describes the fraction of tags found in peaks versus. genomic background. This provides an estimate of how well the ChIP worked. Certain antibodies like H3K4me3, ERa, or PU.1 will yield very high IP efficiencies (>20%), while most are in the 1-20% range. Once this number dips below 1% it's a good sign the ChIP didn't work very well and should probably be optimized.
  • 65. Downstream analysis •  Comparing different samples •  Overlapping regions (intersectBed) •  Occupancy analysis (diffBind) •  Generating profiles •  Re-analyze peak subsets for motif occurrences
  • 66. Generating profiles or metagenes, or histograms (normalized tag counts in bins) To compare different ChIP-seq samples, extensive normalization has to be carried out (HOMER’s annotatePeak can do this). The center (0 point) can be either the peak summit, the TSS or the TFBSs
  • 67. 67 intersectBed Switches: -  -a peakfile1.bed -b peakfile2.bed ((-abam => -bed)) -  -u -  -v -  -c (count b on a) 1 1 0 1 1 0 -  -wo (fusing beds in a “double bed” table) -  -f (minimum overlap %) – -u -f 0.6 -  -r (reciprocal overlap) – -u -f 0.6 -r -  -s (strand specific match)
  • 68. Comparison of ChIP regions from different experiments C57B16 MCSF C57B16 1143 +IL4+DMSO MCSF +DMSO 13090 3554 2190 4265 STAT6 KO MCSF+ IL4+DMSO
  • 69. Different subsets have different motifs C57B16 STAT6 KO C57B16 STAT6 KO MCSF MCSF+ MCSF MCSF+ +IL4+DMSO IL4+DMSO +IL4+DMSO IL4+DMSO peaks 7275 4001 peak s B_Cell_Receptor_Signaling_Pathway_WP23 TGF-beta_Receptor_Signaling_Pathway_WP366 Apoptotic_execution_phase_WP1784 EGFR1_Signaling_Pathway_WP437 BMP_signalling_and_regulation_WP1425 IL-5_Signaling_Pathway_WP127 TGF-beta_Receptor_Signaling_Pathway_WP366 B_Cell_Receptor_Signaling_Pathway_WP23 Wnt_Signaling_Pathway_NetPath_WP363 Toll-like_receptor_signaling_pathway_WP75 Kit_Receptor_Signaling_Pathway_WP304 T_Cell_Receptor_Signaling_Pathway_WP69 IL-6_Signaling_Pathway_WP364 IL-3_Signaling_Pathway_WP286 Integrin_alphaIIb_beta3_signaling_WP1832 Regulation_of_toll- IL-4_signaling_Pathway_WP395 like_receptor_signaling_pathway_WP1449 Type_II_interferon_signaling_(IFNG)_WP619
  • 71. Input files §  1 input file §  Comma Separated Values (.csv) §  1 header line + 1 line for each sample §  Required fields §  Sample ID §  Replicate ID §  Tissue §  Read file (bam) §  Factor §  Control read file (bam) §  Condition §  Peak file
  • 72. Analysis Pipeline §  Data used in the analysis §  Read files §  Peaksets (Homer, MACS) §  2 pipelines §  Occupancy Analysis §  No quality control §  Less strict §  Differential Binding Analysis §  Quality control §  (Less flexible)
  • 73. Analysis Pipeline §  Creating a 'dba' object §  Output based on raw data §  Definition of consensus peakset §  Read counting §  Normalization §  Output based on the consensus peakset §  Contrast, blocking, masking, etc.
  • 74. Analysis Pipeline §  Differential Binding Analysis §  Output based on differentially bound sites §  Heatmaps (correlation, expression) §  Plots (MA, PCA, boxplot) §  Venn diagrams §  Save the peaksets for later use
  • 75. 75 Control vs. LG268 Significant changes Significant changes DiffBind: Attila Horváth 813
  • 76. 76 Control vs. LG268 DiffBind: Attila Horváth
  • 77. 77 Motives are close to peak summits RXR ChIP peaks Width   p-­‐value  target  %   bg  %   fold   50   1E-­‐74   23.17   4.66   4.97   60   1E-­‐91   28.62   5.94   4.82   PU.1   80   1E-­‐142   34.02   5.32   6.39   100   1E-­‐169   44.24   8.28   5.34   50   1E-­‐151   39.27   6.96   5.64   60   1E-­‐184   41.05   6.02   6.82   NRhalf   80   1E-­‐254   52.78   7.63   6.92   100   1E-­‐258   59.23   10.39   5.70   p-­‐value   target  %   bg  %   fold   1E-­‐86   18.97   5.34   3.55   PU.1   1E-­‐135   21.85   4.62   4.73   1E-­‐169   44.24   8.28   5.34   1E-­‐18   0.64   0.01   64.00   NRhalf   1E-­‐52   9.52   2.18   4.37   1E-­‐258   59.23   10.39   5.70  
  • 78. RXR enhancers from different tissues 78 around the Tgm2 gene Adipocytes Liver BMDM
  • 79. 79 Motives on RXR ChIP-seq peaks in different cells (HOMER de novo motif finding) Motives in BMDM: BMDM RXR •  PU.1 •  NR half •  AP-1 AP-1 •  CEPB PU.1 •  RUNX CEBP •  NR DR1 L1_4h RXR •  IRF Liver RXR AP1 CEBP NF1 half STAT L1_D6 RXR NF1 CTCF NF1 NF1 half CEBP
  • 80. 80 Motif matrix files and logos RGKKSANRGKKSA! DRGGTCARAGGTCARN! >Consensus sequence Name Score threshold >Consensus sequence Name >RGKKSANRGKKSA DR1 11.0523208315125 Score threshold R! 0.499 0.001 0.499 0.001 D! >DRGGTCARAGGTCARN 1-DRGGTCARAGGTCARN G! 0.001 0.001 0.997 0.001 R! 8.661417 * K! 0.001 0.001 0.499 0.499 G! 0.367 0.067 0.311 0.256 K! 0.001 0.001 0.499 0.499 G! 0.466 0.001 0.532 0.001 S! 0.001 0.499 0.499 0.001 T! 0.189 0.022 0.656 0.133 A! 0.997 0.001 0.001 0.001 C! 0.111 0.100 0.667 0.122 N! 0.25 0.25 0.25 0.25 A! 0.133 0.256 0.156 0.455 R! 0.499 0.001 0.499 0.001 R! 0.166 0.512 0.222 0.100 G! 0.001 0.001 0.997 0.001 A! 0.966 0.001 0.011 0.022 K! 0.001 0.001 0.499 0.499 G! 0.378 0.067 0.477 0.078 K! 0.001 0.001 0.499 0.499 G! 0.821 0.001 0.177 0.001 S! 0.001 0.499 0.499 0.001 T! 0.066 0.011 0.922 0.001 A! 0.997 0.001 0.001 0.001 C! 0.044 0.022 0.767 0.167 A! 0.111 0.089 0.134 0.666 R! 0.078 0.855 0.045 0.022 N! 0.900 0.001 0.022 0.077 0.278 0.211 0.378 0.134 0.300 0.211 0.222 0.267 Log P value Unused Match in Target and Background, P value * -226.958685 0 T:159.0(19.06%),B:970.0(2.08%),P:1e-98
  • 81. 81 RXR-PU.1 tethering Motif enrichment PU.1 1e-650 83.75% 7.20% AP-1 1e-29 19.81% 7.59% RUNX 1e-24 8.78% 2.04% c/EBP 1e-17 3.32% 0.35% Half 1e-15 4.74% 0.97% SREBP? 1e-14 3.91% 0.70% RGKKSA! <<<<<28.46% of peaks
  • 82. 82 ChIP-Seq Reads / tags Quality improvement: (fastq format) pipeline clippers/trimmers Burrows-Wheeler BWA Alignment Tool Alignment files (bam) Hypergeometric Homer2 Model Based Optimization of makeTagdirectory MACS2 Analysis for w/o input Normalizations, Motif makeUCSCfile* callpeak* ChIP-Seq data strand specificity EnRichment findPeaks *Genome coverage files (bedgraph) Peak files (bed) Peak files (bed) (no summit) Bash Contrasts DiffBind Differential Binding Analysis of ChIP- Bash sort, head edgeR, limma Seq peak data sort, head Significant peaks Significant peaks Significant peaks (top 1000 “summits”) (top 1000 summits) (top 1000 summits) Homer2 Directed motif finding Homer Tagdirectory because of masked out motives findMotifsGenome Motif matrices/logos Redundancy BEDTools Functional Homer2 beds intersectBed annotation of findMotifsGenome binding and Whatever infile modification loci Gene list PeakAnnotator beds lists VennMaster Meta-histogram files Motif files (bed) (txt) IP efficiency Overlaps, distances
  • 83. Chipster (ngs tools from version 2.0) ChIP-seq chipster tutorial session with Eija Korpelainen COST conference Valencia, May, 2013
  • 84. •  Regulatory Sequence Analysis Availability Tools (RSAT) •  http://rsat.ulb.ac.be/rsat/ •  Interfaces •  Stand-alone apps •  Web site •  Web services (SOAP/WSDL API) •  Web interface •  Simplicity of use (“one click” interface). •  Advanced options can be accessed optionally. •  Allows to analyze data set of realistic size (uploaded files).
  • 85. Work flow for chip-seq analysis Reads + quality (fastq) •  ChIP-seq data can be retrieved from Read clean-up specialized databases such as Gene Expression Omnibus (GEO). Data retrieval Cleaned reads •  The GEO database allows to retrieve GEO sequences at various processing stages. Read mapping •  Read sequences: typically, several millions of short sequences (25bp). Alignments •  Read locations: chromosomal Peak calling coordinates of each read. •  Peak locations: several thousands of Peaks variable size regions (typically between 100bp and 10kb). Motif discovery •  A technological bottleneck lies in the next step: exploitation of full peak collections to Over-represented motifs discover motifs and predict binding sites. Pattern matching Binding sites
  • 86. Local over-representation (program local- words) Windows •  The program local-words detects words that are over-represented in specific position windows. •  The result is thus more informative than for position-analysis: in addition to the global positional bias, we detect the precise window where each word is over-represented. C 6-mer (e.g.AACAAA ) Expected occurrences (from whole sequence length) Thomas-Chollier M, Darbo E, Herrmann C, Defrance M, Thieffry D, van Helden J. 2012. A complete workflow for the analysis of full-size ChIP-seq (and similar) data sets using peak-motifs. Nat Protoc 7(8): 1551-1568.
  • 87. Network of motifs discovered in tissue-specific p300 binding regions Thomas-Chollier M, Herrmann C, Defrance M, Sand O, Thieffry D, van Helden J. 2012. RSAT peak-motifs: motif analysis in full-size ChIP-seq datasets. Nucleic Acids Res 40(4): e31.
  • 88. An integrated work flow for analyzing ChIP-seq peaks •  The program peak-motifs is a work flow combining a series of RSAT tools optimized for discovered motifs in large sequence sets (tens of Mb) resulting from ChIP-seq experiments.. •  Multiple pattern discovery algorithms •  Global over-representation •  Positional biases •  Local over-representation •  Discovered motifs are compared with •  motif databases •  user-specified reference motifs. •  Prediction of binding sites, which can be uploaded as custom annotation tracks to genome browsers (e.g. UCSC) for visualization. •  Interfaces •  Stand-alone •  Web interface •  Web services (SOAP/WSDL) Thomas-Chollier M, Herrmann C, Defrance M, Sand O, Thieffry D, van Helden J. 2012. RSAT peak-motifs: motif analysis in full-size ChIP-seq datasets. Nucleic Acids Res 40(4): e31.
  • 93. ChIPseeqer tasks (command line and GUI) •  Peak detection •  Gene-level annotation of peaks •  Pathways enrichment analysis •  Regulatory element analysis, using either a denovo approach, known or user-defined motifs •  Nongenic peak annotation (repeats, CpG island, duplications) •  Conservation analysis •  Clustering analysis •  Visualisation •  Integration and comparison across different ChIP-seq experiments
  • 94. ChIPseeqer workflow Giannopoulou and Elemento BMC Bioinformatics 2011 12:277 doi: 10.1186/1471-2105-12 -277
  • 98. A system-level overview of the core framework of ChIPBase. Yang J et al. Nucl. Acids Res. 2012;nar.gks1060 © The Author(s) 2012. Published by Oxford University Press.
  • 99. Summary of ChIP-seq analysis •  Having a good ChIP is the most important for the analysis •  There are many alternatives for the analysis pipeline •  Analysis speed, percentage of mapped reads etc. are not the most important parameters during the analysis •  Read number influences strongly the peak number •  Our practice is that for a TF ChIP 6-10 million of reads (depends on the expected number of peaks) is a minimum but it could be sufficient (15-20 million of reads at the histone ChIPs) as well •  Having controls and parallels is good, but not always necessary •  The primary analysis can be run easily from scripts or with workflows •  The most difficult and time-consuming part of the process is the downstream analysis
  • 100. Acknowledgments http://genomics.med.unideb.hu/ •  László Nagy director http://nlab.med.unideb.hu/ •  Bálint L Bálint head of the lab László Nagy lab leader Bálint L Bálint senior researcher Bioinformatics team: Zsuzsanna Nagy senior researcher •  Gergely Nagy PhD student Bence Dániel PhD student •  Attila Horváth System administrator, R programmer Zoltán Simándi PhD student •  Dávid Jónás Bioinformatician Péter Brázda PhD student •  László Steiner Mathematician Ixchelt Cuaranta PhD student •  Erik Czipa MSc student