1. Canadian Bioinformatics Workshops

2. Module #: Title of Module2

3. Module 6 Structural variant calling
Guillaume Bourque
Informatics on High-throughput Sequencing Data
June 9-10, 2016

4. 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

5. 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. Nat Rev Genet 2011

6. Detection and confirmation of SVs
Feuk et al. Nat Rev Genet 2006

7. Structural variants in cancer
Can higher resolution maps help identify recurrent aberrations and driver mutations in cancer?

8. Classes of SVs Copy number variants (CNVs):
Deletions
Duplications
Copy neutral rearrangements:
Inversions
Translocations
Other structural variants:
Novel insertions
Mobile-element transpositions

9. Classes of SVs
Alkan et al. Nat Rev Genet 2011

10. Our understanding is driven by technology
Aaron Quinlan

11. Array-based detection of CNVs
Alkan et al. Nat Rev Genet 2011

12. Detecting SVs from NGS data
Meyerson et al. Nat Rev Genet 2010

13. Strategies for calling SVs from NGS data
Baker Nat Methods 2012

14. Strategies for calling SVs from NGS data1.
Baker Nat Methods 2012

15. Discordant read pairs
Concordant
Discordant
(distance too long)
(distance too short)
Genomic distance between mapped paired tags
Read 1
Read 2
insert size
Reads pairs are also Discordant when order or orientation isn’t as expected.

16. Using discordant reads to detect SVs
Adapted from Aaron Quinlan

17. Using discordant reads to detect SVs
Adapted from Aaron Quinlan

18. Using discordant reads to detect SVs
Adapted from Aaron Quinlan

19. Using discordant reads to detect SVs
Adapted from Aaron Quinlan

20. Read-pair tools BreakDancer VariationHunter MoDIL GASV-PRO DELLY LUMPY
GenomeSTRiP
Etc.

21. Detecting SVs with read-pairs
Hillmer et al. Genome Res 2011

22. Read-pairs in complex regions
Hillmer et al. Genome Res 2011

23. Read-pair summary Weaknesses Strengths:
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

24. Strategies for calling SVs from NGS data2.
Baker Nat Methods 2012

25. Read-depth
Aaron Quinlan

26. Read-depth
Aaron Quinlan

27. Normalization issues

28. Population based SV detection : PopSV
Monlong et al. BioRxiv

29. Read depth tools ReadDepth RDXplorer cnvSeq CNVer CopySeq GenomeSTRiP
CNVnator
PopSV
Etc.

30. Read-depth summary Weaknesses Strengths:
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

31. Strategies for calling SVs from NGS data3.
Baker Nat Methods 2012

32. Split reads
Rausch et al. Bioinformatics 2012

33. Split read tools
Pindel
DELLY
LUMPY
PRISM
Mobster
Etc.

34. Split reads summary Weaknesses Strengths: Requires sufficient coverage
Can have false positives especially in repetitive regions
Strengths:
Can be added to read-pairs methods
Base-pair resolution of breakpoints

35. Strategies for calling SVs from NGS data4.
Baker Nat Methods 2012

36. De novo assembly for SVs
Adapted from Alkan et al. Nat Rev Genet 2011

37. De novo assembly tools for SVs
Cortex
SGA
DISCOVAR
ABySS
Ray
Etc.

38. 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

39. Summary of strategies for calling SVs
Aaron Quinlan

40. Bottom line: try many methods and validate
Mills et al. Nature 2011
Kloosterman et al. Genome Res 2015

41. Visual validation: a deletion
Aaron Quinlan

42. Visual validation: a duplication
Aaron Quinlan

43. Visual validation: an inversion
Aaron Quinlan

44. Visual validation: an insertion
(in the reference)
Aaron Quinlan

45. SVs summary view : Circos plots
circos.ca

46. Lab time!

47. We are on a Coffee Break & Networking Session