Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection

Ewing, Adam D., Houlahan, Kathleen E., Hu, Yin, Ellrott, Kyle, Caloian, Cristian, Yamaguchi, Takafumi N., Bare, J. Christopher, P'Ng, Christine, Waggott, Daryl, Sabelnykova, Veronica Y., ICGC-TCGA DREAM Somatic Mutation Calling Challenge participants, Kellen, Michael R., Norman, Thea C., Haussler, David, Friend, Stephen H., Stolovitzky, Gustavo, Margolin, Adam A., Stuart, Joshua M. and Boutros, Paul C. (2015) Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection. Nature Methods, 12 7: 623-630. doi:10.1038/nmeth.3407


Author Ewing, Adam D.
Houlahan, Kathleen E.
Hu, Yin
Ellrott, Kyle
Caloian, Cristian
Yamaguchi, Takafumi N.
Bare, J. Christopher
P'Ng, Christine
Waggott, Daryl
Sabelnykova, Veronica Y.
ICGC-TCGA DREAM Somatic Mutation Calling Challenge participants
Kellen, Michael R.
Norman, Thea C.
Haussler, David
Friend, Stephen H.
Stolovitzky, Gustavo
Margolin, Adam A.
Stuart, Joshua M.
Boutros, Paul C.
Title Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection
Journal name Nature Methods   Check publisher's open access policy
ISSN 1548-7105
1548-7091
Publication date 2015-06-01
Year available 2015
Sub-type Article (original research)
DOI 10.1038/nmeth.3407
Open Access Status DOI
Volume 12
Issue 7
Start page 623
End page 630
Total pages 8
Place of publication London, United Kingdom
Publisher Nature Publishing Group
Language eng
Subject 1305 Biotechnology
1303 Biochemistry
1312 Molecular Biology
1307 Cell Biology
Abstract The detection of somatic mutations from cancer genome sequences is key to understanding the genetic basis of disease progression, patient survival and response to therapy. Benchmarking is needed for tool assessment and improvement but is complicated by a lack of gold standards, by extensive resource requirements and by difficulties in sharing personal genomic information. To resolve these issues, we launched the ICGC-TCGA DREAM Somatic Mutation Calling Challenge, a crowdsourced benchmark of somatic mutation detection algorithms. Here we report the BAMSurgeon tool for simulating cancer genomes and the results of 248 analyses of three in silico tumors created with it. Different algorithms exhibit characteristic error profiles, and, intriguingly, false positives show a trinucleotide profile very similar to one found in human tumors. Although the three simulated tumors differ in sequence contamination (deviation from normal cell sequence) and in subclonality, an ensemble of pipelines outperforms the best individual pipeline in all cases. BAMSurgeon is available at https://github.com/adamewing/bamsurgeon/.
Formatted abstract
The detection of somatic mutations from cancer genome sequences is key to understanding the genetic basis of disease progression, patient survival and response to therapy. Benchmarking is needed for tool assessment and improvement but is complicated by a lack of gold standards, by extensive resource requirements and by difficulties in sharing personal genomic information. To resolve these issues, we launched the ICGC-TCGA DREAM Somatic Mutation Calling Challenge, a crowdsourced benchmark of somatic mutation detection algorithms. Here we report the BAMSurgeon tool for simulating cancer genomes and the results of 248 analyses of three in silico tumors created with it. Different algorithms exhibit characteristic error profiles, and, intriguingly, false positives show a trinucleotide profile very similar to one found in human tumors. Although the three simulated tumors differ in sequence contamination (deviation from normal cell sequence) and in subclonality, an ensemble of pipelines outperforms the best individual pipeline in all cases. BAMSurgeon is available at https://github.com/adamewing/bamsurgeon/.
Keyword Somatic mutation
Cancer
Benchmarking
Tumor genome
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID RS2014-01
R01-CA180778
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Mater Research Institute-UQ (MRI-UQ)
Official 2016 Collection
 
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