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/.
journal_name
Nat Methodsjournal_title
Nature methodsauthors
Ewing AD,Houlahan KE,Hu Y,Ellrott K,Caloian C,Yamaguchi TN,Bare JC,P'ng C,Waggott D,Sabelnykova VY,ICGC-TCGA DREAM Somatic Mutation Calling Challenge participants.,Kellen MR,Norman TC,Haussler D,Friend SH,Stolovitzky G,Madoi
10.1038/nmeth.3407subject
Has Abstractpub_date
2015-07-01 00:00:00pages
623-30issue
7eissn
1548-7091issn
1548-7105pii
nmeth.3407journal_volume
12pub_type
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