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通过综合突变分析确定基因型-表型相关性

Identifying Genotype-Phenotype Correlations via Integrative Mutation Analysis.

作者信息

Airey Edward, Portelli Stephanie, Xavier Joicymara S, Myung Yoo Chan, Silk Michael, Karmakar Malancha, Velloso João P L, Rodrigues Carlos H M, Parate Hardik H, Garg Anjali, Al-Jarf Raghad, Barr Lucy, Geraldo Juliana A, Rezende Pâmela M, Pires Douglas E V, Ascher David B

机构信息

Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia.

ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia.

出版信息

Methods Mol Biol. 2021;2190:1-32. doi: 10.1007/978-1-0716-0826-5_1.

Abstract

Mutations in protein-coding regions can lead to large biological changes and are associated with genetic conditions, including cancers and Mendelian diseases, as well as drug resistance. Although whole genome and exome sequencing help to elucidate potential genotype-phenotype correlations, there is a large gap between the identification of new variants and deciphering their molecular consequences. A comprehensive understanding of these mechanistic consequences is crucial to better understand and treat diseases in a more personalized and effective way. This is particularly relevant considering estimates that over 80% of mutations associated with a disease are incorrectly assumed to be causative. A thorough analysis of potential effects of mutations is required to correctly identify the molecular mechanisms of disease and enable the distinction between disease-causing and non-disease-causing variation within a gene. Here we present an overview of our integrative mutation analysis platform, which focuses on refining the current genotype-phenotype correlation methods by using the wealth of protein structural information.

摘要

蛋白质编码区域的突变可导致重大的生物学变化,并与包括癌症、孟德尔疾病以及耐药性在内的遗传病症相关联。尽管全基因组和外显子组测序有助于阐明潜在的基因型-表型相关性,但在新变异的识别与其分子后果的解读之间仍存在很大差距。全面了解这些机制后果对于以更个性化和有效的方式更好地理解和治疗疾病至关重要。鉴于估计超过80%与疾病相关的突变被错误地假定为致病因素,这一点尤为重要。需要对突变的潜在影响进行深入分析,以正确识别疾病的分子机制,并区分基因内致病和非致病变异。在此,我们概述了我们的综合突变分析平台,该平台专注于利用丰富的蛋白质结构信息来完善当前的基因型-表型相关方法。

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