Res2s2aM: Deep residual network-based model for identifying functional noncoding SNPs in trait-associated regions
Res2s2aM: Deep residual network-based model for identifying functional noncoding SNPs in trait-associated regions
复制标题
Res2s2aM：基于深度残差网络的模型，用于识别性状相关区域中的功能性非编码 SNP
DOI：
10.1142/9789813279827_0008
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Ramsey, Stephen A.
中科院分区：
文献类型：
--
作者：
Liu, Zheng;Yao, Yao;Wei, Qi;Weeder, Benjamin;Ramsey, Stephen A.
非编码单核苷酸多态(SNPs)及其靶基因是疾病遗传力和其他多基因性状的重要组成部分。识别这些SNPs和靶基因可能会揭示新的分子机制，并推动精确医学的发展。对于多基因性状，全基因组关联研究是识别性状相关区域的首选工具。然而，识别这些区域中的因果非编码SNPs是计算生物学中的一个难题。非编码SNP的DNA序列背景是一个重要的信息源，有助于区分功能和非编码SNP。我们描述了一个基于深度残差网络(ResNet)的模型Res2s2aM的使用，该模型将ANING DNA序列信息与额外的SNP注释信息融合在一起，以区分功能和非功能非编码SNP。在从全基因组SNPs与表型关联库(GRASP)数据库汇编的疾病相关SNPs的基本事实集上，Res2s2aM与仅基于序列信息的模型相比，显著提高了功能性SNPs的预测精度，并且是后GWA非编码SNP优先排序的领先工具(RegulomeDB)。
Noncoding single nucleotide polymorphisms (SNPs) and their target genes are important components of the heritability of diseases and other polygenic traits. Identifying these SNPs and target genes could potentially reveal new molecular mechanisms and advance precision medicine. For polygenic traits, genome-wide association studies (GWAS) are preferred tools for identifying trait-associated regions. However, identifying causal noncoding SNPs within such regions is a difficult problem in computational biology. The DNA sequence context of a noncoding SNP is well-established as an important source of information that is beneficial for discriminating functional from nonfunctional noncoding SNPs. We describe the use of a deep residual network (ResNet)-based model—entitled Res2s2aM—that fuses anking DNA sequence information with additional SNP annotation information to discriminate functional from nonfunctional noncoding SNPs. On a ground-truth set of disease-associated SNPs compiled from the Genome-wide Repository of Associations between SNPs and Phenotypes (GRASP) database, Res2s2aM improves the prediction accuracy of functional SNPs significantly in comparison to models based only on sequence information as well as a leading tool for post-GWAS noncoding SNP prioritization (RegulomeDB).
CERENKOV：监管非编码变量的计算阐明
DOI：
10.1145/3107411.3107414
发表时间：
2017
期刊：
Computational Biology,and Health Informatics
影响因子：
--
作者：
Yao, Yao;Liu, Zheng;Singh, Satpreet;Wei, Qi;Ramsey, Stephen A.
通讯作者：
Ramsey, Stephen A.