Genome Modeling System: A Knowledge Management Platform for Genomics.

Griffith M1, Griffith OL2, Smith SM3, Ramu A3, Callaway MB3, Brummett AM3, Kiwala MJ3, Coffman AC3, Regier AA3, Oberkfell BJ3, Sanderson GE3, Mooney TP3, Nutter NG3, Belter EA3, Du F3, Long RL3, Abbott TE3, Ferguson IT3, Morton DL3, Burnett MM3, Weible JV3, Peck JB3, Dukes A3, McMichael JF3, Lolofie JT3, Derickson BR3, Hundal J3, Skidmore ZL3, Ainscough BJ3, Dees ND3, Schierding WS3, Kandoth C3, Kim KH3, Lu C3, Harris CC3, Maher N4, Maher CA5,Magrini VJ1, Abbott BS3, Chen K3, Clark E3, Das I3, Fan X3, Hawkins AE3, Hepler TG3, Wylie TN3, Leonard SM3, Schroeder WE3, Shi X3, Carmichael LK3, Weil MR3, Wohlstadter RW3, Stiehr G3, McLellan MD3, Pohl CS3, Miller CA3, Koboldt DC3, Walker JR3, Eldred JM3, Larson DE1, Dooling DJ3, Ding L6, Mardis ER7,Wilson RK7.

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Abstract

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.