Toward Identification of Functional Sequences and Variants in Noncoding DNA.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Annual Review of Biomedical Data Science Pub Date : 2023-08-10 DOI:10.1146/annurev-biodatasci-122120-110102
Remo Monti, Uwe Ohler
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引用次数: 0

Abstract

Understanding the noncoding part of the genome, which encodes gene regulation, is necessary to identify genetic mechanisms of disease and translate findings from genome-wide association studies into actionable results for treatments and personalized care. Here we provide an overview of the computational analysis of noncoding regions, starting from gene-regulatory mechanisms and their representation in data. Deep learning methods, when applied to these data, highlight important regulatory sequence elements and predict the functional effects of genetic variants. These and other algorithms are used to predict damaging sequence variants. Finally, we introduce rare-variant association tests that incorporate functional annotations and predictions in order to increase interpretability and statistical power.

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非编码DNA功能序列和变异的鉴定。
了解基因组中编码基因调控的非编码部分,对于确定疾病的遗传机制和将全基因组关联研究的发现转化为治疗和个性化护理的可操作结果是必要的。在这里,我们从基因调控机制及其在数据中的表现开始,概述了非编码区域的计算分析。当将深度学习方法应用于这些数据时,可以突出重要的调控序列元素并预测遗传变异的功能影响。这些和其他算法用于预测破坏性序列变异。最后,我们引入了包含功能注释和预测的罕见变量关联测试,以提高可解释性和统计能力。
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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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