Genome modeling system: A knowledge management platform for genomics

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

Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps

We expanded GWAS discovery for type 2 diabetes (T2D) by combining data from 898,130 European-descent individuals (9% cases), after imputation to high-density reference panels. With these data, we (i) extend the inventory of T2D-risk variants (243 loci,135 newly implicated in T2D predisposition, comprising 403 distinct association signals); (ii) enrich discovery of lower-frequency risk alleles (80 index variants with minor allele frequency 2); (iii) substantially improve fine-mapping of causal variants (at 51 signals, one variant accounted for >80% posterior probability of association (PPA)); (iv) extend fine-mapping through integration of tissue-specific epigenomic information (islet regulatory annotations extend the number of variants with PPA >80% to 73); (v) highlight validated therapeutic targets (18 genes with associations attributable to coding variants); and (vi) demonstrate enhanced potential for clinical translation (genome-wide chip heritability explains 18% of T2D risk; individuals in the extremes of a T2D polygenic risk score differ more than ninefold in prevalence).Peer reviewe

Somatic variation processing profile and workflow.

<p>To illustrate key GMS concepts, the processing profiles and workflow for the somatic variation pipeline are shown. Abbreviations: copy number variant (CNV), copy number amplification (CNA), genome analysis tool kit (GATK), insertion/deletion (Indel), loss of heterozygosity (LOH), mapping quality (MQ), single nucleotide variant (SNV), structural variant (SV), variant allele frequency (VAF).</p

Major GMS pipelines.

<p>A brief description of each analysis pipeline tested for initial release of the GMS.</p

Overview of the GMS.

<p>The genome modeling system (GMS) is implemented to use a federated disk SAN, with meta-data stored in a PostgreSQL relational database. Sample management tools allow the import of new samples and instrument data. Data are then processed through various analysis pipelines (e.g., reference alignment, somatic variation detection, etc.) that in turn are managed and monitored by a workflow system (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004274#box001" target="_blank">Box 1</a>). Stand-alone GMS tools, not part of automated pipelines, are available through a common tool tree. Most components of the system can be accessed through an Ubuntu Linux command-line interface or Ruby-on-Rails web interface.</p