Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.
Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.
Bioinformatics. 2020 Jun 1;36(12):3938-3940. doi: 10.1093/bioinformatics/btaa228.
Fully realizing the promise of personalized medicine will require rapid and accurate classification of pathogenic human variation. Multiplexed assays of variant effect (MAVEs) can experimentally test nearly all possible variants in selected gene targets. Planning a MAVE study involves identifying target genes with clinical impact, and identifying scalable functional assays for that target. Here, we describe MaveQuest, a web-based resource enabling systematic variant effect mapping studies by identifying potential functional assays, disease phenotypes and clinical relevance for nearly all human protein-coding genes.
MaveQuest service: https://mavequest.varianteffect.org/. MaveQuest source code: https://github.com/kvnkuang/mavequest-front-end/.
Supplementary data are available at Bioinformatics online.
要充分实现个性化医学的承诺，就需要快速准确地对致病性人类变异进行分类。变异效应的多重分析（MAVE）可以在选定的基因靶标中实验性地测试几乎所有可能的变异。规划 MAVE 研究包括确定具有临床影响的靶基因，并为该靶基因确定可扩展的功能分析。在这里，我们描述了 MaveQuest，这是一个基于网络的资源，通过识别潜在的功能分析、疾病表型和几乎所有人类蛋白编码基因的临床相关性，使系统的变异效应映射研究成为可能。
MaveQuest 服务：https://mavequest.varianteffect.org/。MaveQuest 源代码：https://github.com/kvnkuang/mavequest-front-end/。
补充数据可在“Bioinformatics”在线获取。
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