1. Polymorphism discovery in next- generation re-sequencing data Gabor T. Marth Boston College Biology Department Illumina workshop, Washington, DC November 19-20, 2007

2. Why we care about genetic variations? underlie phenotypic differences cause inherited diseases allow tracking ancestral human history

3. Variation types Structural variations SNPs Mikkelsen et al. Nature 2007 Epigenetic variations

4. Sequence resources for polymorphism discovery read length bases per machine run 10 bp1,000 bp100 bp 100 Mb 10 Mb 1Mb 1Gb Illumina/Solexa, AB/SOLiD short-read sequencers ABI capillary sequencer 454 pyrosequencer (100 Mb in ~250 bp reads) (1-4 Gb in 25-50 bp reads)

5. 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

6. Talk topics Tools for resequencing read analysis Data mining projects base calling resequenceability analysis read mapping / alignment / assembly SNP calling structural variation discovery read data visualization SNP and short-INDEL discovery in C. elegans Complete mutational profiling in Pichia stipitis

7. …AND the cover on the box Reference-guided read alignment Reference-sequence guided assembly: …they give you the pieces…

8. Some pieces are easier to place than others… …pieces with unique features pieces that look like each other…

9. Resequenceability: unique read placement Reads from repeats cannot be uniquely mapped RepeatMasker does not capture all repeats at the read length scale Near-perfect repeats can be also a problem because of sequencing errors and / or SNPs fraction of reads number of mismatches

10. Finding micro-repeats is not easy Hash based methods (fast but only work out to a couple of mismatches) Exact methods (very slow but find every repeat copy) Heuristic methods (fast but miss a fraction of the repeats)

11. Presenting repeats for downstream analysis masking bases masking fragments bases in repetitive fragments may be resequenced with reads representing other, unique fragments  fragment-level repeat annotations spare a higher fraction of the genome than base-level repeat masking

12. Fragment level annotation is economical

13. Paired-end reads will not make the question go away

14. Read alignment

15. INDELs require gapped alignment ABI/cap. 454/FLX Illumina 454/GS20 sequences, often from different machine types, must be assembled together billions of sequences must be aligned

16. 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

17. MOSAIK – performance Solexa read alignments to C. elegans genome: 100 million reads aligned in 95 minutes 18,000 reads / second 454 reads to Pichia (yeast-size) genome GS20: 2,000 reads / second FLX: 300 reads / second Solexa read alignments to masked human genome: 40 seconds for 1 million reads 18,000 reads / second 5.5 GB RAM used (more for longer initial hash sizes)

18. Polymorphism detection Goal: to discern true variation from sequencing error sequencing error polymorphism

19. Using base quality values use base quality values to help us decide if mismatches are true polymorphisms or sequencing errors

20. Bayesian detection algorithm AAAAAAAAAA CCCCCCCCCC TTTTTTTTTT GGGGGGGGGG polymorphic combination monomorphic combination Bayesian posterior probability i.e. the SNP score Base call + Base quality Expected polymorphism rate Base composition Depth of coverage

21. The PolyBayes software Marth et al. Nature Genetics 1999 http://bioinformatics.bc.edu/~marth/PolyBayes

22. 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

23. SNP calling in short-read coverage C. elegans reference genome (Bristol, N2 strain) Pasadena, CB4858 (1 ½ machine runs) Bristol, N2 strain (3 ½ machine runs) 5 runs (~120 million) Illumina reads from Wash. U. (Elaine Mardis) detect polymorphisms between the Pasadena and the Bristol strain aligned / assembled the reads (< 4 hours on 1 CPU) found 44,642 SNP candidates (2 hours on our 160-CPU cluster) SNP density: 1 in 1,630 bp (of non-repeat genome sequence)

24. Polymorphism discovery in C. elegans SNP calling error rate very low: Validation rate = 97.8% (224/229) Conversion rate = 92.6% (224/242) Missed SNP rate = 3.75% (26/693) SNP INS INDEL candidates validate and convert at similar rates to SNPs: Validation rate = 89.3% (193/216) Conversion rate = 87.3% (193/221)

25. Mutational profiling: deep 454/Illumina data collaboration with Doug Smith at Agencourt Pichia stipitis converts xylose to ethanol (bio-fuel production) one mutagenized strain had especially high conversion efficiency determine where the mutations were that caused this phenotype we resequenced the 15MB genome with 454 Illumina, and SOLiD reads Pichia stipitis reference sequence Image from JGI web site

26. Mutational profiling: comparisons TechnologyCoverageNominal coverageFPFNTotal error 454/FLX2 runs12.9x101 454/FLX1 run9.8x617 Illumina7 lanes53.5x000 Illumina3 lanes23.4x000 Illumina2 lanes15.6x202 Illumina1 lane7.6x222 SOLiD-30.0X000 SOLiD-20.0X000 SOLiD-10.0X000 SOLiD-8.0X044 SOLiD-6.0X066

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

28. Credits http://bioinformatics.bc.edu/marthlab Elaine Mardis (Washington 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

29. Resequencing of diploid individual genomes Ind. 1 Ind. 2 Ind. 3 Ind. 4

30. How do we find sequence variations? compare multiple sequences from the same genome region

31. Resequencing applications of next-gen sequencers Emerging applications: DNA-protein interaction analysis (CHiP-Seq) epigenetic analysis (methylation profiling) novel transcript discovery quantification of gene expression Polymorphism discovery: organismal SNP discovery complete mutational profiling individual human resequencing for SNP, INDEL and structural variation discovery DEL SNP reference genome resequenced individual

32. Task 5. Dealing with massive data volumes Short-read format working group ssrformat@ubc.ca (Asim Siddiqui, UBC) Assembly format working group Boston College http://assembly.bc.edu two connected working groups to define standard data formats

33. SNP calling in low 454 coverage with Andy Clark (Cornell) and Elaine Mardis (Wash. U.) 10 different African and American melanogaster isolates 10 runs of 454 reads (~300,000 reads per isolate) (~1.5X total) can we detect SNPs in survey-style 454 read coverage? DNA courtesy of Chuck Langley, UC Davis base-calling with PYROBAYES alignment to 120 Mb euchromatic reference sequence with MOSAIK SNP detection with POLYBAYES

34. SNP calling results iso-1 reference 46-2 454 read 46-2 ABI reads (2 fwd + 2 rev) 92.9 % validation rate (1,342 / 1,443) 2.0% missed SNP rate (25 / 1247) 658,280 SNPs Ѳ ≈ 5x10 -3 (1 SNP / 200 bp)

35. Flow signal vs. actual base number

36. Reference-guided read alignment

37. PYROBAYES: A 454 base caller program better correlation between assigned and measured quality values higher fraction of high-quality bases Aaron Quinlan

38. 454 errors: over and under-calls

39. Validation / score calibration

40. Traditional SNP discovery data capillary sequences (ABI) clonal (haploid) sequences

41. The SNP score polymorphism specific variation

42. SNPs and short INDELs Single-base substitutions (SNPs) Insertion-deletion polymorphisms (INDELs)

43. Structural variations Translocations: DNA exchange between different chromosomes Inversion of long chromosomal tracts “Simple” duplications and deletions Multiple duplications (copy number changes)

44. Epigenetic variations Epigenetic variations e.g. changes in methylation / chromatin structure that do not strictly involve base changes Mikkelsen et al. Nature 2007

45. Task 1. Base calling / base accuracy estimation how do we translate the machine readouts to base calls? how do we estimate and represent sequencing errors (base quality values)?

46. 454 pyrosequencing errors

47. 454 pyrosequencer error profile INDEL errors dominate

48. 454 base quality values most bases have low quality values, not optimal for SNP discovery native 454 base quality values underestimate true accuracy

49. Illumina/Solexa base accuracy Most errors are substitutions  PHRED quality values work Measured base quality is a function of base position within the read (i.e. there is need for quality value calibration)

50. Illumina/Solexa base accuracy error rate grows as a function of base position within the read a large fraction of the reads contains 1 or 2 errors

51. Task 2. Read mapping and assembly … is similar to a jigsaw puzzle… … that you have to put together all by yourself De novo assembly:

52. Structural variation discovery copy number variations (deletions & amplifications) can be detected from variations in the depth of read coverage structural rearrangements (inversions and translocations) require paired-end reads

53. Task 4. Data visualization make screenshot with annotation

54. Applications 2. Mutational profiling in deep 454 and Illumina read data (Pichia stipitis) 1. SNP and INDEL discovery in deep Illumina short-read coverage (Caenorhabditis elegans) (image from Nature Biotech.)