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A probabilistic framework for SV discovery          Ryan Layer, Ira Hall, Aaron Quinlan                    quinlanlab.org
Structural variants. Easy to grok. Hard to find (well).                                                             Refere...
“Signals” for SV discovery Depth of                  Paired-end coverage                   mapping                        ...
DELLY: Rausch et al, 2012 Depth of            Paired-end coverage             mapping                       too big       ...
GASVPro: Sindhi et al, 2012           Depth of               Paired-end           coverage                mapping         ...
Layer et al, unpub.  Depth of                 Paired-end  coverage                  mapping                             to...
Ryan Layer   Graduate StudentCo-mentored with Ira Hall  github.com/ryanlayer
LUMPY integrates all SV signals LUMPYSources                                 Sample                          Prior Result ...
Paired-end library statistics inform                 SV breakpoint prediction                                             ...
Paired-end library statistics inform                 SV breakpoint prediction                                             ...
Paired-end library statistics inform                 SV breakpoint prediction                                             ...
Paired-end library statistics inform                 SV breakpoint prediction                                             ...
Paired-end library statistics inform                 SV breakpoint prediction                                             ...
Paired-end library statistics inform                 SV breakpoint prediction                                             ...
Combining SV signals                       Sample                       Reference
Combining SV signals                       Sample                       Reference
Combining SV signals                                Sample                                Reference1.0000             1.00...
Combining SV signals                                Sample                                Reference1.0000             1.00...
Combining SV signals                                Sample                                Reference1.0000             1.00...
Combining SV signals                                Sample                                Reference1.0000             1.00...
Combining SV signals                                Sample                                Reference1.0000             1.00...
Combining SV signals                                Sample                                Reference1.0000             1.00...
Combining SV signals                                Sample                                Reference1.0000             1.00...
Combining SV signals                                                           Sample                                     ...
Bakeoff #1: detection of 4000 simulated SVs                                               chr10- Simulate 4000 SVs on chr1...
Fraction of deletions found                                           0.50                                                ...
Fraction of deletions found                                                                        0.82                   ...
Increased sensitivity for deletions: 2X coverage                                                                          ...
Fraction of deletions found                                                                             0.79              ...
delly−sr Fraction of deletions found                                                    0.5                               ...
Best sensitivity across the board- Profound for improvement for smaller (<1kb) variants- And, importantly, at low coverage...
Sensitivity is crucial in the context of        tumor heterogeneity                                    Russnes et al, 2011
Tumor heterogeneity simulation: an in silico “spike in”                        140 SVs >= 100bp            chr17 (HuRef)  ...
Tumor heterogeneity simulation: an in silico “spike in”                                 140 SVs >= 100bp                  ...
Tumor heterogeneity simulation: an in silico “spike in”                                 140 SVs >= 100bp                  ...
Tumor heterogeneity simulation: an in silico “spike in”                                 140 SVs >= 100bp                  ...
Sensitivity for tumor heterogeneity                        1.00                               1.00                        ...
Sensitivity for tumor heterogeneity                        1.00                                      1.00                 ...
Sensitivity for tumor heterogeneity                        1.00                                      1.00                 ...
Sensitivity for tumor heterogeneity                        1.00                                      1.00                 ...
1. Integrates all SV signals          2. High sensitivity3. Power for low frequency variants: cancer genomics / heterogene...
Acknowledgments  Ryan Layer                           Ira Hall                      Raphael Lab   Graduate Student        ...
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Lumpy agbt-pres

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Lumpy agbt-pres

  1. 1. A probabilistic framework for SV discovery Ryan Layer, Ira Hall, Aaron Quinlan quinlanlab.org
  2. 2. Structural variants. Easy to grok. Hard to find (well). Reference Deletion Duplication Inversion Insertion Complex Ira Hall. Saturday @ 3PM: Complex SV in 64 tumor genomes
  3. 3. “Signals” for SV discovery Depth of Paired-end coverage mapping too big (deletion) Split-read Prior mapping knowledge Known SV sites Predictions from other toolsMost SV software exploit just one signal
  4. 4. DELLY: Rausch et al, 2012 Depth of Paired-end coverage mapping too big (deletion) 1. Predict Split-read Prior mapping knowledge “Stepwise” 2. Refine SV sites Known Predictions from other tools
  5. 5. GASVPro: Sindhi et al, 2012 Depth of Paired-end coverage mapping too big (deletion)Combines DoC and PEM signals for greater specificity, especially for deletions (using DoC) Split-read Prior mapping knowledge “Integrative” Known SV sites Predictions from other tools
  6. 6. Layer et al, unpub. Depth of Paired-end coverage mapping too big (deletion) Split-read Prior mapping knowledge Known SV sites Predictions from other toolsLUMPY integrates all (and future) signals
  7. 7. Ryan Layer Graduate StudentCo-mentored with Ira Hall github.com/ryanlayer
  8. 8. LUMPY integrates all SV signals LUMPYSources Sample Prior Result Pair-End Split-Read Aligner AlignerInput Pair-End Split-Read GenericEvidence Module Module Module 1 1 1 1 1 1 0.75 0.75 0.75 0.75 0.75 0.75Breakpoints 0.5 0.5 0.5 0.5 0.5 0.5 0.25 0.25 0.25 0.25 0.25 0.25 0 0 0 0 0 0 Pool 1 1 0.75 0.75 0.5 0.5SV Prediction 0.25 0.25 0 0
  9. 9. Paired-end library statistics inform SV breakpoint prediction DNA librarySample fragment size distributiongenome (~500bp library) Histogram of fooReference 0.025 1kb genome 0.020 0.015 Density 0.010 0.005 0.000 450 460 475 480 500 500 525 520 550 540 575 560 foo
  10. 10. Paired-end library statistics inform SV breakpoint prediction DNA librarySample fragment size distributiongenome (~500bp library) Histogram of fooReference 0.025 1kb genome 0.020 0.015 Density 0.010 0.005 When aligned to reference, ends 0.000 map ~1500bp apart. 450 460 475 480 500 500 525 520 550 540 575 560 foo Where are the breakpoints?
  11. 11. Paired-end library statistics inform SV breakpoint prediction DNA librarySample fragment size distributiongenome (~500bp library) Histogram of fooReference 0.025 1kb genome 0.020 1.0000 0.7500 0.015 0.5000 Density 0.2500 0.010 0 0.005 When aligned to reference, ends 0.000 map ~1500bp apart. 450 460 475 480 500 500 525 520 550 540 575 560 foo Where are the breakpoints?
  12. 12. Paired-end library statistics inform SV breakpoint prediction DNA librarySample fragment size distributiongenome (~500bp library) Histogram of fooReference 0.025 1kb genome 0.020 1.0000 0.7500 500bp 0.015 0.5000 Density 0.2500 0.010 0 0.005 When aligned to reference, ends 0.000 map ~1500bp apart. 450 460 475 480 500 500 525 520 550 540 575 560 foo Where are the breakpoints?
  13. 13. Paired-end library statistics inform SV breakpoint prediction DNA librarySample fragment size distributiongenome (~500bp library) Histogram of fooReference 0.025 1kb genome 0.020 1.0000 0.7500 500bp 0.015 0.5000 Density 0.2500 550bp 0.010 0 0.005 When aligned to reference, ends 0.000 map ~1500bp apart. 450 460 475 480 500 500 525 520 550 540 575 560 foo Where are the breakpoints?
  14. 14. Paired-end library statistics inform SV breakpoint prediction DNA librarySample fragment size distributiongenome (~500bp library) Histogram of fooReference 0.025 1kb genome 0.020 1.0000 0.7500 500bp 0.015 0.5000 Density 0.2500 550bp 0.010 0 575bp 0.005 When aligned to reference, ends 0.000 map ~1500bp apart. 450 460 475 480 500 500 525 520 550 540 575 560 foo Where are the breakpoints?
  15. 15. Combining SV signals Sample Reference
  16. 16. Combining SV signals Sample Reference
  17. 17. Combining SV signals Sample Reference1.0000 1.00000.7500 0.75000.5000 0.50000.2500 0.2500 0 0
  18. 18. Combining SV signals Sample Reference1.0000 1.00000.7500 0.75000.5000 0.50000.2500 0.2500 0 0
  19. 19. Combining SV signals Sample Reference1.0000 1.00000.7500 0.75000.5000 0.50000.2500 0.2500 0 0
  20. 20. Combining SV signals Sample Reference1.0000 1.00000.7500 0.75000.5000 0.50000.2500 0.2500 0 0
  21. 21. Combining SV signals Sample Reference1.0000 1.00000.7500 0.75000.5000 0.50000.2500 0.2500 0 0
  22. 22. Combining SV signals Sample Reference1.0000 1.00000.7500 0.75000.5000 0.50000.2500 0.2500 0 0
  23. 23. Combining SV signals Sample Reference1.0000 1.00000.7500 0.75000.5000 0.50000.2500 0.2500 0 0
  24. 24. Combining SV signals Sample Reference1.0000 1.00000.7500 0.75000.5000 0.50000.2500 0.2500 0 0 Predicted breakpoint intervals Much greater SV breakpoint resolution and sensitivity
  25. 25. Bakeoff #1: detection of 4000 simulated SVs chr10- Simulate 4000 SVs on chr10 (build 37) - 1000 deletions - 1000 duplications - 1000 insertions - 1000 inversions- For each SV type, 500 < 1kb and 500 >= 1kb- “Sequence” mutant chr10 to 2X, 5X, 20X w/ wgsim- Compare LUMPY, HYDRA, GASVPro, DELLY
  26. 26. Fraction of deletions found 0.50 0.75 1.00 0.00 0.25 0.00 0.25 0.50 0.75 1.00 lum py (PE ) lum 0.86 py (SR )0.95 0.93lum p y( bo th)0.96 0.95 hy dra 20x ga 0.78 svp ro de 0.7 lly (pe )de lly 0.93 (pe +s r) 0.82 0.00 0.25 0.50 0.75 1.00 0.36all Legend 0.39 Increased sensitivity for deletions: 20X coverage< 1kb Delly: 82% GASV: 70% Hydra: 78% Lumpy: 95%>= 1kb
  27. 27. Fraction of deletions found 0.82 0.50 0.75 1.00 0.00 0.25 0.00 0.25 0.50 0.75 1.00 lum py (PE ) lum 0.36 py (SR ) 0.39lum py (bo th) 0.79 hy dra 5x ga 0.31 svp ro de 0.28 lly (pe )de lly 0.4 (pe +s r) 0.29 0.00 0.25 0.50 0.75 1.00 0.04all Legend 0.03 sensitive Increased sensitivity for deletions: 10X coverage< 1kb Delly: 29% GASV: 28% Hydra: 31% Lumpy: 79%>= 1kb >2 times more
  28. 28. Increased sensitivity for deletions: 2X coverage 2x Lumpy: 24% Hydra: 3% 1.00 1.00Fraction of deletions found GASV: 3% Delly: 4% 0.75 0.75 6 times more 0.50 0.50 0.29 0.24 sensitive 0.25 0.25 0.04 0.04 0.03 0.03 0.03 0.02 Legend 0.00 0.00 ) ) r) ) th) dra ro (PE (SR (pe +s svp < 1kb (bo hy (pe py py lly ga >= 1kb de lum lum py lly all de lum
  29. 29. Fraction of deletions found 0.79 0.50 0.75 1.00 0.00 0.25 0.00 0.25 0.50 0.75 1.00 0.00 lum py (PE ) lum 0.27 py (SRlum ) py 0.36 (bo th) 0.7 hy dra ga 0.26 svp ro de lly 0 (pe )de lly (pe 0.3 +s r) 0.21 0.00 0.25 0.50 0.75 1.00 0.00 Same goes for duplications (5X) 0.03all Legend sensitive 0.04< 1kb Lumpy: 70% GASV: N/A Delly: 21% Hydra: 26%>= 1kb ~3 times more
  30. 30. delly−sr Fraction of deletions found 0.5 0.50 0.75 1.00 0.00 0.25 0.00 0.25 0.50 0.75 1.00 0.00 lum py (PE lumpy−pe ) lum 0.74 py (SR lumpy−srlum ) py 0.83 (bo th) lumpy 0.95 hy dra hydra ga 0.52 svp ro gasvpro de lly 0.71 (pe ) dedelly−pe lly (pe 0.55 +s r) delly−sr ...and inversions (5X) 0.1 0.00 0.25 0.50 0.75 1.00 0.00 lumpy−pe 0.22 lumpy−srall Legend sensitive 0.26< 1kb Lumpy: 95% Delly: 10% GASV: 71% Hydra: 52%>= 1kb 1.2 - 2X more lumpy
  31. 31. Best sensitivity across the board- Profound for improvement for smaller (<1kb) variants- And, importantly, at low coverage. - up to 6X more sensitive.- No significant increase in false positives.
  32. 32. Sensitivity is crucial in the context of tumor heterogeneity Russnes et al, 2011
  33. 33. Tumor heterogeneity simulation: an in silico “spike in” 140 SVs >= 100bp chr17 (HuRef) chr17 (build 37)
  34. 34. Tumor heterogeneity simulation: an in silico “spike in” 140 SVs >= 100bp chr17 (HuRef) chr17 (build 37) 50% tumor freq.FASTA FASTAwgsim wgsim(20x) (20X) What fraction of 40X the 140 SVs BAM can we detect?
  35. 35. Tumor heterogeneity simulation: an in silico “spike in” 140 SVs >= 100bp chr17 (HuRef) chr17 (build 37) 50% tumor freq. 20% tumor freq. *FASTA FASTA FASTA FASTAwgsim wgsim wgsim wgsim(20x) (20X) (4x) (36X) What fraction of 40X the 140 SVs 40X BAM can we detect? BAM * Not even close to scale.
  36. 36. Tumor heterogeneity simulation: an in silico “spike in” 140 SVs >= 100bp chr17 (HuRef) chr17 (build 37) 50% tumor freq. 20% tumor freq. . . . 1% tumor freq. * *FASTA FASTA FASTA FASTA FASTA FASTAwgsim wgsim wgsim wgsim wgsim wgsim(20x) (20X) (4x) (36X) (0.4x) (39.6X) What fraction of 40X the 140 SVs 40X 40X BAM can we detect? BAM BAM * Not even close to scale.
  37. 37. Sensitivity for tumor heterogeneity 1.00 1.00 10X DELLYFraction of SVs found 0.75 0.75 GASVpro 0.50 -c 2 0.50 LUMPY 0.25 -w 2 0.25 0.0 0.00 1% 5% 10% 20% 50%
  38. 38. Sensitivity for tumor heterogeneity 1.00 1.00 1.00 1.00 10X 20X DELLYFraction of SVs found 0.75 0.75 0.75 0.75 GASVpro 0.50 0.50 -c 2 0.50 0.50 LUMPY 0.25 0.25 -w 2 0.25 0.25 0.0 0.0 0.00 0.00 1% 5% 10% 20% 50% 1% 5% 10% 20% 50%
  39. 39. Sensitivity for tumor heterogeneity 1.00 1.00 1.00 1.00 10X 20X DELLYFraction of SVs found 0.75 0.75 0.75 0.75 GASVpro 0.50 0.50 -c 2 0.50 0.50 LUMPY 0.25 0.25 -w 2 0.25 0.25 0.0 0.0 0.00 0.00 1% 5% 10% 20% 50% 1% 5% 10% 20% 50% 1.00 1.00 40X 0.75 0.75 0.50 0.50 0.25 0.25 0.0 0.00 1% 5% 10% 20% 50%
  40. 40. Sensitivity for tumor heterogeneity 1.00 1.00 1.00 1.00 10X 20X DELLYFraction of SVs found 0.75 0.75 0.75 0.75 GASVpro 0.50 0.50 -c 2 0.50 0.50 LUMPY 0.25 0.25 -w 2 0.25 0.25 0.0 0.0 0.00 0.00 1% 5% 10% 20% 50% 1% 5% 10% 20% 50% 1.00 1.00 1.00 1.00 40X 80X 0.75 0.75 0.75 0.75 0.50 0.50 0.50 0.50 0.25 0.25 0.25 0.25 0.0 0.0 0.00 0.00 1% 5% 10% 20% 50% 1% 5% 10% 20% 50%
  41. 41. 1. Integrates all SV signals 2. High sensitivity3. Power for low frequency variants: cancer genomics / heterogeneity github.com/arq5x/lumpy-sv
  42. 42. Acknowledgments Ryan Layer Ira Hall Raphael Lab Graduate Student Univ. of Virginia Brown UniversityCo-mentored with Ira Hall Former mentor & key collaborator Help w/ GASV & Venter simulation github.com/ryanlayer faculty.virginia.edu/irahall/ compbio.cs.brown.edu/Funding R01 HG006693-01 Fund for Excellence in Science & Technology

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