...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>BMC Genomics >Identifying Mendelian disease genes with the Variant Effect Scoring Tool
【24h】

Identifying Mendelian disease genes with the Variant Effect Scoring Tool

机译：使用变异效应评分工具鉴定孟德尔疾病基因

获取原文
   

获取外文期刊封面封底 >>

       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

BackgroundWhole exome sequencing studies identify hundreds to thousands of rare protein coding variants of ambiguous significance for human health. Computational tools are needed to accelerate the identification of specific variants and genes that contribute to human disease.ResultsWe have developed the Variant Effect Scoring Tool (VEST), a supervised machine learning-based classifier, to prioritize rare missense variants with likely involvement in human disease. The VEST classifier training set comprised ~ 45,000 disease mutations from the latest Human Gene Mutation Database release and another ~45,000 high frequency (allele frequency >1%) putatively neutral missense variants from the Exome Sequencing Project. VEST outperforms some of the most popular methods for prioritizing missense variants in carefully designed holdout benchmarking experiments (VEST ROC AUC = 0.91, PolyPhen2 ROC AUC = 0.86, SIFT4.0 ROC AUC = 0.84). VEST estimates variant score p-values against a null distribution of VEST scores for neutral variants not included in the VEST training set. These p-values can be aggregated at the gene level across multiple disease exomes to rank genes for probable disease involvement. We tested the ability of an aggregate VEST gene score to identify candidate Mendelian disease genes, based on whole-exome sequencing of a small number of disease cases. We used whole-exome data for two Mendelian disorders for which the causal gene is known. Considering only genes that contained variants in all cases, the VEST gene score ranked dihydroorotate dehydrogenase (DHODH) number 2 of 2253 genes in four cases of Miller syndrome, and myosin-3 (MYH3) number 2 of 2313 genes in three cases of Freeman Sheldon syndrome.ConclusionsOur results demonstrate the potential power gain of aggregating bioinformatics variant scores into gene-level scores and the general utility of bioinformatics in assisting the search for disease genes in large-scale exome sequencing studies. VEST is available as a stand-alone software package at http://wiki.chasmsoftware.org and is hosted by the CRAVAT web server at http://www.cravat.us
机译：背景整个外显子组测序研究确定了数百至数千种对人类健康意义不明的稀有蛋白质编码变体。结果我们开发了一种基于机器学习的监督分类器-Variant Effect Scoring Tool（VEST），以优先考虑可能与人类疾病有关的稀有错义变异体，从而需要使用计算工具来加速对导致人类疾病的特定变体和基因的鉴定。 。 VEST分类器训练集包括来自最新人类基因突变数据库版本的〜45,000种疾病突变和外显子组测序项目的另一种〜45,000种高频（等位基因频率> 1％）推定的中性错义变体。在精心设计的保持基准测试中，VEST的性能优于某些最受欢迎的方法，用于对错义变体进行优先级排序（VEST ROC AUC = 0.91，PolyPhen2 ROC AUC = 0.86，SIFT4.0 ROC AUC = 0.84）。对于未包括在VEST训练集中的中性变体，VEST针对VEST分数的零分布来估计变体得分p值。可以在多个疾病外显子组的基因水平上汇总这些p值，对可能的疾病参与基因进行排名。我们基于少数疾病病例的全外显子组测序，测试了总VEST基因得分识别候选孟德尔疾病基因的能力。我们使用因果基因已知的两种孟德尔疾病的全外显子数据。仅考虑所有情况下均包含变异的基因，VEST基因评分对4例Miller综合征的2253个基因的二氢乳清酸脱氢酶（DHODH）排名，对3例Freeman Sheldon的2313个基因的肌球蛋白3（MYH3）排名2结论我们的结果证明了将生物信息学变异分数汇总为基因水平分数的潜在功效，以及生物信息学在协助大规模外显子组测序研究中寻找疾病基因方面的通用性。 VEST可作为独立软件包在http://wiki.chasmsoftware.org上获得，并由CRAVAT Web服务器在http://www.cravat.us上托管。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号