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一个基于网络的交互式框架,用于协助在全外显子组测序研究中对疾病候选基因进行优先级排序。

A web-based interactive framework to assist in the prioritization of disease candidate genes in whole-exome sequencing studies.

机构信息

Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia 46012, Spain Bioinformatics of Rare Diseases (BIER), CIBER de Enfermedades Raras (CIBERER), Valencia 46010, Spain.

Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia 46012, Spain.

出版信息

Nucleic Acids Res. 2014 Jul;42(Web Server issue):W88-93. doi: 10.1093/nar/gku407. Epub 2014 May 6.

Abstract

Whole-exome sequencing has become a fundamental tool for the discovery of disease-related genes of familial diseases and the identification of somatic driver variants in cancer. However, finding the causal mutation among the enormous background of individual variability in a small number of samples is still a big challenge. Here we describe a web-based tool, BiERapp, which efficiently helps in the identification of causative variants in family and sporadic genetic diseases. The program reads lists of predicted variants (nucleotide substitutions and indels) in affected individuals or tumor samples and controls. In family studies, different modes of inheritance can easily be defined to filter out variants that do not segregate with the disease along the family. Moreover, BiERapp integrates additional information such as allelic frequencies in the general population and the most popular damaging scores to further narrow down the number of putative variants in successive filtering steps. BiERapp provides an interactive and user-friendly interface that implements the filtering strategy used in the context of a large-scale genomic project carried out by the Spanish Network for Research in Rare Diseases (CIBERER) in which more than 800 exomes have been analyzed. BiERapp is freely available at: http://bierapp.babelomics.org/

摘要

全外显子组测序已成为发现家族性疾病相关基因和鉴定癌症体细胞驱动变异的基本工具。然而,在少数样本中大量个体变异的背景下找到致病突变仍然是一个巨大的挑战。本文描述了一个基于网络的工具 BiERapp,它可以有效地帮助识别家族性和散发性遗传疾病中的致病变异。该程序读取受影响个体或肿瘤样本和对照中预测变异(核苷酸取代和插入/缺失)的列表。在家族研究中,可以轻松定义不同的遗传模式,以过滤掉与疾病不沿家族遗传的变异。此外,BiERapp 集成了其他信息,如一般人群中的等位基因频率和最流行的破坏性评分,以便在随后的过滤步骤中进一步缩小潜在变异的数量。BiERapp 提供了一个交互式和用户友好的界面,实现了在西班牙罕见疾病研究网络(CIBERER)进行的大规模基因组项目背景下使用的过滤策略,该项目已经分析了 800 多个外显子组。BiERapp 可免费获取:http://bierapp.babelomics.org/

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