Combining accurate tumor genome simulation with crowdsourcing to benchmark somatic structural variant detection.
Combining accurate tumor genome simulation with crowdsourcing to benchmark somatic structural variant detection.
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DOI：
10.1186/s13059-018-1539-5
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发表时间：
2018-11-06
期刊：
影响因子：
12.3
通讯作者：
Boutros PC
中科院分区：
文献类型：
--
作者：
Lee AY;Ewing AD;Ellrott K;Hu Y;Houlahan KE;Bare JC;Espiritu SMG;Huang V;Dang K;Chong Z;Caloian C;Yamaguchi TN;ICGC-TCGA DREAM Somatic Mutation Calling Challenge Participants;Kellen MR;Chen K;Norman TC;Friend SH;Guinney J;Stolovitzky G;Haussler D;Margolin AA;Stuart JM;Boutros PC
癌细胞的表型部分由体细胞结构变异驱动。结构变异可以引发肿瘤，增强其侵袭性，并提供独特的治疗机会。肿瘤的全基因组测序可以允许详尽地鉴定个体癌症中存在的特定结构变体，从而促进临床诊断和发现新的致突变机制。已经创建了过多的体细胞结构变异检测算法来实现这些发现;然而，没有系统的基准。体细胞结构变异检测方法的严格性能评估受到缺乏金标准、广泛的资源需求以及因需要共享个人基因组信息而产生的困难的挑战。为了便于结构变异检测算法的评估，我们创建了一个强大的模拟框架，体细胞结构变异的扩展BAMSurgeon算法。然后，我们在ICGC-TCGA DREAM体细胞突变呼叫挑战赛（SMC-DNA）中组织并启用众包基准测试。我们在这里报告了三种不同肿瘤的结构变异基准测试结果，包括来自15个团队的204份提交材料。除了排名方法外，我们还确定了各个算法的特征误差分布以及它们之间的一般趋势。令人惊讶的是，我们发现分析管道的集合并不总是优于最好的单个方法，这表明需要新的方法来聚合体细胞结构变异检测方法。合成肿瘤和体细胞结构变异检测排行榜仍然可作为社区基准资源，BAMSurgeon可在https://github.com/adamewing/bamsurgeon上获得。本文的在线版本（10.1186/s13059-018-1539-5）包含补充材料，可供授权用户使用。
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. 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. 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. The online version of this article (10.1186/s13059-018-1539-5) contains supplementary material, which is available to authorized users.
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DOI：
10.1093/bioinformatics/btq033
发表时间：
2010-03-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Quinlan AR;Hall IM
通讯作者：
Hall IM
影响因子：
3
作者：
Slater GS;Birney E
通讯作者：
Birney E
影响因子：
48
作者：
Chong Z;Ruan J;Gao M;Zhou W;Chen T;Fan X;Ding L;Lee AY;Boutros P;Chen J;Chen K
通讯作者：
Chen K
DOI：
10.1073/pnas.79.24.7837
发表时间：
1982-01-01
期刊：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA-BIOLOGICAL SCIENCES
影响因子：
--
作者：
TAUB, R;KIRSCH, I;LEDER, P
通讯作者：
LEDER, P
影响因子：
12.3
作者：
Boutros PC;Margolin AA;Stuart JM;Califano A;Stolovitzky G
通讯作者：
Stolovitzky G