TY - JOUR T1 - Editorial: Critical assessment of massive data analysis (CAMDA) annual conference 2021. JF - Front Genet Y1 - 2023 A1 - Łabaj, Paweł P A1 - Dopazo, Joaquin A1 - Xiao, Wenzhong A1 - Kreil, David P VL - 14 ER - TY - JOUR T1 - The NCI Genomic Data Commons JF - Nature Genetics Y1 - 2021 A1 - Heath, Allison P. A1 - Ferretti, Vincent A1 - Agrawal, Stuti A1 - An, Maksim A1 - Angelakos, James C. A1 - Arya, Renuka A1 - Bajari, Rosita A1 - Baqar, Bilal A1 - Barnowski, Justin H. B. A1 - Burt, Jeffrey A1 - Catton, Ann A1 - Chan, Brandon F. A1 - Chu, Fay A1 - Cullion, Kim A1 - Davidsen, Tanja A1 - Do, Phuong-My A1 - Dompierre, Christian A1 - Ferguson, Martin L. A1 - Fitzsimons, Michael S. A1 - Ford, Michael A1 - Fukuma, Miyuki A1 - Gaheen, Sharon A1 - Ganji, Gajanan L. A1 - Garcia, Tzintzuni I. A1 - George, Sameera S. A1 - Gerhard, Daniela S. A1 - Gerthoffert, Francois A1 - Gomez, Fauzi A1 - Han, Kang A1 - Hernandez, Kyle M. A1 - Issac, Biju A1 - Jackson, Richard A1 - Jensen, Mark A. A1 - Joshi, Sid A1 - Kadam, Ajinkya A1 - Khurana, Aishmit A1 - Kim, Kyle M. J. A1 - Kraft, Victoria E. A1 - Li, Shenglai A1 - Lichtenberg, Tara M. A1 - Lodato, Janice A1 - Lolla, Laxmi A1 - Martinov, Plamen A1 - Mazzone, Jeffrey A. A1 - Miller, Daniel P. A1 - Miller, Ian A1 - Miller, Joshua S. A1 - Miyauchi, Koji A1 - Murphy, Mark W. A1 - Nullet, Thomas A1 - Ogwara, Rowland O. A1 - Ortuño, Francisco M. A1 - Pedrosa, Jesús A1 - Pham, Phuong L. A1 - Popov, Maxim Y. A1 - Porter, James J. A1 - Powell, Raymond A1 - Rademacher, Karl A1 - Reid, Colin P. A1 - Rich, Samantha A1 - Rogel, Bessie A1 - Sahni, Himanso A1 - Savage, Jeremiah H. A1 - Schmitt, Kyle A. A1 - Simmons, Trevar J. A1 - Sislow, Joseph A1 - Spring, Jonathan A1 - Stein, Lincoln A1 - Sullivan, Sean A1 - Tang, Yajing A1 - Thiagarajan, Mathangi A1 - Troyer, Heather D. A1 - Wang, Chang A1 - Wang, Zhining A1 - West, Bedford L. A1 - Wilmer, Alex A1 - Wilson, Shane A1 - Wu, Kaman A1 - Wysocki, William P. A1 - Xiang, Linda A1 - Yamada, Joseph T. A1 - Yang, Liming A1 - Yu, Christine A1 - Yung, Christina K. A1 - Zenklusen, Jean Claude A1 - Zhang, Junjun A1 - Zhang, Zhenyu A1 - Zhao, Yuanheng A1 - Zubair, Ariz A1 - Staudt, Louis M. A1 - Grossman, Robert L. UR - http://www.nature.com/articles/s41588-021-00791-5 JO - Nat Genet ER - TY - JOUR T1 - Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection. JF - Nature methods Y1 - 2015 A1 - Ewing, Adam D A1 - Houlahan, Kathleen E A1 - Hu, Yin A1 - Ellrott, Kyle A1 - Caloian, Cristian A1 - Yamaguchi, Takafumi N A1 - Bare, J Christopher A1 - P’ng, Christine A1 - Waggott, Daryl A1 - Sabelnykova, Veronica Y A1 - Kellen, Michael R A1 - Norman, Thea C A1 - Haussler, David A1 - Friend, Stephen H A1 - Stolovitzky, Gustavo A1 - Margolin, Adam A A1 - Stuart, Joshua M A1 - Boutros, Paul C ED - ICGC-TCGA DREAM Somatic Mutation Calling Challenge participants ED - Liu Xi ED - Ninad Dewal ED - Yu Fan ED - Wenyi Wang ED - David Wheeler ED - Andreas Wilm ED - Grace Hui Ting ED - Chenhao Li ED - Denis Bertrand ED - Niranjan Nagarajan ED - Qing-Rong Chen ED - Chih-Hao Hsu ED - Ying Hu ED - Chunhua Yan ED - Warren Kibbe ED - Daoud Meerzaman ED - Kristian Cibulskis ED - Mara Rosenberg ED - Louis Bergelson ED - Adam Kiezun ED - Amie Radenbaugh ED - Anne-Sophie Sertier ED - Anthony Ferrari ED - Laurie Tonton ED - Kunal Bhutani ED - Nancy F Hansen ED - Difei Wang ED - Lei Song ED - Zhongwu Lai ED - Liao, Yang ED - Shi, Wei ED - Carbonell-Caballero, José ED - Joaquín Dopazo ED - Cheryl C K Lau ED - Justin Guinney KW - cancer KW - NGS KW - variant calling AB - 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/. UR - http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3407.html ER - TY - JOUR T1 - Prediction of human population responses to toxic compounds by a collaborative competition. JF - Nature biotechnology Y1 - 2015 A1 - Eduati, Federica A1 - Mangravite, Lara M A1 - Wang, Tao A1 - Tang, Hao A1 - Bare, J Christopher A1 - Huang, Ruili A1 - Norman, Thea A1 - Kellen, Mike A1 - Menden, Michael P A1 - Yang, Jichen A1 - Zhan, Xiaowei A1 - Zhong, Rui A1 - Xiao, Guanghua A1 - Xia, Menghang A1 - Abdo, Nour A1 - Kosyk, Oksana AB - The 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. UR - http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3299.html ER - TY - JOUR T1 - Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures. JF - Nature communications Y1 - 2014 A1 - Munro, Sarah A A1 - Lund, Steven P A1 - Pine, P Scott A1 - Binder, Hans A1 - Clevert, Djork-Arné A1 - Ana Conesa A1 - Dopazo, Joaquin A1 - Fasold, Mario A1 - Hochreiter, Sepp A1 - Hong, Huixiao A1 - Jafari, Nadereh A1 - Kreil, David P A1 - Labaj, Paweł P A1 - Li, Sheng A1 - Liao, Yang A1 - Lin, Simon M A1 - Meehan, Joseph A1 - Mason, Christopher E A1 - Santoyo-López, Javier A1 - Setterquist, Robert A A1 - Shi, Leming A1 - Shi, Wei A1 - Smyth, Gordon K A1 - Stralis-Pavese, Nancy A1 - Su, Zhenqiang A1 - Tong, Weida A1 - Wang, Charles A1 - Wang, Jian A1 - Xu, Joshua A1 - Ye, Zhan A1 - Yang, Yong A1 - Yu, Ying A1 - Salit, Marc KW - RNA-seq AB - There is a critical need for standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments. Here we assess technical performance with a proposed standard ’dashboard’ of metrics derived from analysis of external spike-in RNA control ratio mixtures. These control ratio mixtures with defined abundance ratios enable assessment of diagnostic performance of differentially expressed transcript lists, limit of detection of ratio (LODR) estimates and expression ratio variability and measurement bias. The performance metrics suite is applicable to analysis of a typical experiment, and here we also apply these metrics to evaluate technical performance among laboratories. An interlaboratory study using identical samples shared among 12 laboratories with three different measurement processes demonstrates generally consistent diagnostic power across 11 laboratories. Ratio measurement variability and bias are also comparable among laboratories for the same measurement process. We observe different biases for measurement processes using different mRNA-enrichment protocols. VL - 5 UR - http://www.nature.com/ncomms/2014/140925/ncomms6125/full/ncomms6125.html ER - TY - JOUR T1 - The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. JF - Nature biotechnology Y1 - 2010 A1 - Shi, Leming A1 - Campbell, Gregory A1 - Jones, Wendell D A1 - Campagne, Fabien A1 - Wen, Zhining A1 - Walker, Stephen J A1 - Su, Zhenqiang A1 - Chu, Tzu-Ming A1 - Goodsaid, Federico M A1 - Pusztai, Lajos A1 - Shaughnessy, John D A1 - Oberthuer, André A1 - Thomas, Russell S A1 - Paules, Richard S A1 - Fielden, Mark A1 - Barlogie, Bart A1 - Chen, Weijie A1 - Du, Pan A1 - Fischer, Matthias A1 - Furlanello, Cesare A1 - Gallas, Brandon D A1 - Ge, Xijin A1 - Megherbi, Dalila B A1 - Symmans, W Fraser A1 - Wang, May D A1 - Zhang, John A1 - Bitter, Hans A1 - Brors, Benedikt A1 - Bushel, Pierre R A1 - Bylesjo, Max A1 - Chen, Minjun A1 - Cheng, Jie A1 - Cheng, Jing A1 - Chou, Jeff A1 - Davison, Timothy S A1 - Delorenzi, Mauro A1 - Deng, Youping A1 - Devanarayan, Viswanath A1 - Dix, David J A1 - Dopazo, Joaquin A1 - Dorff, Kevin C A1 - Elloumi, Fathi A1 - Fan, Jianqing A1 - Fan, Shicai A1 - Fan, Xiaohui A1 - Fang, Hong A1 - Gonzaludo, Nina A1 - Hess, Kenneth R A1 - Hong, Huixiao A1 - Huan, Jun A1 - Irizarry, Rafael A A1 - Judson, Richard A1 - Juraeva, Dilafruz A1 - Lababidi, Samir A1 - Lambert, Christophe G A1 - Li, Li A1 - Li, Yanen A1 - Li, Zhen A1 - Lin, Simon M A1 - Liu, Guozhen A1 - Lobenhofer, Edward K A1 - Luo, Jun A1 - Luo, Wen A1 - McCall, Matthew N A1 - Nikolsky, Yuri A1 - Pennello, Gene A A1 - Perkins, Roger G A1 - Philip, Reena A1 - Popovici, Vlad A1 - Price, Nathan D A1 - Qian, Feng A1 - Scherer, Andreas A1 - Shi, Tieliu A1 - Shi, Weiwei A1 - Sung, Jaeyun A1 - Thierry-Mieg, Danielle A1 - Thierry-Mieg, Jean A1 - Thodima, Venkata A1 - Trygg, Johan A1 - Vishnuvajjala, Lakshmi A1 - Wang, Sue Jane A1 - Wu, Jianping A1 - Wu, Yichao A1 - Xie, Qian A1 - Yousef, Waleed A A1 - Zhang, Liang A1 - Zhang, Xuegong A1 - Zhong, Sheng A1 - Zhou, Yiming A1 - Zhu, Sheng A1 - Arasappan, Dhivya A1 - Bao, Wenjun A1 - Lucas, Anne Bergstrom A1 - Berthold, Frank A1 - Brennan, Richard J A1 - Buness, Andreas A1 - Catalano, Jennifer G A1 - Chang, Chang A1 - Chen, Rong A1 - Cheng, Yiyu A1 - Cui, Jian A1 - Czika, Wendy A1 - Demichelis, Francesca A1 - Deng, Xutao A1 - Dosymbekov, Damir A1 - Eils, Roland A1 - Feng, Yang A1 - Fostel, Jennifer A1 - Fulmer-Smentek, Stephanie A1 - Fuscoe, James C A1 - Gatto, Laurent A1 - Ge, Weigong A1 - Goldstein, Darlene R A1 - Guo, Li A1 - Halbert, Donald N A1 - Han, Jing A1 - Harris, Stephen C A1 - Hatzis, Christos A1 - Herman, Damir A1 - Huang, Jianping A1 - Jensen, Roderick V A1 - Jiang, Rui A1 - Johnson, Charles D A1 - Jurman, Giuseppe A1 - Kahlert, Yvonne A1 - Khuder, Sadik A A1 - Kohl, Matthias A1 - Li, Jianying A1 - Li, Li A1 - Li, Menglong A1 - Li, Quan-Zhen A1 - Li, Shao A1 - Li, Zhiguang A1 - Liu, Jie A1 - Liu, Ying A1 - Liu, Zhichao A1 - Meng, Lu A1 - Madera, Manuel A1 - Martinez-Murillo, Francisco A1 - Medina, Ignacio A1 - Meehan, Joseph A1 - Miclaus, Kelci A1 - Moffitt, Richard A A1 - Montaner, David A1 - Mukherjee, Piali A1 - Mulligan, George J A1 - Neville, Padraic A1 - Nikolskaya, Tatiana A1 - Ning, Baitang A1 - Page, Grier P A1 - Parker, Joel A1 - Parry, R Mitchell A1 - Peng, Xuejun A1 - Peterson, Ron L A1 - Phan, John H A1 - Quanz, Brian A1 - Ren, Yi A1 - Riccadonna, Samantha A1 - Roter, Alan H A1 - Samuelson, Frank W A1 - Schumacher, Martin M A1 - Shambaugh, Joseph D A1 - Shi, Qiang A1 - Shippy, Richard A1 - Si, Shengzhu A1 - Smalter, Aaron A1 - Sotiriou, Christos A1 - Soukup, Mat A1 - Staedtler, Frank A1 - Steiner, Guido A1 - Stokes, Todd H A1 - Sun, Qinglan A1 - Tan, Pei-Yi A1 - Tang, Rong A1 - Tezak, Zivana A1 - Thorn, Brett A1 - Tsyganova, Marina A1 - Turpaz, Yaron A1 - Vega, Silvia C A1 - Visintainer, Roberto A1 - von Frese, Juergen A1 - Wang, Charles A1 - Wang, Eric A1 - Wang, Junwei A1 - Wang, Wei A1 - Westermann, Frank A1 - Willey, James C A1 - Woods, Matthew A1 - Wu, Shujian A1 - Xiao, Nianqing A1 - Xu, Joshua A1 - Xu, Lei A1 - Yang, Lun A1 - Zeng, Xiao A1 - Zhang, Jialu A1 - Zhang, Li A1 - Zhang, Min A1 - Zhao, Chen A1 - Puri, Raj K A1 - Scherf, Uwe A1 - Tong, Weida A1 - Wolfinger, Russell D AB -

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

VL - 28 UR - http://www.nature.com/nbt/journal/v28/n8/full/nbt.1665.html ER -