TY - JOUR T1 - Reporting guidelines for human microbiome research: the STORMS checklist. JF - Nat Med Y1 - 2021 A1 - Mirzayi, Chloe A1 - Renson, Audrey A1 - Zohra, Fatima A1 - Elsafoury, Shaimaa A1 - Geistlinger, Ludwig A1 - Kasselman, Lora J A1 - Eckenrode, Kelly A1 - van de Wijgert, Janneke A1 - Loughman, Amy A1 - Marques, Francine Z A1 - MacIntyre, David A A1 - Arumugam, Manimozhiyan A1 - Azhar, Rimsha A1 - Beghini, Francesco A1 - Bergstrom, Kirk A1 - Bhatt, Ami A1 - Bisanz, Jordan E A1 - Braun, Jonathan A1 - Bravo, Hector Corrada A1 - Buck, Gregory A A1 - Bushman, Frederic A1 - Casero, David A1 - Clarke, Gerard A1 - Collado, Maria Carmen A1 - Cotter, Paul D A1 - Cryan, John F A1 - Demmer, Ryan T A1 - Devkota, Suzanne A1 - Elinav, Eran A1 - Escobar, Juan S A1 - Fettweis, Jennifer A1 - Finn, Robert D A1 - Fodor, Anthony A A1 - Forslund, Sofia A1 - Franke, Andre A1 - Furlanello, Cesare A1 - Gilbert, Jack A1 - Grice, Elizabeth A1 - Haibe-Kains, Benjamin A1 - Handley, Scott A1 - Herd, Pamela A1 - Holmes, Susan A1 - Jacobs, Jonathan P A1 - Karstens, Lisa A1 - Knight, Rob A1 - Knights, Dan A1 - Koren, Omry A1 - Kwon, Douglas S A1 - Langille, Morgan A1 - Lindsay, Brianna A1 - McGovern, Dermot A1 - McHardy, Alice C A1 - McWeeney, Shannon A1 - Mueller, Noel T A1 - Nezi, Luigi A1 - Olm, Matthew A1 - Palm, Noah A1 - Pasolli, Edoardo A1 - Raes, Jeroen A1 - Redinbo, Matthew R A1 - Rühlemann, Malte A1 - Balfour Sartor, R A1 - Schloss, Patrick D A1 - Schriml, Lynn A1 - Segal, Eran A1 - Shardell, Michelle A1 - Sharpton, Thomas A1 - Smirnova, Ekaterina A1 - Sokol, Harry A1 - Sonnenburg, Justin L A1 - Srinivasan, Sujatha A1 - Thingholm, Louise B A1 - Turnbaugh, Peter J A1 - Upadhyay, Vaibhav A1 - Walls, Ramona L A1 - Wilmes, Paul A1 - Yamada, Takuji A1 - Zeller, Georg A1 - Zhang, Mingyu A1 - Zhao, Ni A1 - Zhao, Liping A1 - Bao, Wenjun A1 - Culhane, Aedin A1 - Devanarayan, Viswanath A1 - Dopazo, Joaquin A1 - Fan, Xiaohui A1 - Fischer, Matthias A1 - Jones, Wendell A1 - Kusko, Rebecca A1 - Mason, Christopher E A1 - Mercer, Tim R A1 - Sansone, Susanna-Assunta A1 - Scherer, Andreas A1 - Shi, Leming A1 - Thakkar, Shraddha A1 - Tong, Weida A1 - Wolfinger, Russ A1 - Hunter, Christopher A1 - Segata, Nicola A1 - Huttenhower, Curtis A1 - Dowd, Jennifer B A1 - Jones, Heidi E A1 - Waldron, Levi KW - Computational Biology KW - Dysbiosis KW - Humans KW - Microbiota KW - Observational Studies as Topic KW - Research Design KW - Translational Science, Biomedical AB -

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.

VL - 27 IS - 11 U1 - https://www.ncbi.nlm.nih.gov/pubmed/34789871?dopt=Abstract 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 -