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{"title":"Using MARRVEL v1.2 for Bioinformatics Analysis of Human Genes and Variant Pathogenicity","authors":"Julia Wang, Dongxue Mao, Fatima Fazal, Seon-Young Kim, Shinya Yamamoto, Hugo Bellen, Zhandong Liu","doi":"10.1002/cpbi.85","DOIUrl":null,"url":null,"abstract":"<p>One of the greatest challenges in the bioinformatic analysis of human sequencing data is identifying which variants are pathogenic. Numerous databases and tools have been generated to address this difficulty. However, these many useful data and tools are broadly dispersed, requiring users to search for their variants of interest through human genetic databases, variant function prediction tools, and model organism databases. To solve this problem, we collected data and observed workflows of human geneticists, clinicians, and model organism researchers to carefully select and display valuable information that facilitates the evaluation of whether a variant is likely to be pathogenic. This program, Model organism Aggregated Resources for Rare Variant ExpLoration (MARRVEL) v1.2, allows users to collect relevant data from 27 public sources for further efficient bioinformatic analysis of the pathogenicity of human variants. © 2019 by John Wiley & Sons, Inc.</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"67 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.85","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current protocols in bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cpbi.85","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
引用次数: 10
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Abstract
One of the greatest challenges in the bioinformatic analysis of human sequencing data is identifying which variants are pathogenic. Numerous databases and tools have been generated to address this difficulty. However, these many useful data and tools are broadly dispersed, requiring users to search for their variants of interest through human genetic databases, variant function prediction tools, and model organism databases. To solve this problem, we collected data and observed workflows of human geneticists, clinicians, and model organism researchers to carefully select and display valuable information that facilitates the evaluation of whether a variant is likely to be pathogenic. This program, Model organism Aggregated Resources for Rare Variant ExpLoration (MARRVEL) v1.2, allows users to collect relevant data from 27 public sources for further efficient bioinformatic analysis of the pathogenicity of human variants. © 2019 by John Wiley & Sons, Inc.
利用marvel v1.2进行人类基因和变异致病性的生物信息学分析
在人类测序数据的生物信息学分析中,最大的挑战之一是确定哪些变异是致病的。已经产生了许多数据库和工具来解决这一困难。然而,这些有用的数据和工具是广泛分散的,需要用户通过人类遗传数据库、变异功能预测工具和模式生物数据库来搜索他们感兴趣的变体。为了解决这个问题,我们收集了数据,并观察了人类遗传学家、临床医生和模式生物研究人员的工作流程,以仔细选择和显示有价值的信息,从而有助于评估一种变异是否可能具有致病性。模型生物罕见变异探索聚合资源(marvel) v1.2程序允许用户从27个公共来源收集相关数据,以进一步有效地对人类变异的致病性进行生物信息学分析。©2019 by John Wiley &儿子,Inc。
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