1. Data analysis methods for next- generation sequencing technologies Gabor T. Marth Boston College Biology Department Epigenomics & Sequencing Meeting July 14-15, 2008, Boston, MA

2. T1. Roche / 454 FLX system pyrosequencing technology variable read-length the only new technology with >100bp reads tested in many published applications supports paired-end read protocols with up to 10kb separation size

3. T2. Illumina / Solexa Genome Analyzer fixed-length short-read sequencer read properties are very close traditional capillary sequences very low INDEL error rate tested in many published applications paired-end read protocols support short (<600bp) separation

4. T3. AB / SOLiD system ACGT A C G T 2 nd Base 1 st Base 0 0 0 0 1 1 1 1 2 2 2 2 3 3 3 3 fixed-length short-read sequencer employs a 2-base encoding system that can be used for error reduction and improving SNP calling accuracy requires color-space informatics published applications underway / in review paired-end read protocols support up to 10kb separation size

5. T4. Helicos / Heliscope system experimental short-read sequencer system single molecule sequencing no amplification variable read-length error rate reduced with 2- pass template sequencing

6. A1. Variation discovery: SNPs and short-INDELs 1. sequence alignment 2. dealing with non-unique mapping 3. looking for allelic differences

7. A2. Structural variation detection structural variations (deletions, insertions, inversions and translocations) from paired-end read map locations copy number (for amplifications, deletions) from depth of read coverage

8. A3. Identification of protein-bound DNA genome sequence aligned reads Chromatin structure (CHIP-SEQ) (Mikkelsen et al. Nature 2007) Transcription binding sites. Robertson et al. Nature Methods, 2007

9. A4. Novel transcript discovery (genes) Mortazavi et al. Nature Methods

10. A5. Novel transcript discovery (miRNAs) Ruby et al. Cell, 2006

11. A6. Expression profiling by tag counting aligned reads Jones-Rhoads et al. PLoS Genetics, 2007 gene

12. A7. De novo organismal genome sequencing assembled sequence contigs short reads longer reads read pairs Lander et al. Nature 2001

13. C1. Read length read length [bp] 0 100200300 ~200-450 (var) 25-40 (fixed) 25-35 (fixed) 20-35 (var) 400

14. When does read length matter? short reads often sufficient where the entire read length can be used for mapping: SNPs, short-INDELs, SVs CHIP-SEQ short RNA discovery counting (mRNA miRNA) longer reads are needed where one must use parts of reads for mapping: de novo sequencing novel transcript discovery aacttagacttaca gacttacatacgta Known exon 1Known exon 2 accgattactatacta

15. C2. Read error rate error rate dictates the stringency of the read mapper error rate typically 0.4 - 1% the more errors the aligner must tolerate, the lower the fraction of the reads that can be uniquely aligned

16. Error rate grows with each cycle this phenomenon limits useful read length

17. Substitutions vs. INDEL errors

18. C3. Representational biases / library complexity fragmentation biases amplification biases PCR sequencing biases sequencing low/no representation high representation

19. Dispersal of read coverage this affects variation discovery (deeper starting read coverage is needed) it should have major impact is on counting applications

20. Amplification errors many reads from clonal copies of a single fragment early PCR errors in “clonal” read copies lead to false positive allele calls early amplification error gets propagated onto every clonal copy

21. C4. Paired-end reads fragment amplification: fragment length 100 - 600 bp fragment length limited by amplification efficiency circularization: 500bp - 10kb (sweet spot ~3kb) fragment length limited by library complexity Korbel et al. Science 2007 paired-end read can improve read mapping accuracy (if unique map positions are required for both ends) or efficiency (if fragment length constraint is used to rescue non-uniquely mapping ends)

22. Technologies / properties / applications Technology Roche/454Illumina/SolexaAB/SOLiD Read properties Read length200-450bp20-50bp25-50bp Error rate<0.5%<1.0%<0.5% Dominant error typeINDELSUB Quality values availableyes not really Paired-end separation< 10kb (3kb optimal)100 - 600bp500bp - 10kb (3kb optimal) Applications SNP discovery●●○ short-INDEL discovery ●○ SV discovery○○● CHIP-SEQ○●● small RNA/gene discovery○●● mRNA Xcript discovery●○○ Expression profiling○●● De novo sequencing● ??

23. Resequencing-based SNP discovery (iv) read assembly REF (iii) read mapping (pair-wise alignment to genome reference) IND (v) SNP calling (vi) SNP validation (ii) micro-repeat analysis (vii) data viewing, hypothesis generation

24. The “toolbox” base callers microrepeat finders read mappers SNP callers structural variation callers assembly viewers

25. …AND they give you the cover on the box Reference guided read mapping Reference-sequence guided mapping: …you get the pieces… Some pieces are more unique than others

26. MOSAIK: an anchored aligner / assembler Step 1. initial short-hash scan for possible read locations Step 2. evaluation of candidate locations with SW method Michael Stromberg

27. Non-unique mapping, gapped alignments 1. Non-unique read mapping: optionally either only report uniquely mapped reads or report all map locations for each read (mapping quality values for all mapped reads are being implemented) 2. Gapped alignments: allow for mapping reads with insertion or deletion sequencing errors, and reads with bona fide INDEL alleles

28. Read types aligned, paired-end read strategy 3. Aligns and co-assembles customary read types: ABI/capillary Illumina/Solexa AB/SOLiD Roche/454 Helicos/Heliscope ABI/capillary 454 FLX 454 GS20 Illumina 4. Paired-end read alignments

29. Other mainstream read mappers ELAND (Tony Cox, Illumina) -- the “official” read mapper supplied by Illumina, fast MAQ (Li Heng + Richard Durbin, Sanger) -- the most widely used read mapper, low RAM footprint SOAP (Beijing Genomics Institute) -- a new mapper developed for human next-gen reads SHRIMP (Michael Brudno, University of Toronto) -- full Smith-Waterman

30. Speed

31. Polymorphism / mutation detection sequencing error polymorphism

32. Determining genotype directly from sequence AACGTTAGCATA AACGTTCGCATA AACGTTAGCATA individual 1 individual 3 individual 2 A/C C/CC/C A/A

33. Software SNP INS

34. Data visualization 1.aid software development: integration of trace data viewing, fast navigation, zooming/panning 2.facilitate data validation (e.g. SNP validation): co-viewing of multiple read types, quality value displays 3.promote hypothesis generation: integration of annotation tracks Weichun Huang

35. Applications 1. SNP discovery in shallow, single-read 454 coverage (Drosophila melanogaster) 3. Mutational profiling in deep 454 and Illumina read data (Pichia stipitis) 2. SNP and INDEL discovery in deep Illumina short-read coverage (Caenorhabditis elegans) (image from Nature Biotech.)

36. Our software is available for testing http://bioinformatics.bc.edu/marthlab/Beta_Release

37. Credits http://bioinformatics.bc.edu/marthlab Elaine Mardis (Washington University) Andy Clark (Cornell University) Doug Smith (Agencourt) Research supported by: NHGRI (G.T.M.) BC Presidential Scholarship (A.R.Q.) Derek Barnett Eric Tsung Aaron Quinlan Damien Croteau-Chonka Weichun Huang Michael Stromberg Chip Stewart Michele Busby

38. Accuracy As is the case for all heuristic alignment algorithms accuracy and speed are option- and parameter-dependent

39. C3. Quality values are important for allele calling PHRED base quality values represent the estimated likelihood of sequencing error and help us pick out true alternate alleles inaccurate or not well calibrated base quality values hinder allele calling Q-values should be accurate … and high!

40. Software tools for next-gen sequence analysis

41. Next-generation sequencing technologies and applications