从遗传变异到计算诊断的 BALL-SNPgp。

BALL-SNPgp-from genetic variants toward computational diagnostics.

机构信息

Chair for Clinical Bioinformatics, Saarland University, Saarbrücken 66123, Germany Department of Human Genetics, Saarland University, Homburg 66421, Germany.

Chair for Clinical Bioinformatics, Saarland University, Saarbrücken 66123, Germany.

出版信息

Bioinformatics. 2016 Jun 15;32(12):1888-90. doi: 10.1093/bioinformatics/btw084. Epub 2016 Feb 15.

DOI:10.1093/bioinformatics/btw084

PMID:27153685

Abstract

UNLABELLED

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.

AVAILABILITY AND IMPLEMENTATION

BALL-SNPgp is based on the comprehensive C ++ framework Biochemical Algorithms Library (BALL) and its visualization front-end BALLView. Our tool is available at www.ccb.uni-saarland.de/BALL-SNPgp

CONTACT

ballsnp@milaman.cs.uni-saarland.de.

摘要

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在医学研究中，了解基因改变的功能后果至关重要，例如非 synonymous 单核苷酸变异（nsSNV）。已知 nsSNV 是几种人类疾病的致病因素。然而，糖尿病或癌症等复杂疾病的遗传基础包含多个因素。然而，分析多个此类因素的潜在协同作用的方法是有限的。在这里，我们专注于 nsSNV，并介绍了 BALL-SNPgp，这是一种用于 nsSNV 结构和功能特征分析的工具，旨在提高计算诊断中的致病性评估。基于注释的 SNV 数据，BALL-SNPgp 创建了编码蛋白的三维可视化图，从不同资源中收集有关疾病相关性和其他功能注释的可用信息，执行聚类分析，预测潜在的结合口袋，并提供已知相互作用位点的数据。