03376nas a2200793   4500008004100000022001400041245011500055210006900170260001600239520112300255653001101378653000801389653002001397100001901417700002601436700001201462700001801474700002201492700002701514700002201541700002201563700001901585700002901604700002301633700002001656700002001676700002301696700002501719700002201744700002201766700002101788700004001809700001201849700001701861700001201878700001601890700001901906700001801925700002101943700001601964700002001980700002402000700002002024700001802044700001302062700001702075700001802092700002102110700002402131700002002155700002102175700001702196700002102213700002502234700002102259700001902280700001902299700002102318700001602339700001402355700001702369700001502386700001302401700003102414700002102445700002102466700002002487856007502507       2015                        eng d  a1548-710500aCombining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection.0 aCombining tumor genome simulation with crowdsourcing to benchmar  c2015 May 183 aThe 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/.10acancer10aNGS10avariant calling1 aEwing, Adam, D1 aHoulahan, Kathleen, E1 aHu, Yin1 aEllrott, Kyle1 aCaloian, Cristian1 aYamaguchi, Takafumi, N1 aBare, Christopher1 aP’ng, Christine1 aWaggott, Daryl1 aSabelnykova, Veronica, Y1 aKellen, Michael, R1 aNorman, Thea, C1 aHaussler, David1 aFriend, Stephen, H1 aStolovitzky, Gustavo1 aMargolin, Adam, A1 aStuart, Joshua, M1 aBoutros, Paul, C1 aparticipants, ICGC-TCGA, DREAM Soma1 aXi, Liu1 aDewal, Ninad1 aFan, Yu1 aWang, Wenyi1 aWheeler, David1 aWilm, Andreas1 aTing, Grace, Hui1 aLi, Chenhao1 aBertrand, Denis1 aNagarajan, Niranjan1 aChen, Qing-Rong1 aHsu, Chih-Hao1 aHu, Ying1 aYan, Chunhua1 aKibbe, Warren1 aMeerzaman, Daoud1 aCibulskis, Kristian1 aRosenberg, Mara1 aBergelson, Louis1 aKiezun, Adam1 aRadenbaugh, Amie1 aSertier, Anne-Sophie1 aFerrari, Anthony1 aTonton, Laurie1 aBhutani, Kunal1 aHansen, Nancy, F1 aWang, Difei1 aSong, Lei1 aLai, Zhongwu1 aLiao, Yang1 aShi, Wei1 aCarbonell-Caballero, José1 aDopazo, Joaquín1 aLau, Cheryl, C K1 aGuinney, Justin  uhttp://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3407.html02147nas a2200301   4500008004100000022001400041245009600055210006900151260001600220520124900236100002101485700002401506700001401530700001401544700002201558700001701580700001701597700001701614700002301631700001701654700001801671700001501689700001901704700001801723700001501741700001801756856007101774       2015                        eng d  a1546-169600aPrediction of human population responses to toxic compounds by a collaborative competition.0 aPrediction of human population responses to toxic compounds by a  c2015 Aug 103 aThe ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson’s r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.1 aEduati, Federica1 aMangravite, Lara, M1 aWang, Tao1 aTang, Hao1 aBare, Christopher1 aHuang, Ruili1 aNorman, Thea1 aKellen, Mike1 aMenden, Michael, P1 aYang, Jichen1 aZhan, Xiaowei1 aZhong, Rui1 aXiao, Guanghua1 aXia, Menghang1 aAbdo, Nour1 aKosyk, Oksana  uhttp://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3299.html