PLoS Computational Biology (Jul 2015)

Genome Modeling System: A Knowledge Management Platform for Genomics.

  • Malachi Griffith,
  • Obi L Griffith,
  • Scott M Smith,
  • Avinash Ramu,
  • Matthew B Callaway,
  • Anthony M Brummett,
  • Michael J Kiwala,
  • Adam C Coffman,
  • Allison A Regier,
  • Ben J Oberkfell,
  • Gabriel E Sanderson,
  • Thomas P Mooney,
  • Nathaniel G Nutter,
  • Edward A Belter,
  • Feiyu Du,
  • Robert L Long,
  • Travis E Abbott,
  • Ian T Ferguson,
  • David L Morton,
  • Mark M Burnett,
  • James V Weible,
  • Joshua B Peck,
  • Adam Dukes,
  • Joshua F McMichael,
  • Justin T Lolofie,
  • Brian R Derickson,
  • Jasreet Hundal,
  • Zachary L Skidmore,
  • Benjamin J Ainscough,
  • Nathan D Dees,
  • William S Schierding,
  • Cyriac Kandoth,
  • Kyung H Kim,
  • Charles Lu,
  • Christopher C Harris,
  • Nicole Maher,
  • Christopher A Maher,
  • Vincent J Magrini,
  • Benjamin S Abbott,
  • Ken Chen,
  • Eric Clark,
  • Indraniel Das,
  • Xian Fan,
  • Amy E Hawkins,
  • Todd G Hepler,
  • Todd N Wylie,
  • Shawn M Leonard,
  • William E Schroeder,
  • Xiaoqi Shi,
  • Lynn K Carmichael,
  • Matthew R Weil,
  • Richard W Wohlstadter,
  • Gary Stiehr,
  • Michael D McLellan,
  • Craig S Pohl,
  • Christopher A Miller,
  • Daniel C Koboldt,
  • Jason R Walker,
  • James M Eldred,
  • David E Larson,
  • David J Dooling,
  • Li Ding,
  • Elaine R Mardis,
  • Richard K Wilson

DOI
https://doi.org/10.1371/journal.pcbi.1004274
Journal volume & issue
Vol. 11, no. 7
p. e1004274

Abstract

Read online

In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms.