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SSAHA_pileup
- 1. SSAHA_pileup: A Genome Variation Detection Pipeline for Various Sequencing Platforms Photo Credit: saynine on flickr.com Ben Blackburne Wellcome Trust Sanger Institute
- 2. Acknowledgments ●Zemin Ning ●Yong Gu ●Antony Cox ●Adam Spargo ●Hannes Ponstingl
- 3. Introduction ●New sequencing technologies – More data – Different kinds of data ●Solexa, 454 ●capillary, too – Diploid genomes – SNPs, indels, VNTRs Photo Credit: mknowles on flickr.com
- 7. SSAHA_pileup ●Sequence Search and Alignment by Hashing Algorithm ●SSAHA_SNP – Global positioning with SSAHA algorithm – Fast Smith-Waterman implementation (from Cross_Match) – Identification of best match ●SSAHA_pileup – Determines SNPs from set of best alignments ●Works on Solexa, 454, and capillary reads
- 8. The Toolchain Reference Genome SSAHA_snp/ Alignments SSAHA_pileup SSAHA2 variations Reads refinement
- 9. SSAHA_SNP ●Reference genome is “hashed” – table made of all k-mer words – overlapping or not, at user's option
- 10. SSAHA_SNP ●k-mer matches found for query in reference chr n chr m
- 11. SSAHA_SNP chr n Global Mapping chr m
- 12. SSAHA_SNP chr n score: 126 Local Mapping (Smith-Waterman) score: 113 chr m
- 13. SSAHA_SNP chr n score: 126 Select best match score: 113 chr m
- 14. SSAHA_SNP ●Read pair information – currently possible with extra step using SSAHA2 – being integrated into SSAHA_SNP – Removes incorrectly mapped pairs Photo Credit: Matthew Fang on flickr.com
- 15. SSAHA_pileup Reference Genome SSAHA_snp/ Alignments SSAHA_pileup SSAHA2 variations Reads refinement
- 16. SSAHA_pileup Reference ...GGTCCCACAGAGCTGGAGAAAG... GGTCCCACGGAGCTGGAG CCACGGAGCTGGAGAAAGCCT TCCCACGGAGCTGGAGAAAGCCT TCCCACGGAGCTGGAGAAAGCCT TCCCACGGAGCTGGAGAAAGCCT Aligned reads Homozygous SNP
- 17. SSAHA_pileup Reference ...GGTCCCACAGAGCTGGAGAAAG... GGTCCCACAGAGCTGGAG CCACAGAGCTGGAGAAAGCCT TCCCACGGAGCTGGAGAAAGCCT TCCCACGGAGCTGGAGAAAGCCT TCCCACGGAGCTGGAGAAAGCCT Aligned reads Heterozygous SNP
- 18. SSAHA_pileup Reference ...GGTCCCACAGAGCTGGAGAAAG... GGTCCCACAGAGCTGGAG CCACAGAGCTGGAGAAAGCCT TCCCACggagCTGGAGAAAGCCT TCCCACggagcTGGAGAAAGCCT TCCCacggagcTGGAGAAAGCCT Aligned reads Heterozygous SNP?? (Probably not)
- 19. SSAHA_pileup Reference ...GGTCCCACAGAGCTGGAGAAAG... GGTCCCAC-----TGGAG CCAC-----TGGAGAAAGCCT TCCCACGGAGCTGGAGAAAGCCT TCCCACGGAGCTGGAGAAAGCCT TCCCACGGAGCTGGAGAAAGCCT Aligned reads Heterozygous indel
- 20. How well does it work?
- 21. Datasets ●Venter: ABI capillary reads – Celera: 19,397,599 55% in pairs – JCVI: 12,541,352 98% in pairs – Total: 31,938,951 72% in pairs (90% mapped) ●Watson: 454 GS FLX reads – Baylor & Roche 74,198,831 (90.5% mapped) – single end reads with length 150 – 280 bps ●Chromosome X Illumina reads – 278,557,156 reads (71.6% mapped) – (paired with insert size 200bps)
- 22. How conservative should we be?
- 23. How conservative should we be?
- 24. Or.... How liberal should we be?
- 25. How do we even know if we are winning?
- 28. Filtering ●Processes that cause bogus SNPs – Incorrect global mapping – Incorrect local alignment – Poor quality reads – Sequence amplification errors
- 29. Global Mapping Problems ●Reads from unmapped regions of the genome – Lead to absurdly high apparent coverage chr n ` ` ` ` ` ` ` ` `` ` ` `` ` chr m
- 30. Global Mapping Problems ●Reads from unmapped regions of the genome – Lead to absurdly high apparent coverage chr n ` ` ` ` ` ` ` ` `` ` ` `` ` chr m
- 31. Global Mapping Problems ●Reads from unmapped regions of the genome – Lead to absurdly high apparent coverage chr n ` ` ` `` ` ` ` ` ` ` ` `` `
- 32. SNPs
- 33. Solution: Filter out SNPs called from abnormally high read depths
- 34. Global Mapping Problems ●Incorrectly aligned reads chr n ` score: 132 ` score: 136 chr m
- 35. Solution: nd Filter out SNPs where 2 best score is too close
- 36. Local Alignment Problems ●Misalignment – Uncaught incorrect global alignment – Variations in short repeats
- 37. Local Misalignment Reference ...GGTCCCACAGAGCTGGAGAAAA... GGTCCCACT---CTAGTG CCACT---CTAGTGAAAA TCCCACT---CTAGTGAAAA Aligned reads Real SNPs?
- 38. Local Misalignment Reference ..TAATAATAATAATAATAATAAGAAG.. AATAATAAGAAGAAGAAGAAGAAG AATAATAAGAAGAAGAAGAAGAAG AATAATAAGAAGAAGAAGAAGAAG Aligned reads Real SNPs?
- 39. Solution: Filter out short blocks of many SNPs
- 40. Venter SNP Calling (Capillary) count fraction in dbSNP Homozygous SNPs 1 347 806 97.1% Heterozygous SNPs 1 857 167 90.9% Total SNPs 3 204 973 93.5%
- 41. Watson SNP Calling (454) count fraction in dbSNP Homozygous SNPs 1 298 309 93.0% Heterozygous SNPs 1 767 951 63.9% Total SNPs 3 066 260 76.3%
- 42. X Chromosome SNPs (Solexa) count fraction in dbSNP Homozygous SNPs 27 708 92.8% Heterozygous SNPs 63 197 81.8% Total SNPs 90 905 85.1%
- 43. Venter-Watson Overlap 1 593 791 1 611 182 1 455 078 Venter Watson
- 44. X Chromosome Overlap Solexa X reads 40 625 19 978 12 590 17 712 26 502 6 588 22 872 Venter Watson
- 45. Conclusions ●SSAHA_pileup is effective across both new and old sequencing technologies ●Questions – When is a SNP not a SNP? – Homozygous/Heterozygous SNPs
- 46. Conclusions ●SSAHA_pileup is effective across both new and old sequencing technologies ●Questions – When is a SNP not a SNP? – Homozygous/Heterozygous SNPs ●Length matters...? – But it's what you do with it that counts
- 47. Obtaining SSAHA_pileup SSAHA_pileup: ftp://ftp.sanger.ac.uk/pub/zn1/ssaha_pileup/ SSAHA2: http://www.sanger.ac.uk/Software/analysis/SSAHA2/ These Slides: http://slideshare.net/bpb/