1. Next-generation sequencing: informatics & software aspects Gabor T. Marth Boston College Biology Department

2. Technologies

3. Roche / 454 system pyrosequencing technology variable read-length the only new technology with >100bp reads

4. Illumina / Solexa Genome Analyzer fixed-length short-read sequencer very high throughput read properties are very close to traditional capillary sequences low INDEL error rate

5. 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-reads very high throughput 2-base encoding system color-space informatics

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

7. Data characteristics

8. Read length read length [bp] 0 100200300 ~200-450 (variable) 25-70 (fixed) 25-50 (fixed) 20-60 (variable) 400

9. Paired fragment-end reads fragment amplification: fragment length 100 - 600 bp fragment length limited by amplification efficiency 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) instrumental for structural variation discovery circularization: 500bp - 10kb (sweet spot ~3kb) fragment length limited by library complexity

10. Representational biases this affects genome resequencing (deeper starting read coverage is needed) will have major impact is on counting applications “dispersed” coverage distribution

11. 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 into every clonal copy

12. Read quality

13. Error rate (Solexa)

14. Error rate (454)

15. Per-read errors (Solexa)

16. Per read errors (454)

17. Applications

18. Genome resequencing for variation discovery SNPs short INDELs structural variations the most immediate application area

19. Genome resequencing for mutational profiling Organismal reference sequence likely to change “classical genetics” and mutational analysis

20. De novo genome sequencing Lander et al. Nature 2001 difficult problem with short reads promising, especially as reads get longer

21. Identification of protein-bound DNA Chromatin structure (CHIP-SEQ) (Mikkelsen et al. Nature 2007) Transcription binding sites. (Robertson et al. Nature Methods, 2007) DNA methylation. (Meissner et al. Nature 2008) natural applications for next-gen. sequencers

22. Transcriptome sequencing: transcript discovery Mortazavi et al. Nature Methods 2008 Ruby et al. Cell, 2006 high-throughput, but short reads pose challenges

23. Transcriptome sequencing: expression profiling Jones-Rhoads et al. PLoS Genetics, 2007 Cloonan et al. Nature Methods, 2008 high-throughput, short-read sequencing should make a major impact, and potentially replace expression microarrays

24. Analysis software

25. Individual resequencing (iii) read assembly REF (ii) read mapping IND (i) base calling IND (iv) SNP and short INDEL calling (vi) data validation, hypothesis generation (v) SV calling

26. The variation discovery “toolbox” base callers read mappers SNP callers SV callers assembly viewers

27. 1. Base calling base sequence base quality (Q-value) sequence diverse chemistry & sequencing error profiles

28. 454 pyrosequencer error profile multiple bases in a homo-polymeric run are incorporated in a single incorporation test  the number of bases must be determined from a single scalar signal  the majority of errors are INDELs

29. 454 base quality values the native 454 base caller assigns too low base quality values

30. PYROBAYES: determine base number

31. PYROBAYES: Performance better correlation between assigned and measured quality values higher fraction of high-quality bases

32. … and they give you the picture on the box 2. Read mapping Read mapping is like doing a jigsaw puzzle… …you get the pieces… Unique pieces are easier to place than others…

33. Non-uniqueness of reads confounds mapping Reads from repeats cannot be uniquely mapped back to their true region of origin RepeatMasker does not capture all micro-repeats, i.e. repeats at the scale of the read length

34. Strategies to deal with non-unique mapping 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) 0.8 0.190.01 read mapping to multiple loci requires the assignment of alignment probabilities

35. Paired-end reads help unique read placement fragment amplification: fragment length 100 - 600 bp fragment length limited by amplification efficiency Korbel et al. Science 2007 circularization: 500bp - 10kb (sweet spot ~3kb) fragment length limited by library complexity PE MP PE reads are now the standard for genome resequencing

36. MOSAIK

37. Error types are very different Illumina 454

38. Gapped alignments

39. Aligning multiple read types together ABI/capillary 454 FLX 454 GS20 Illumina Alignment and co- assembly of multiple reads types permits simultaneous analysis of data from multiple sources and error characteristics

40. MOSAIK is one of the fastest read mappers

41. 3. Polymorphism / mutation detection sequencing error polymorphism

42. SNP calling with Bayesian mathematics

43. New challenges for SNP calling deep alignments of 100s / 1000s of individuals trio sequences

44. Allele discovery is a multi-step sampling process Population SamplesReads

45. Capturing the allele in the sample

46. Allele calling in deep sequence data aatgtagtaAgtacctac aatgtagtaCgtacctac aatgtagtaAgtacctac Q30Q40Q50Q60 10.01 0.10.5 20.821.0 3

47. More samples or deeper coverage / sample? Shallower read coverage from more individuals … …or deeper coverage from fewer samples? simulation analysis by Aaron Quinlan

48. Analysis indicates a balance

49. SNP calling in trios the child inherits one chromosome from each parent there is a small probability for a mutation in the child

50. SNP calling in trios aatgtagtaAgtacctac aatgtagtaCgtacctac aatgtagtaAgtacctac aatgtagtaCgtacctac mother father child P=0.79 P=0.86

51. 4. Structural variation discovery

52. SV events from PE read mapping patterns

53. Deletion: Aberrant positive mapping distance

54. Copy number estimation from depth of coverage

55. Spanner – a hybrid SV/CNV detection tool Navigation bar Fragment lengths in selected region Depth of coverage in selected region

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

57. Data visualization

58. New analysis tools are needed 1.Tailoring existing tools for specialized applications (e.g. read mappers for transcriptome sequencing) 2.Analysis pipelines and viewers that focus on the essential results e.g. the few mutations in a mutant, or compare 1000 genome sequences (but hide most details) 3.Work-bench style tools to support downstream analysis

59. Data storage and data standards

60. What level of data to store? images traces base quality values base-called reads

61. Data standards different data storage needs (archival, transfer, processing) often poses contradictory requirements (e.g. normalized vs. non-normalized storage of assembly, alignment, read, image data) even different analysis goals often call for different optimal storage / data access strategies (e.g. paired-end read analysis for SV detection vs. SNP calling) requirements include binary formats, fast sequential and / or random access, and flexible indexing (e.g. an entire genome assembly can no longer reside in RAM)

62. Data standards (II) Sequence Read Format, SRF (Asim Siddiqui, UBC) ssrformat@ubc.ca Assembly format working group http://assembly.bc.edu Genotype Likelihood Format (Richard Durbin, Sanger)

63. Summary

64. Conclusions: next-gen sequencing software Next-generation sequencing is a boon for mass-scale human resequencing, whole-genome mutational profiling, expression analysis and epigenetic studies Informatics tools already effective for basic applications There is a need both for “generic” analysis tools e.g. flexible read aligners and for specialized tools tailored to specific applications (e.g. expression profiling) Move toward tools that focus on biological analysis Most challenges are technical in nature (e.g. data storage, useful data formats, fast read mapping)… many of these will be addressed at this conference