An Integrative Computational Approach for Prioritization of Genomic Variants

<div><p>An essential step in the discovery of molecular mechanisms contributing to disease phenotypes and efficient experimental planning is the development of weighted hypotheses that estimate the functional effects of sequence variants discovered by high-throughput genomics. With the increasing specialization of the bioinformatics resources, creating analytical workflows that seamlessly integrate data and bioinformatics tools developed by multiple groups becomes inevitable. Here we present a case study of a use of the distributed analytical environment integrating four complementary specialized resources, namely the Lynx platform, VISTA RViewer, the Developmental Brain Disorders Database (DBDB), and the RaptorX server, for the identification of high-confidence candidate genes contributing to pathogenesis of spina bifida. The analysis resulted in prediction and validation of deleterious mutations in the SLC19A placental transporter in mothers of the affected children that causes narrowing of the outlet channel and therefore leads to the reduced folate permeation rate. The described approach also enabled correct identification of several genes, previously shown to contribute to pathogenesis of spina bifida, and suggestion of additional genes for experimental validations. The study demonstrates that the seamless integration of bioinformatics resources enables fast and efficient prioritization and characterization of genomic factors and molecular networks contributing to the phenotypes of interest.</p></div

Integration of services in the described analytical environment.

<p>Lynx logo © 2013–2014 Department of Genetics, University of Chicago. RViewer logo © 2010–2012 The Regents of the University of California.</p

Access to the analytical tools within the described bioinformatics environment.

<p>Access to the analytical tools within the described bioinformatics environment.</p

Docked folate conformations (blue: native, red: H27R mutant-bound folate molecules) showing the distinct change in the optimal conformation between native and the mutant.

<p>Docked folate conformations (blue: native, red: H27R mutant-bound folate molecules) showing the distinct change in the optimal conformation between native and the mutant.</p

Identified genetic variants in folate metabolism genes in spina bifida patients and unaffected parents.

<p>*The P-values in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114903#pone-0114903-t001" target="_blank">Table 1</a> are generated by 10 000 random permutations of the input data scored according to the strength of association with the phenotype using DBDB recommendations (random reassignment of the scores to network nodes and computation of the corresponding randomized scores for all candidate genes) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114903#pone.0114903-Nitsch1" target="_blank">[38]</a>.</p><p>**Family 1: affected children 2C1, 2C2; mother 2M, father 2F. Family 2: affected children 6C1, 6C2; mother 6M, father 6F.</p><p>Identified genetic variants in folate metabolism genes in spina bifida patients and unaffected parents.</p

Analytical workflow for identification of spina bifida candidate genes.

<p>Analytical workflow for identification of spina bifida candidate genes.</p

Volume and surface area of the of the cleft in the native and the mutant structures as calculated by 3V.

<p>Volume and surface area of the of the cleft in the native and the mutant structures as calculated by 3V.</p