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Translocation detection in lung cancer using mate-pair sequencing and iVIGS
1. Introduction
Methods
Results
Translocation detection in lung cancer
using mate-pair sequencing and iVIGS
Richard Meier & Stefan Graw
University of Kansas Medical Center
February 3, 2014
Translocation detection in lung cancer using mate-pair sequencing and iVIGS
Richard Meier & Stefan Graw
5. Introduction
Methods
Results
Structural variations in mate pair mapping
Insertion
Deletion
reference genome
reference genome
cancer genome
cancer genome
reads map closer than expected
reads map farther away than expected
Translocation
reference genome
cancer genome
reads map to different chromosomes
Translocation detection in lung cancer using mate-pair sequencing and iVIGS
Richard Meier & Stefan Graw
6. Introduction
Methods
Results
Breakpoint resolution with split reads
Where are the breakpoints ?
known reference
cluster
cluster
Looking at soft clipping reads
known reference
cluster
cluster
reads
unknown sample
Translocation detection in lung cancer using mate-pair sequencing and iVIGS
Richard Meier & Stefan Graw
8. Introduction
Methods
Results
Data
A set of mate-pair sequencing data from lung cancer patients was analysed.
35 samples were processed with the sv tool iVIGS
32 samples were processed with the sv tool delly
Translocation detection in lung cancer using mate-pair sequencing and iVIGS
Richard Meier & Stefan Graw
12. Introduction
Methods
Results
Comparison of the tools
Both tools
• cluster paired reads to find potential translocation regions
• use split reads to find potential breakpoint positions
delly
• re-assembles split reads
• re-maps the assembly to the cluster region
iVIGS (tool for identification of variations in genomic structure)
• is developed in our lab and currently still a work in progress
• performs Kernel Density Estimation on split read mapping positions
• estimates propability distribution of breakpoint positions
Translocation detection in lung cancer using mate-pair sequencing and iVIGS
Richard Meier & Stefan Graw
14. Introduction
Methods
Results
Effect of iVIGS quality control filtering
The separation distance distribution was similar for all samples
4e+06
Molina−Dataset
2e+06
0e+00
1e+06
counts
3e+06
unfiltered
filtered
0
1000
2000
3000
4000
5000
6000
distance between mate−reads
Translocation detection in lung cancer using mate-pair sequencing and iVIGS
Richard Meier & Stefan Graw
15. Introduction
Methods
Results
Problems with delly
• Applying iVIGS filter resulted in delly not reporting any translocations
• Taking all reads and applying delly internal filtering resulted in finding
translocations
• Coverage for reads after strict iVIGS filtering was probably too
inconsistent for assembly.
The type of used assembly is also important. (see next slides)
• Thus the following results for delly are refering to a workflow that uses
the internal filtering method.
Translocation detection in lung cancer using mate-pair sequencing and iVIGS
Richard Meier & Stefan Graw
16. Introduction
Methods
Results
Selection of breakpoint distributions calculated by iVIGS
Kernel Density Estimation
Kernel Density Estimation
27735000
27740000
|| | |
|
135500000
|
0.00010
breakpoint density
|
135496000
0.00000
0.0000
||||||||||||||||||| ||||||||||| |||||||||||||||| ||||| || ||||||||||||||| ||||||||| ||||||||||| ||||||||| || |||| | |
|
|
|
|
27730000
0.00005
0.0010
breakpoint density
0.0005
2e−04
0e+00
1e−04
breakpoint density
3e−04
0.0015
0.00015
4e−04
Kernel Density Estimation
|
135504000
135508000
|
|
33970000
|
||
|| || ||| | || |
|
|| |
33975000
base position
Kernel Density Estimation
Kernel Density Estimation
|
121484000
121486000
base position
121488000
0.015
breakpoint density
0.010
||
190194000
190198000
| || ||
|
||
190202000
base position
Translocation detection in lung cancer using mate-pair sequencing and iVIGS
0.000
0.005
0.0005
0.0000
|||||| | || ||||||||||
| | |
121482000
0.020
0.025
0.0020
0.0015
breakpoint density
0.0010
1e−03
8e−04
6e−04
4e−04
2e−04
breakpoint density
|
33980000
Kernel Density Estimation
0e+00
121480000
|| | | ||
base position
0.030
base position
|
61792000
||||
|
61794000
61796000
61798000
base position
Richard Meier & Stefan Graw
17. Introduction
Methods
Results
Model of translocation divergence due to cancer proliferation
chromosomes with highly
active regions
breaking and translocation
subsequent variations in
daughter cells
breakpoint and cluster
distribution
density
position
Translocation detection in lung cancer using mate-pair sequencing and iVIGS
Richard Meier & Stefan Graw
18. Introduction
Methods
Results
Error sources
• Adapter contamination
(before filtering approximately 15% of all reads are contaminated.)
• Ligation errors
• PCR bias
• Sequencing errors
Translocation detection in lung cancer using mate-pair sequencing and iVIGS
Richard Meier & Stefan Graw
19. Introduction
Methods
Results
General information
• Estimated breakpoint positions were highly variable (spanning up to
several thousand base positions in difference)
• Translocations were found to almost always overlap with potential
deletion or insertion cluster regions (estimated by iVIGS).
• In most cases around 35 translocations per sample were estimated
0.02
0.00
0.01
Density
0.03
0.04
translocation discovery of iVIGS
20
30
40
50
60
70
80
number of estimated translocations per sample
Translocation detection in lung cancer using mate-pair sequencing and iVIGS
Richard Meier & Stefan Graw
20. Introduction
Methods
Results
Typical translocation distribution observed in samples
CHRY
CHR1
CHRX
CH
R2
R2
2
1
CH
CH
R2
0
R2
CH
R1
8
CH
R1
9
CH
CH
CHR16
CHR1
7
R3
CHR4
CHR15
4
CHR5
CHR1
R1
CH
CH
R6
3
CH
R1
2
CH
R1
R7
CH
1
CHR1
CHR8
0
CHR9
Translocation detection in lung cancer using mate-pair sequencing and iVIGS
Richard Meier & Stefan Graw
21. Introduction
Methods
Results
Occuring genes altered by a translocation
Genes used for the diagram were altered in one or more samples
delly
108
41
158
iVIGS
Translocation detection in lung cancer using mate-pair sequencing and iVIGS
Richard Meier & Stefan Graw
22. Introduction
Methods
Results
Potential gene fusions
Genes used for the diagram were altered in one or more samples
delly
32
11
73
iVIGS
Translocation detection in lung cancer using mate-pair sequencing and iVIGS
Richard Meier & Stefan Graw
23. Introduction
Methods
Results
Potential gene to intergenic fusions
Genes used for the diagram were altered in one or more samples
delly
106
27
109
iVIGS
Translocation detection in lung cancer using mate-pair sequencing and iVIGS
Richard Meier & Stefan Graw
26. Introduction
Methods
Results
Conclusion
• Results varied significantly between delly and iVIGS
• Reproducibility of iVIGS seems promising
• It is still unclear how strong the influences of diversity in close related
cancer cells and library preparation errors are in respect to the results.
• It is thus still difficult to determine whether predictions are FP or TP.
Translocation detection in lung cancer using mate-pair sequencing and iVIGS
Richard Meier & Stefan Graw
27. Introduction
Methods
Results
Plans for the future
• Apply adapter removal in preprocessing to improve mapping yield
• Pick a subset of potential gene fusions and validate them
• Examine other structural variation types (insertions, deletions,
inversions)
• Find and use additional sv tools
Translocation detection in lung cancer using mate-pair sequencing and iVIGS
Richard Meier & Stefan Graw