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Genome Modeling System: A Knowledge Management Platform for Genomics.

Authors :
Griffith M
Griffith OL
Smith SM
Ramu A
Callaway MB
Brummett AM
Kiwala MJ
Coffman AC
Regier AA
Oberkfell BJ
Sanderson GE
Mooney TP
Nutter NG
Belter EA
Du F
Long RL
Abbott TE
Ferguson IT
Morton DL
Burnett MM
Weible JV
Peck JB
Dukes A
McMichael JF
Lolofie JT
Derickson BR
Hundal J
Skidmore ZL
Ainscough BJ
Dees ND
Schierding WS
Kandoth C
Kim KH
Lu C
Harris CC
Maher N
Maher CA
Magrini VJ
Abbott BS
Chen K
Clark E
Das I
Fan X
Hawkins AE
Hepler TG
Wylie TN
Leonard SM
Schroeder WE
Shi X
Carmichael LK
Weil MR
Wohlstadter RW
Stiehr G
McLellan MD
Pohl CS
Miller CA
Koboldt DC
Walker JR
Eldred JM
Larson DE
Dooling DJ
Ding L
Mardis ER
Wilson RK
Source :
PLoS computational biology [PLoS Comput Biol] 2015 Jul 09; Vol. 11 (7), pp. e1004274. Date of Electronic Publication: 2015 Jul 09 (Print Publication: 2015).
Publication Year :
2015

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.

Details

Language :
English
ISSN :
1553-7358
Volume :
11
Issue :
7
Database :
MEDLINE
Journal :
PLoS computational biology
Publication Type :
Academic Journal
Accession number :
26158448
Full Text :
https://doi.org/10.1371/journal.pcbi.1004274