New Methods and Enhanced Software for Predicting Functional SNPs

预测功能性 SNP 的新方法和增强软件

• 批准号: 7825415
• 负责人: SHAMIL SUNYAEV
• 金额: $ 33.47万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-05-01 至 2012-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7825415
• 关键词: Accounting; Address; Affect; Amino Acid Sequence; Amino Acids; Biomedical Computing; Client; Clinical Research; Code; Community Services; Complex; Computer software; Data; Development; Disease; Functional RNA; Genetic; Genetic Polymorphism; Genetic Transcription; Human; Human Genome; Imagery; Individual; Laboratories; Methods; Modeling; Nucleotides; Online Systems; Open Reading Frames; Peptide Sequence Determination; Pharmacologic Substance; Phenotype; Predisposition; Protein Analysis; Protein Region; RNA Splicing; Research Personnel; Sequence Alignment; Sequence Homologs; Single Nucleotide Polymorphism; Structure; System; Variant; base; comparative genomics; computer program; graphical user interface; improved; innovation; programs; protein structure function; response; software systems; three dimensional structure; tool; user-friendly

DESCRIPTION (provided by applicant): Single nucleotide polymorphisms (SNPs) comprise the majority of the genetic differences between human individuals. Non-synonymous coding SNPs (nsSNPs), which result in amino acid replacements in protein sequences, together with c/s-regulatory SNPs affecting transcription and splicing are thought collectively to account for much of the genetic component of individual variation in susceptibility to complex diseases, response to Pharmaceuticals, and other phenotypes. Identification of functional nsSNPs can be facilitated by computational predictions based on the analysis of protein multiple sequence alignments, 3D structures and sequence annotations. This analysis was earlier automated in the computer program PolyPhen, an online tool maintained in our laboratory. Numerous researchers in diverse fields currently use PolyPhen to predict the effect of nsSNPs on protein structure and function. However, there is an increasing need for more accurate computational approaches to improve such predictions and to expand applicability of PolyPhen to all classes of polymorphisms. This proposal focuses on improving methods to predict the functional effect of SNPs in the human genome incorporated in PolyPhen and on transforming PolyPhen into scalable user-friendly cross-platform software. The proposal targets three Specific Aims: First, we propose to improve accuracy of PolyPhen by introducing new computational strategies for prediction of the effect of nsSNPs on protein structure and function (Specific Aim 1). Methodological innovations will include development of a multiple sequence alignment pipeline suppressing false predictions arising from misalignments. A new method will eliminate false-negative predictions resulting from compensatory substitutions in homologous sequences. We will use a structurally optimized Bayesian classifier to predict the functional effect of nsSNPs based on multiple features derived from protein sequence and structure. Next, we propose to extend the prediction method to non-coding SNPs (Specific Aim 2). We plan to take advantage of the extensive comparative genomic data that have been and continue to be generated. We will introduce a computational approach to predict functional SNPs in non-coding regions on the basis of probabilistic evolutionary models Finally, we plan to incorporate these developments into a new version of the PolyPhen software system, which will address significant demand for a robust, cross-platform tool that can be easily applied by diverse investigators to the problem of functional analysis of human SNPs (Specific Aim 3). This new version of PolyPhen will be incorporated into the Clinical Research Chart developed by I2b2 National Center of Biomedical Computing and integrated with VISTA visualization tools.

描述（由申请人提供）：单核苷酸多态性（SNP）构成了人类个体之间的大多数遗传差异。集体认为，蛋白质序列中导致氨基酸替代的非同义编码SNP（NSSNP），以及影响转录和剪接的C/S调节性SNP，以说明对复杂疾病的易感性变化的许多遗传成分，对复杂疾病的易感性，对药物的反应，以及其他均质类型。基于蛋白质多个序列比对，3D结构和序列注释的分析，可以通过计算预测来促进功能NSSNP的识别。该分析早些时候是在计算机程序Polyphen中自动化的，这是我们实验室中维护的在线工具。目前，各种领域的许多研究人员都使用多媒体来预测NSSNP对蛋白质结构和功能的影响。但是，越来越需要更准确的计算方法来改善此类预测并扩展多芯子对所有类别多态性的适用性。该建议着重于改进方法，以预测多媒体中纳入的人类基因组中SNP的功能效应，并将多媒体转化为可扩展的用户友好的跨平台软件。该提案针对三个具体目标：首先，我们提出通过引入新的计算策略来预测NSSNP对蛋白质结构和功能的影响（特定目标1），提高多媒体的准确性。方法论创新将包括开发多个序列一致性管道，从而抑制了由未对准而产生的错误预测。一种新方法将消除由同源序列中代码替代产生的假阴性预测。我们将使用结构优化的贝叶斯分类器来预测基于蛋白质序列和结构的多个特征NSSNP的功能效应。接下来，我们建议将预测方法扩展到非编码SNP（特定目标2）。我们计划利用已经并继续生成的广泛的比较基因组数据。我们将介绍一种计算方法，以根据概率进化模型预测非编码区域的功能SNP，最后，我们计划将这些发展纳入Polyphen软件系统的新版本，这将解决对强大的跨平台工具的重大需求，该工具可以通过多元化的研究者轻松地应用于人类SNP的功能分析（特定的SNPS 3）。这种新版本的多媒体将纳入由I2B2国家生物医学计算中心开发的临床研究图表中，并与Vista可视化工具集成在一起。