Abstract
BACKGROUND: The phenotypes of cancer cells are driven in part by somatic structural variants. Structural variants can initiate tumors, enhance their aggressiveness, and provide unique therapeutic opportunities. Whole-genome sequencing of tumors can allow exhaustive identification of the specific structural variants present in an individual cancer, facilitating both clinical diagnostics and the discovery of novel mutagenic mechanisms. A plethora of somatic structural variant detection algorithms have been created to enable these discoveries; however, there are no systematic benchmarks of them. Rigorous performance evaluation of somatic structural variant detection methods has been challenged by the lack of gold standards, extensive resource requirements, and difficulties arising from the need to share personal genomic information. RESULTS: To facilitate structural variant detection algorithm evaluations, we create a robust simulation framework for somatic structural variants by extending the BAMSurgeon algorithm. We then organize and enable a crowdsourced benchmarking within the ICGC-TCGA DREAM Somatic Mutation Calling Challenge (SMC-DNA). We report here the results of structural variant benchmarking on three different tumors, comprising 204 submissions from 15 teams. In addition to ranking methods, we identify characteristic error profiles of individual algorithms and general trends across them. Surprisingly, we find that ensembles of analysis pipelines do not always outperform the best individual method, indicating a need for new ways to aggregate somatic structural variant detection approaches. CONCLUSIONS: The synthetic tumors and somatic structural variant detection leaderboards remain available as a community benchmarking resource, and BAMSurgeon is available at https://github.com/adamewing/bamsurgeon .
| Original language | English (US) |
|---|---|
| Number of pages | 1 |
| Journal | Genome Biology |
| Volume | 19 |
| Issue number | 1 |
| DOIs | |
| State | Published - Nov 6 2018 |
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Keywords
- Benchmarking
- Cancer genomics
- Crowdsourcing
- Simulation
- Somatic mutations
- Structural variants
- Whole-genome sequencing
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics
- Genetics
- Cell Biology
Cite this
Combining accurate tumor genome simulation with crowdsourcing to benchmark somatic structural variant detection. / ICGC-TCGA DREAM Somatic Mutation Calling Challenge Participants.
In: Genome Biology, Vol. 19, No. 1, 06.11.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Combining accurate tumor genome simulation with crowdsourcing to benchmark somatic structural variant detection
AU - ICGC-TCGA DREAM Somatic Mutation Calling Challenge Participants
AU - Lee, Anna Y.
AU - Ewing, Adam D.
AU - Ellrott, Kyle
AU - Hu, Yin
AU - Houlahan, Kathleen E.
AU - Bare, J. Christopher
AU - Espiritu, Shadrielle Melijah G.
AU - Huang, Vincent
AU - Dang, Kristen
AU - Chong, Zechen
AU - Caloian, Cristian
AU - Yamaguchi, Takafumi N.
AU - Kellen, Michael R.
AU - Chen, Ken
AU - Norman, Thea C.
AU - Friend, Stephen H.
AU - Guinney, Justin
AU - Stolovitzky, Gustavo
AU - Haussler, David
AU - Margolin, Adam A.
AU - Margolin, Adam
AU - Boutros, Paul C.
PY - 2018/11/6
Y1 - 2018/11/6
N2 - BACKGROUND: The phenotypes of cancer cells are driven in part by somatic structural variants. Structural variants can initiate tumors, enhance their aggressiveness, and provide unique therapeutic opportunities. Whole-genome sequencing of tumors can allow exhaustive identification of the specific structural variants present in an individual cancer, facilitating both clinical diagnostics and the discovery of novel mutagenic mechanisms. A plethora of somatic structural variant detection algorithms have been created to enable these discoveries; however, there are no systematic benchmarks of them. Rigorous performance evaluation of somatic structural variant detection methods has been challenged by the lack of gold standards, extensive resource requirements, and difficulties arising from the need to share personal genomic information. RESULTS: To facilitate structural variant detection algorithm evaluations, we create a robust simulation framework for somatic structural variants by extending the BAMSurgeon algorithm. We then organize and enable a crowdsourced benchmarking within the ICGC-TCGA DREAM Somatic Mutation Calling Challenge (SMC-DNA). We report here the results of structural variant benchmarking on three different tumors, comprising 204 submissions from 15 teams. In addition to ranking methods, we identify characteristic error profiles of individual algorithms and general trends across them. Surprisingly, we find that ensembles of analysis pipelines do not always outperform the best individual method, indicating a need for new ways to aggregate somatic structural variant detection approaches. CONCLUSIONS: The synthetic tumors and somatic structural variant detection leaderboards remain available as a community benchmarking resource, and BAMSurgeon is available at https://github.com/adamewing/bamsurgeon .
AB - BACKGROUND: The phenotypes of cancer cells are driven in part by somatic structural variants. Structural variants can initiate tumors, enhance their aggressiveness, and provide unique therapeutic opportunities. Whole-genome sequencing of tumors can allow exhaustive identification of the specific structural variants present in an individual cancer, facilitating both clinical diagnostics and the discovery of novel mutagenic mechanisms. A plethora of somatic structural variant detection algorithms have been created to enable these discoveries; however, there are no systematic benchmarks of them. Rigorous performance evaluation of somatic structural variant detection methods has been challenged by the lack of gold standards, extensive resource requirements, and difficulties arising from the need to share personal genomic information. RESULTS: To facilitate structural variant detection algorithm evaluations, we create a robust simulation framework for somatic structural variants by extending the BAMSurgeon algorithm. We then organize and enable a crowdsourced benchmarking within the ICGC-TCGA DREAM Somatic Mutation Calling Challenge (SMC-DNA). We report here the results of structural variant benchmarking on three different tumors, comprising 204 submissions from 15 teams. In addition to ranking methods, we identify characteristic error profiles of individual algorithms and general trends across them. Surprisingly, we find that ensembles of analysis pipelines do not always outperform the best individual method, indicating a need for new ways to aggregate somatic structural variant detection approaches. CONCLUSIONS: The synthetic tumors and somatic structural variant detection leaderboards remain available as a community benchmarking resource, and BAMSurgeon is available at https://github.com/adamewing/bamsurgeon .
KW - Benchmarking
KW - Cancer genomics
KW - Crowdsourcing
KW - Simulation
KW - Somatic mutations
KW - Structural variants
KW - Whole-genome sequencing
UR - http://www.scopus.com/inward/record.url?scp=85056286059&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056286059&partnerID=8YFLogxK
U2 - 10.1186/s13059-018-1539-5
DO - 10.1186/s13059-018-1539-5
M3 - Article
C2 - 30400818
AN - SCOPUS:85056286059
VL - 19
JO - Genome Biology
JF - Genome Biology
SN - 1474-7596
IS - 1
ER -