LUMPY is a probabilistic framework for structural variant discovery that integrates multiple signals, including depth of coverage, paired-end mapping, and split-read mapping. It combines evidence from these different signals to make SV predictions. LUMPY also uses paired-end library statistics to help predict SV breakpoints, leveraging the expected fragment size distribution to identify where breakpoints are likely to occur.

1. A probabilistic framework for SV discovery
Ryan Layer, Ira Hall, Aaron Quinlan
quinlanlab.org

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. “Signals” for SV discovery
Depth of Paired-end
coverage mapping
too big
(deletion)
Split-read Prior
mapping knowledge
Known SV sites
Predictions from
other tools
Most SV software exploit just one signal

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. 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. Layer et al, unpub.
Depth of Paired-end
coverage mapping
too big
(deletion)
Split-read Prior
mapping knowledge
Known SV sites
Predictions from
other tools
LUMPY integrates all (and future) signals

7. Ryan Layer
Graduate Student
Co-mentored with Ira Hall
github.com/ryanlayer

8. LUMPY integrates all SV signals
LUMPY
Sources Sample Prior Result
Pair-End Split-Read
Aligner Aligner
Input
Pair-End Split-Read Generic
Evidence Module Module Module
1 1 1 1 1 1
0.75 0.75 0.75 0.75 0.75 0.75
Breakpoints
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 0.5
SV Prediction
0.25 0.25
0 0

9. Paired-end library statistics inform
SV breakpoint prediction
DNA library
Sample fragment size distribution
genome (~500bp library)
Histogram of foo
Reference
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. Paired-end library statistics inform
SV breakpoint prediction
DNA library
Sample fragment size distribution
genome (~500bp library)
Histogram of foo
Reference
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. Paired-end library statistics inform
SV breakpoint prediction
DNA library
Sample fragment size distribution
genome (~500bp library)
Histogram of foo
Reference
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. Paired-end library statistics inform
SV breakpoint prediction
DNA library
Sample fragment size distribution
genome (~500bp library)
Histogram of foo
Reference
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. Paired-end library statistics inform
SV breakpoint prediction
DNA library
Sample fragment size distribution
genome (~500bp library)
Histogram of foo
Reference
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. Paired-end library statistics inform
SV breakpoint prediction
DNA library
Sample fragment size distribution
genome (~500bp library)
Histogram of foo
Reference
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. Combining SV signals
Sample
Reference

16. Combining SV signals
Sample
Reference

17. Combining SV signals
Sample
Reference
1.0000 1.0000
0.7500 0.7500
0.5000 0.5000
0.2500 0.2500
0 0

18. Combining SV signals
Sample
Reference
1.0000 1.0000
0.7500 0.7500
0.5000 0.5000
0.2500 0.2500
0 0

19. Combining SV signals
Sample
Reference
1.0000 1.0000
0.7500 0.7500
0.5000 0.5000
0.2500 0.2500
0 0

20. Combining SV signals
Sample
Reference
1.0000 1.0000
0.7500 0.7500
0.5000 0.5000
0.2500 0.2500
0 0

21. Combining SV signals
Sample
Reference
1.0000 1.0000
0.7500 0.7500
0.5000 0.5000
0.2500 0.2500
0 0

22. Combining SV signals
Sample
Reference
1.0000 1.0000
0.7500 0.7500
0.5000 0.5000
0.2500 0.2500
0 0

23. Combining SV signals
Sample
Reference
1.0000 1.0000
0.7500 0.7500
0.5000 0.5000
0.2500 0.2500
0 0

24. Combining SV signals
Sample
Reference
1.0000 1.0000
0.7500 0.7500
0.5000 0.5000
0.2500 0.2500
0 0
Predicted breakpoint intervals
Much greater SV breakpoint resolution and sensitivity

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. 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 0.93
lum
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
0.36
all
Legend
0.39
Increased sensitivity for deletions: 20X coverage
< 1kb
Delly: 82%
GASV: 70%
Hydra: 78%
Lumpy: 95%
>= 1kb

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
)
0.39
lum
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
0.04
all
Legend
0.03
sensitive
Increased sensitivity for deletions: 10X coverage
< 1kb
Delly: 29%
GASV: 28%
Hydra: 31%
Lumpy: 79%
>= 1kb
>2 times more

28. Increased sensitivity for deletions: 2X coverage
2x
Lumpy: 24%
Hydra: 3%
1.00
1.00
Fraction 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. 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
(SR
lum )
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)
0.03
all
Legend
sensitive
0.04
< 1kb
Lumpy: 70%
GASV: N/A
Delly: 21%
Hydra: 26%
>= 1kb
~3 times more

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−sr
lum )
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−sr
all
Legend
sensitive
0.26
< 1kb
Lumpy: 95%
Delly: 10%
GASV: 71%
Hydra: 52%
>= 1kb
1.2 - 2X more
lumpy

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. Sensitivity is crucial in the context of
tumor heterogeneity
Russnes et al, 2011

33. Tumor heterogeneity simulation: an in silico “spike in”
140 SVs >= 100bp
chr17 (HuRef) chr17 (build 37)

34. Tumor heterogeneity simulation: an in silico “spike in”
140 SVs >= 100bp
chr17 (HuRef) chr17 (build 37)
50% tumor freq.
FASTA FASTA
wgsim wgsim
(20x) (20X)
What fraction of
40X the 140 SVs
BAM can we detect?

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 FASTA
wgsim 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. 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 FASTA
wgsim 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. Sensitivity for tumor heterogeneity
1.00
1.00
10X DELLY
Fraction 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. Sensitivity for tumor heterogeneity
1.00 1.00
1.00
1.00
10X 20X DELLY
Fraction 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. Sensitivity for tumor heterogeneity
1.00 1.00
1.00
1.00
10X 20X DELLY
Fraction 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. Sensitivity for tumor heterogeneity
1.00 1.00
1.00
1.00
10X 20X DELLY
Fraction 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. 1. Integrates all SV signals
2. High sensitivity
3. Power for low frequency variants:
cancer genomics / heterogeneity
github.com/arq5x/lumpy-sv

42. Acknowledgments
Ryan Layer Ira Hall Raphael Lab
Graduate Student Univ. of Virginia Brown University
Co-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