1. Canadian Bioinformatics Workshops www.bioinformatics.ca

2. Cold Spring Harbor Laboratory & New York Genome Center In collaboration with

3. 3Module #: Title of Module

4. Module 6 Structural variant calling

5. Module 6: Structural variant calling bioinformatics.ca Learning Objectives of Module To understand what are structural variants (SVs) To appreciate how SVs are discovered from NGS data To appreciate the strengths and weaknesses of each SV discovery strategy To recognize the sequence alignment SV “signals” To be able to visually explore read support for SVs

6. Module 6: Structural variant calling bioinformatics.ca Structural Variants (SVs) Structural Variants (SVs): Genomic rearrangements that affect >50bp (or 100bp, or 1Kb) of sequence, including: deletions novel insertions inversions mobile-element transpositions duplications translocations Adapted from Alkan et al. 2011

7. Module 6: Structural variant calling bioinformatics.ca Detection and confirmation of SVs Feuk et al. 2006

8. Module 6: Structural variant calling bioinformatics.ca Can higher resolution maps help identify recurrent aberrations and driver mutations in cancer? Structural variants in cancer

9. Module 6: Structural variant calling bioinformatics.ca Classes of SVs Copy number variants (CNVs): – Deletions – Duplications Copy neutral rearrangements: – Inversions – Translocations Other structural variants: – Novel insertions – Mobile-element transpositions

10. Module 6: Structural variant calling bioinformatics.ca Classes of SVs Alkan et al. 2011

11. Module 6: Structural variant calling bioinformatics.ca Aaron Quinlan Our understanding is driven by technology

12. Module 6: Structural variant calling bioinformatics.ca Array-based detection of CNVs Alkan et al. 2011

13. Module 6: Structural variant calling bioinformatics.ca Detecting SVs from NGS data Meyerson et al. 2010

14. Module 6: Structural variant calling bioinformatics.ca Strategies for calling SVs from NGS data Baker 2012 1. 2. 3.4.

15. Module 6: Structural variant calling bioinformatics.ca Strategies for calling SVs from NGS data Baker 2012 1.

16. Module 6: Structural variant calling bioinformatics.ca Discordant read pairs Read 1 Read 2 insert size ConcordantDiscordant (distance too long) Discordant (distance too short) Genomic distance between mapped paired tags Reads pairs are also Discordant when order or orientation isn’t as expected.

17. Module 6: Structural variant calling bioinformatics.ca Using discordant reads to detect SVs Adapted from Aaron Quinlan

18. Module 6: Structural variant calling bioinformatics.ca Using discordant reads to detect SVs Adapted from Aaron Quinlan

19. Module 6: Structural variant calling bioinformatics.ca Using discordant reads to detect SVs Adapted from Aaron Quinlan

20. Module 6: Structural variant calling bioinformatics.ca Using discordant reads to detect SVs Adapted from Aaron Quinlan

21. Module 6: Structural variant calling bioinformatics.ca Read-pair tools BreakDancer VariationHunter MoDIL GASV-PRO DELLY LUMPY GenomeSTRiP…

22. Module 6: Structural variant calling bioinformatics.ca Detecting SVs with read-pairs Hillmer et al. 2011

23. Module 6: Structural variant calling bioinformatics.ca Read-pairs in complex regions Hillmer et al. 2011

24. Module 6: Structural variant calling bioinformatics.ca Read-pair summary Weaknesses – Difficult to interpret read-pairs in repetitive regions – Difficult to fully characterize highly rearranged regions – High rate of false positives Strengths: – Most classes of variation can, in principle, be detected

25. Module 6: Structural variant calling bioinformatics.ca Strategies for calling SVs from NGS data Baker 2012 2.

26. Module 6: Structural variant calling bioinformatics.ca Read-depth Aaron Quinlan

27. Module 6: Structural variant calling bioinformatics.ca Read-depth Aaron Quinlan

28. Module 6: Structural variant calling bioinformatics.ca Normalization issues

29. Module 6: Structural variant calling bioinformatics.ca Population based SV detection : PopSV Monlong et al. in preparation

30. Module 6: Structural variant calling bioinformatics.ca Read depth tools ReadDepth RDXplorer cnvSeq CNVer CopySeq GenomeSTRiP CNVnator PopSV …

31. Module 6: Structural variant calling bioinformatics.ca Read-depth summary Weaknesses – Relatively low resolution (normally ~10Kb) – Cannot detect balanced rearrangements (e.g., inversions), or transposon insertions Strengths: – Determines DNA copy number (unlike most other methods) – Provides useful information even with low coverage, albeit at low resolution

32. Module 6: Structural variant calling bioinformatics.ca Strategies for calling SVs from NGS data Baker 2012 3.

33. Module 6: Structural variant calling bioinformatics.ca Split reads Rausch et al. 2012

34. Module 6: Structural variant calling bioinformatics.ca Split read tools Pindel DELLY LUMPY PRISM Mobster …

35. Module 6: Structural variant calling bioinformatics.ca Split reads summary Weaknesses – Requires sufficient coverage – Can have false positives especially in repetitive regions Strengths: – Can be added to read-pairs methods – Base-pair resolution of breakpoints

36. Module 6: Structural variant calling bioinformatics.ca Strategies for calling SVs from NGS data Baker 2012 4.

37. Module 6: Structural variant calling bioinformatics.ca De novo assembly for SVs Adapted from Alkan et al. 2011

38. Module 6: Structural variant calling bioinformatics.ca De novo assembly tools for SVs Cortex SGA DISCOVAR ABySS Ray …

39. Module 6: Structural variant calling bioinformatics.ca De novo assembly for SVs summary Weaknesses – Computationally very intensive – Hard to resolve repetitive and complex regions Strengths: – Base-pair resolution of breakpoints – All classes of variation can, in principle, be detected

40. Module 6: Structural variant calling bioinformatics.ca Summary of strategies for calling SVs Aaron Quinlan

41. Module 6: Structural variant calling bioinformatics.ca Bottom line: try many methods and validate Mills et al. 2011Kloosterman et al. 2015

42. Module 6: Structural variant calling bioinformatics.ca Visual validation: a deletion Aaron Quinlan

43. Module 6: Structural variant calling bioinformatics.ca Visual validation: a duplication Aaron Quinlan

44. Module 6: Structural variant calling bioinformatics.ca Visual validation: an inversion Aaron Quinlan

45. Module 6: Structural variant calling bioinformatics.ca Visual validation: an insertion Aaron Quinlan (in the reference)

46. Module 6: Structural variant calling bioinformatics.ca SVs summary view : Circos plots circos.ca

47. Module 6: Structural variant calling bioinformatics.ca Lab time!

48. Module 6: Structural variant calling bioinformatics.ca We are on a Coffee Break & Networking Session