BALL-SNPgp-from genetic variants toward computational diagnostics

Mueller, SC; Backes, C; Gress, A; Baumgarten, N; Kalinina, OV; Moll, A; Kohlbacher, O; Meese, E; Keller, A

doi:10.1093/bioinformatics/btw084

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BALL-SNPgp-from genetic variants toward computational diagnostics

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Kohlbacher, O
Research Group Biomolecular Interactions, Max Planck Institute for Developmental Biology, Max Planck Society;

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Mueller, S., Backes, C., Gress, A., Baumgarten, N., Kalinina, O., Moll, A., et al. (2016). BALL-SNPgp-from genetic variants toward computational diagnostics. Bioinformatics, 32(12), 1888-1890. doi:10.1093/bioinformatics/btw084.

Cite as: https://hdl.handle.net/21.11116/0000-000A-6669-E

Abstract

In medical research, it is crucial to understand the functional consequences of genetic alterations, for example, non-synonymous single nucleotide variants (nsSNVs). NsSNVs are known to be causative for several human diseases. However, the genetic basis of complex disorders such as diabetes or cancer comprises multiple factors. Methods to analyze putative synergetic effects of multiple such factors, however, are limited. Here, we concentrate on nsSNVs and present BALL-SNPgp, a tool for structural and functional characterization of nsSNVs, which is aimed to improve pathogenicity assessment in computational diagnostics. Based on annotated SNV data, BALL-SNPgp creates a three-dimensional visualization of the encoded protein, collects available information from different resources concerning disease relevance and other functional annotations, performs cluster analysis, predicts putative binding pockets and provides data on known interaction sites.