Genome Modeling System: A Knowledge Management Platform for Genomics.

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

doi:10.1371/journal.pcbi.1004274

Malachi Griffith, Lynn K Carmichael + Show 61 more

Open Access

https://doi.org/10.1371/journal.pcbi.1004274

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Journal: PLoS computational biology	Publication Date: Jul 9, 2015
Citations: 100	License type: CC BY 4.0

Affiliation: Washington University in St. Louis

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.

Highlights

The increasing sequence data output of massively parallel sequencing platforms [1] has allowed the application of sequencing to an incredible diversity of research projects in the biological, genomic, and medical fields [2,3,4,5,6]
A typical genome analysis using the Genome Modeling System (GMS) might start from any combination of whole-genome, exome or RNA-seq data and produce alignments against a reference genome, somatic variant calls including single nucleotide variants (SNVs), structural variants (SVs), copy-number variants (CNVs), transcript expression levels, RNA fusion predictions, and more
To address challenges of scale, tracking, optimization, and reproducibility, we have developed an analysis information management system called the Genome Modeling System (GMS)

Summary

Introduction

The increasing sequence data output of massively parallel sequencing platforms [1] has allowed the application of sequencing to an incredible diversity of research projects in the biological, genomic, and medical fields [2,3,4,5,6]. Data are processed through various analysis pipelines (e.g., reference alignment, somatic variation detection, etc.) that in turn are managed and monitored by a workflow system (Box 1).

Results

Conclusion