Cloud-based uniform ChIP-Seq processing tools for modENCODE and ENCODE.
Cloud-based uniform ChIP-Seq processing tools for modENCODE and ENCODE.
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DOI：
10.1186/1471-2164-14-494
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发表时间：
2013-07-22
期刊：
影响因子：
4.4
通讯作者：
Stein LD
中科院分区：
文献类型：
--
作者：
Trinh QM;Jen FY;Zhou Z;Chu KM;Perry MD;Kephart ET;Contrino S;Ruzanov P;Stein LD
Funded by the National Institutes of Health (NIH), the aim of the Model Organism ENCyclopedia of DNA Elements (modENCODE) project is to provide the biological research community with a comprehensive encyclopedia of functional genomic elements for both model organisms C. elegans (worm) and D. melanogaster (fly). With a total size of just under 10 terabytes of data collected and released to the public, one of the challenges faced by researchers is to extract biologically meaningful knowledge from this large data set. While the basic quality control, pre-processing, and analysis of the data has already been performed by members of the modENCODE consortium, many researchers will wish to reinterpret the data set using modifications and enhancements of the original protocols, or combine modENCODE data with other data sets. Unfortunately this can be a time consuming and logistically challenging proposition. In recognition of this challenge, the modENCODE DCC has released uniform computing resources for analyzing modENCODE data on Galaxy (https://github.com/modENCODE-DCC/Galaxy), on the public Amazon Cloud (http://aws.amazon.com), and on the private Bionimbus Cloud for genomic research (http://www.bionimbus.org). In particular, we have released Galaxy workflows for interpreting ChIP-seq data which use the same quality control (QC) and peak calling standards adopted by the modENCODE and ENCODE communities. For convenience of use, we have created Amazon and Bionimbus Cloud machine images containing Galaxy along with all the modENCODE data, software and other dependencies. Using these resources provides a framework for running consistent and reproducible analyses on modENCODE data, ultimately allowing researchers to use more of their time using modENCODE data, and less time moving it around.
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影响因子：
14.9
作者：
Contrino S;Smith RN;Butano D;Carr A;Hu F;Lyne R;Rutherford K;Kalderimis A;Sullivan J;Carbon S;Kephart ET;Lloyd P;Stinson EO;Washington NL;Perry MD;Ruzanov P;Zha Z;Lewis SE;Stein LD;Micklem G
通讯作者：
Micklem G
影响因子：
14.9
作者：
Bairoch, Amos;Bougueleret, Lydie;Zhang, Jian
通讯作者：
Zhang, Jian
影响因子：
12.3
作者：
Goecks J;Nekrutenko A;Taylor J;Galaxy Team
通讯作者：
Galaxy Team
影响因子：
14.9
作者：
Tweedie S;Ashburner M;Falls K;Leyland P;McQuilton P;Marygold S;Millburn G;Osumi-Sutherland D;Schroeder A;Seal R;Zhang H;FlyBase Consortium
通讯作者：
FlyBase Consortium
2006年的基因本体论（GO）项目。
影响因子：
14.9
作者：
Harris, Midori A.;Clark, Jennifer I.;Ireland, Amelia;Lomax, Jane;Ashburner, Michael;Collins, Russell;Eilbeck, Karen;Lewis, Suzanna;Mungall, Chris;Richter, John;Rubin, Gerald M.;Shu, ShengQiang;Blake, Judith A.;Bult, Carol J.;Diehl, Alexander D.;Dolan, Mary E.;Drabkin, Harold J.;Eppig, Janan T.;Hill, David P.;Ni, Li;Ringwald, Martin;Balakrishnan, Rama;Binkley, Gail;Cherry, J. Michael;Christie, Karen R.;Costanzo, Maria C.;Dong, Qing;Engel, Stacia R.;Fisk, Dianna G.;Hirschman, Jodi E.;Hitz, Benjamin C.;Hong, Eurie L.;Lane, Christopher;Miyasato, Stuart;Nash, Robert;Sethuraman, Anand;Skrzypek, Marek;Theesfeld, Chandra L.;Weng, Shuai;Botstein, David;Dolinski, Kara;Oughtred, Rose;Berardini, Tanya;Mundodi, Suparna;Rhee, Seung Y.;Apweiler, Rolf;Barrell, Daniel;Camon, Evelyn;Dimmer, Emily;Mulder, Nicola;Chisholm, Rex;Fey, Petra;Gaudet, Pascale;Kibbe, Warren;Pilcher, Karen;Bastiani, Carol A.;Kishore, Ranjana;Schwarz, Erich M.;Sternberg, Paul;Van Auken, Kimberly;Gwinn, Michelle;Hannick, Linda;Wortman, Jennifer;Aslett, Martin;Berriman, Matthew;Wood, Valerie;Bromberg, Susan;Foote, Cindy;Jacob, Howard;Pasko, Dean;Petri, Victoria;Reilly, Dorothy;Seiler, Kathy;Shimoyama, Mary;Smith, Jennifer;Twigger, Simon;Jaiswal, Pankaj;Seigfried, Trent;Collmer, Candace;Howe, Doug;Westerfield, Monte
通讯作者：
Westerfield, Monte