Combining accurate tumour genome simulation with crowd-sourcing to benchmark somatic structural variant detection Article Swipe
Anna Y. Lee
,
Adam D. Ewing
,
Kyle Ellrott
,
Yin Hu
,
Kathleen E. Houlahan
,
J Christopher Bare
,
Shadrielle M. G. Espiritu
,
Vincent Huang
,
Kristen K. Dang
,
Zechen Chong
,
Cristian Caloian
,
Takafumi N. Yamaguchi
,
Michael Kellen
,
Ken Chen
,
Thea Norman
,
Stephen Friend
,
Justin Guinney
,
Gustavo Stolovitzky
,
David Haussler
,
Jake Lin
,
Joshua M. Stuart
,
Paul C. Boutros
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1101/224733
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1101/224733
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 crowd-sourced 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 .
Related Topics
Concepts
Benchmarking
Benchmark (surveying)
Somatic cell
Computer science
Computational biology
Ranking (information retrieval)
Identification (biology)
Genome
Structural variation
Resource (disambiguation)
Data mining
Machine learning
Biology
Gene
Genetics
Business
Geodesy
Marketing
Botany
Computer network
Geography
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/224733
- https://www.biorxiv.org/content/biorxiv/early/2018/08/28/224733.full.pdf
- OA Status
- green
- Cited By
- 2
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2468074436
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2468074436Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/224733Digital Object Identifier
- Title
-
Combining accurate tumour genome simulation with crowd-sourcing to benchmark somatic structural variant detectionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-11-25Full publication date if available
- Authors
-
Anna Y. Lee, Adam D. Ewing, Kyle Ellrott, Yin Hu, Kathleen E. Houlahan, J Christopher Bare, Shadrielle M. G. Espiritu, Vincent Huang, Kristen K. Dang, Zechen Chong, Cristian Caloian, Takafumi N. Yamaguchi, Michael Kellen, Ken Chen, Thea Norman, Stephen Friend, Justin Guinney, Gustavo Stolovitzky, David Haussler, Jake Lin, Joshua M. Stuart, Paul C. BoutrosList of authors in order
- Landing page
-
https://doi.org/10.1101/224733Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2018/08/28/224733.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2018/08/28/224733.full.pdfDirect OA link when available
- Concepts
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Benchmarking, Benchmark (surveying), Somatic cell, Computer science, Computational biology, Ranking (information retrieval), Identification (biology), Genome, Structural variation, Resource (disambiguation), Data mining, Machine learning, Biology, Gene, Genetics, Business, Geodesy, Marketing, Botany, Computer network, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2020: 1, 2018: 1Per-year citation counts (last 5 years)
- References (count)
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30Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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