Jan2016 fritz sedlazeck mapping and sv calling from pac bio

1. Giab workshop
Fritz Sedlazeck
CSHL, JHU

2. Previous meetings: Utilizing long reads
1. How to predict the breakpoints?
2. How to assess genotype ?
3. Complex SVs?

3. 1. Breakpoint prediction
• Over BWA-MEM alignments
– First version had a bug…
• Redesigning Sniffles
– Improved speed
– Improved accuracy on noisy alignment
– Improved read filtering -> reducing FDR
– Optional realignment step
• Improved breakpoint accuracy
• Improving Genotyping

4. Sniffles v01 error

5. Sniffles v02

6. Current limitations
• Linear: gap cost always the same
• Affine: separate penalties for opening and extending a gap
• Using one gap cost is considered state of the art
• Problem with PacBio/ONT: two different gap models required
– Sequencing error: large high number of 1 bp indels
– Real indels: extending a gap more likely than opening a new one
– Sequencing error + repeats cause one gap cost to fail even for real
indels
AAAGAATTCA
A-A-A-T-CA
AAAGAATTCA
AAA----TCA
vs.

7. Convex gap costs
• Costs for a gap follow a convex function of gap length
• Close to linear gap costs for 1 - 2 bp gaps
• As gap gets longer penalty for "splitting" gaps increases
• Problem optimal approach: O(nm2 + n2m)
• Heuristic implementation O(nm)

8. NGM-LR workflow

9. NGM-LR reconcile
Read within inversion Read within duplication

10. Deletion

11. Deletion

12. Insertions

13. Inversions

14. Translocations

15. Nested SV (SKBR3)

16. Outlook
• Finish new version of Sniffles
– Assessment of noisy alignments
• NGM-LR:
– MQ calculation
– Runtime
• Visual inspection and comparison of SV calls