Next-generation sequencing and quality control: An Introduction (2016)
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The document provides an overview of next-generation sequencing (NGS) and quality control, detailing the workflow of genomics experiments, including DNA sequencing, sequencing errors, and data quality assessment. It introduces the FASTQ file format, the sources of sequencing errors, and methods for assessing and preprocessing sequencing data to ensure reliable results. The learning outcomes emphasize understanding NGS, quality scoring, and the distinction between good and bad sequencing runs.
Next-generation sequencing and quality control: An Introduction (2016)
- 1. Sebastian Schmeier s.schmeier@massey.ac.nz http://sschmeier.com/bioinf-workshop/ Next-generation sequencing and quality control: An introduction 2016
- 2. Sebastian Schmeier Overview • Typical workflow of a genomics experiment • Genome versus transcriptome • DNA sequencing • The FASTQ-file format • Sources of sequencing errors • Quality assessment of a sequencing run • Pre-processing sequencing data 2 DNA sequencing technologies Quality assessment of a sequencing run Pre-processing sequencing data
- 3. Sebastian Schmeier Learning outcomes • Being able to describe what next generation sequencing is. • Being able to describe the FastQ-sequence format and understand what information it contains and how it can be used. • Describe what the Phred-based quality score is. • Be able to describe the sources of sequencing errors. • Being able to compute, investigate and evaluate the quality of sequence data from a sequencing experiment. • Be able to distinguish between a good and a bad sequencing run. • Being able to describe the steps involved in cleaning sequencing data. 3
- 4. Sebastian Schmeier Typical workflow of a genomics experiment 4 Scientific question Experimental design Run the experiment Assess data quality Analyse the data This is where the biological experiment happens This is the bottleneck of the whole experiment The essential part to make all downstream analysis work This is the bottleneck of the whole experiment This is where the biological experiment happens The essential part to make all downstream analysis work Meaningful results
- 5. Sebastian Schmeier Genome versus transcriptome Genome The entirety of an organism's ancestral information. It is encoded either in DNA or, for many types of viruses, in RNA. Transcriptome The set of all RNA molecules, including messenger RNA, ribosomal RNA, transfer RNA, and other non- coding RNA produced in one or a population of cells 5 Name Base Pairs HIV 9,749 9.7kb E.Coli 4,600,000 4.6MB Yeast 12,100,000 12.1Mb Drosophila 130,000,000 130MB Homo sapiens 3,200,000,000 3.2GB marbled lungfish 130,000,000,000 130Gb "Amoeba" dubia 670,000,000,000 670Gb
- 6. Sebastian Schmeier DNA sequencing DNA sequencing is the process of determining the nucleotide order of a given DNA fragment. First-generation sequencing: 1977 Sanger sequencing method development (chain-termination method) 2001, Sanger method produced a draft sequence of the human genome Next-generation sequencing (NGS) Demand for low-cost sequencing has driven the development of high- throughput sequencing (or NGS) technologies that parallelize the sequencing process, producing thousands or millions of sequences concurrently 2004 454 Life Sciences marketed a parallelized version of pyrosequencing 6
- 7. Sebastian Schmeier Result of a sequencing run Short read sequences • The result of NGS technology are a collection of short nucleotide sequences (reads) of varying length (~40-400nt) depending on the technology used to generate the reads • Usually a reads quality is good at the beginning of the read and errors accumulate the longer the read gets => IMPORTANT 7
- 8. Sebastian Schmeier Illumina sequencing MiSeq: • Bench-top sequencer • Produces around 30 million reads/run • Reads are up to 250nt HiSeq: • Large-scale sequencer • 4 billion reads/run • Reads up to 150nt The Illumina systems accumulate errors towards the end of the read sequence. 8 http://www.illumina.com/
- 9. Sebastian Schmeier Illumina sequencing (2) • An Illumina flowcell is a surface to which seq. adaptors are covalently attached. • DNA with complementary adaptors is attached, clonally amplified, and then sequenced by synthesis • Each flowcell is subdivided into hundreds of tiles 9
- 10. Sebastian Schmeier Illumina sequencing (3) 10Addressing challenges in the production and analysis of illumina sequencing data. Kirchner et al. BMC Genomics, 2011,12:382
- 11. Sebastian Schmeier The FASTQ-file format The file-format that you will encounter soon is called FASTQ 11 @EAS139:136:FC706VJ:2:2104:15343:197393 1:Y:18:ATCACG GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTC + ‘'*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>
- 12. Sebastian Schmeier The FASTQ-file format (2) The file-format that you will encounter soon is called FASTQ 12 @EAS139:136:FC706VJ:2:2104:15343:197393 1:Y:18:ATCACG GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTC + ‘'*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>> Sequence ID
- 13. Sebastian Schmeier The FASTQ-file format (3) The file-format that you will encounter soon is called FASTQ 13 @EAS139:136:FC706VJ:2:2104:15343:197393 1:Y:18:ATCACG GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTC + ‘'*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>> Sequence ID Sequence
- 14. Sebastian Schmeier The FASTQ-file format (4) The file-format that you will encounter soon is called FASTQ 14 @EAS139:136:FC706VJ:2:2104:15343:197393 1:Y:18:ATCACG GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTC + ‘'*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>> Sequence ID Sequence Phred quality of the corresponding nucleotide (ASCII code)
- 15. Sebastian Schmeier The FASTQ-file format (5) The file-format that you will encounter soon is called FASTQ 15 @EAS139:136:FC706VJ:2:2104:15343:197393 1:Y:18:ATCACG GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTC + ‘'*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>> Sequence ID Casava 1.8 the format
- 16. Sebastian Schmeier Sequencing errors • Sequencing errors or mis-called bases occur when a sequencing method calls one or more bases incorrectly leading to an incorrect read. • The chance of a sequencing error is generally known and quantifiable, thanks to extensive testing and calibration of the sequencing machines • Each base in a read is assigned a quality score, indicating confidence that the base has been called correctly. 16https://www.broadinstitute.org/crd/wiki/index.php/Sequencing_error
- 17. Sebastian Schmeier The FASTQ-file format (6) • FastQ: Phred base quality • One ASCII character per nucleotide. • Encodes for a quality Q = -10*log10(P), where P is the error probability 17 @EAS139:136:FC706VJ:2:2104:15343:197393 1:Y:18:ATCACG GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTC + ‘'*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>> Phred quality of the corresponding nucleotide (ASCII code) -10*log10(0.1) =
- 18. Sebastian Schmeier Sources of sequencing errors • The importance and the relative effect of each error source on downstream applications depend on many factors, such as: sample acquisition reagents tissue type protocol 18The role of replicates for error mitigation in next-generation sequencing. Robasky et al. Nature Reviews Genetics, 2014, 15, 56-62 instrumentation experimental conditions analytical application the ultimate goal of the study.
- 19. Sebastian Schmeier Sources of sequencing errors (2) Sequencing errors can stem from any time point throughout the experimental workflow, including initial sequence preparation, library preparation and sequencing. 19The role of replicates for error mitigation in next-generation sequencing. Robasky et al. Nature Reviews Genetics, 2014, 15, 56-62
- 20. Sebastian Schmeier Sources of sequencing errors (3) 20The role of replicates for error mitigation in next-generation sequencing. Robasky et al. Nature Reviews Genetics, 2014, 15, 56-62 Sample preparation • User errors; for example, mislabelling • Degradation of DNA and/or RNA from preservation methods; for example, tissue autolysis, nucleic acid degradation and crosslinking during the preparation of formalin-fixed, paraffin-embedded (FFPE) tissues • Alien sequence contamination; for example, those of mycoplasma and xenograft hosts • Low DNA input
- 21. Sebastian Schmeier Sources of sequencing errors (4) 21The role of replicates for error mitigation in next-generation sequencing. Robasky et al. Nature Reviews Genetics, 2014, 15, 56-62 Library preparation • User errors; for example, carry-over of DNA from one sample to the next and contamination from previous reactions • PCR amplification errors • Primer biases; for example, binding bias, methylation bias, biases that result from mispriming, nonspecific binding and the formation of primer dimers, hairpins and interfering pairs, and biases that are introduced by having a melting temperature that is too high or too low • 3ʹ-end capture bias that is introduced during poly(A) enrichment in high-throughput RNA sequencing • Private mutations; for example, those introduced by repeat regions and mispriming over private variation • Machine failure; for example, incorrect PCR cycling temperatures • Chimeric reads • Barcode and/or adaptor errors; for example, adaptor contamination, lack of barcode diversity and incompatible barcodes
- 22. Sebastian Schmeier Sources of sequencing errors (5) 22The role of replicates for error mitigation in next-generation sequencing. Robasky et al. Nature Reviews Genetics, 2014, 15, 56-62 Sequencing and imaging • User errors; for example, cluster crosstalk caused by overloading the flow cell • Dephasing; for example, incomplete extension and addition of multiple nucleotides instead of a single nucleotide • ‘Dead’ fluorophores, damaged nucleotides and overlapping signals • Sequence context; for example, GC richness, homologous and low-complexity regions, and homopolymers • Machine failure; for example, failure of laser, hard drive, software and fluidics • Strand biases
- 23. Sebastian Schmeier Sources of sequencing errors (6) It is important to remember that one get somatic (acquired) mutations as well.These are not sequencing error but can be mistaken for them. 23The role of replicates for error mitigation in next-generation sequencing. Robasky et al. Nature Reviews Genetics, 2014, 15, 56-62
- 24. Sebastian Schmeier Assessing quality: Reads • If the quality of the reads is bad we can trim the nucleotides that are bad of the end of the reads • Not trimming the end has a huge influence on downstream processes, e.g. assemblies 24 Good Bad Trimming needed Position in read Position in read Quality http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
- 25. Sebastian Schmeier 25 Assessing quality: Reads (2) http://solexaqa.sourceforge.net/
- 26. Sebastian Schmeier Assessing quality:Tiles • One can assess also the quality of a run based on the tiles of a lane of a flowcell spot problems with a particular tile on a lane, e.g. Bubbles in the reagents • The homogeneity of the Illumina process ensures that the relative frequencies are similar from tile to tile and distributed uniformly across each tile when the machine is functioning properly • Major discrepancies in these conditions can be discerned by sight • Many such discrepancies are small and their effects are limited to one, or a few, tiles. 26
- 27. Sebastian Schmeier Assessing quality:Tiles (2) • Encoded in the FASTQ-file is the flowcell tile from which each read came. • The graph allows you to look at the quality scores from each tile across all of your bases to see if there was a loss in quality associated with only one part of the flowcell. 27 Good run Bad run http://solexaqa.sourceforge.net/
- 28. Sebastian Schmeier Assessing quality:Tiles (3) • The plot shows the deviation from the average quality for each tile. • The colours are on a cold to hot scale • Cold colours being positions where the quality was at or below the average for that base in the run • Hotter colours indicate that a tile had worse qualities than other tiles for that base. 28 Good run Bad run http://solexaqa.sourceforge.net/
- 29. Sebastian Schmeier Assessing quality:Tiles (4) 29 Good run Bad run http://solexaqa.sourceforge.net/
- 30. Sebastian Schmeier Assessing quality: Data processing • Adapter trimming: If not already done, we can remove the adapter used for sequencing. 30 DNA sequence of interest Universal adapter Indexed adapter 6 base index region Example for IlluminaTrueSeq
- 31. Sebastian Schmeier Assessing quality: Data processing (2) • Filtering:We can remove all reads that do not have a particular quality over the read length, e.g. at least q20 for 80% of the read 31 good quality bad quality Too many errors? Discard X
- 32. Sebastian Schmeier Assessing quality: Data processing (3) • Cropping:We can try to remove all nt from both ends that do not fulfil a certain quality 32 good quality bad quality
- 33. Sebastian Schmeier Assessing quality: Data processing (4) • Removal: We can remove reads that are too short after cropping. 33 good quality bad quality Fragment too short? Discard X
- 34. Sebastian Schmeier Assessing quality: Data processing (5) • Adapter trimming: If not already done, we can remove the adapter used for sequencing • Filtering:We can remove all reads that do not have a particular quality over the read length, e.g. at least q20 for 80% of the read • Cropping:We can try to remove all nt from both ends that do not fulfil a certain quality • Removal: We can remove reads that are too short after cropping. In the end, we work with an adjusted set of sequencing reads for which we are more certain that they represent correct nt sequences from the genome => However filtering/trimming does not always improve things as we loose information 34
- 35. Sebastian Schmeier s.schmeier@massey.ac.nz http://sschmeier.com/bioinf-workshop/ References The role of replicates for error mitigation in next-generation sequencing. Robasky et al. Nature Reviews Genetics, 2014, 15, 56-62 Addressing challenges in the production and analysis of illumina sequencing data. Kirchner et al. BMC Genomics, 2011,12:382