Bioinformatics approaches and resources for single nucleotide polymorphism functional analysis
Bioinformatics approaches and resources for single nucleotide polymorphism functional analysis
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DOI：
10.1093/bib/6.1.44
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发表时间：
2005-03-01
影响因子：
9.5
通讯作者：
Mooney, S
中科院分区：
文献类型：
--
作者：
Mooney, S
自从人类基因组首次测序以来，许多项目正在进行中，以了解个体之间遗传变异的影响。使用计算方法预测和理解遗传变异的下游效应对于遗传学研究中的单核苷酸多态性（SNP）选择和理解疾病的分子基础变得越来越重要。根据NIH的数据，现在人类基因组中有超过400万个经过验证的SNP。已知遗传变异的数量很适合信息学方法。生物信息学家已经非常擅长从功能和结构基因组学中推导出功能推断方法。这篇综述将从结构、表达、进化和表型的角度对可用于收集和理解功能变异的工具和资源进行广泛的概述。此外，公共资源可用于SNP识别和表征进行了总结。
Since the initial sequencing of the human genome, many projects are underway to understand the effects of genetic variation between individuals. Predicting and understanding the downstream effects of genetic variation using computational methods are becoming increasingly important for single nucleotide polymorphism (SNP) selection in genetics studies and understanding the molecular basis of disease. According to the NIH, there are now more than four million validated SNPs in the human genome. The volume of known genetic variations lends itself well to an informatics approach. Bioinformaticians have become very good at functional inference methods derived from functional and structural genomics. This review will present a broad overview of the tools and resources available to collect and understand functional variation from the perspective of structure, expression, evolution and phenotype. Additionally, public resources available for SNP identification and characterisation are summarised.