New Methods and Enhanced Software for Predicting Functional SNPs

描述(由申请人提供)：单核苷酸多态(SNPs)构成了人类个体之间的大部分遗传差异。导致蛋白质序列中氨基酸替换的非同义编码SNPs(NsSNPs)与影响转录和剪接的c/S调节SNPs一起被认为是复杂疾病易感性、药物反应和其他表型个体差异的主要遗传成分。基于对蛋白质多序列比对、三维结构和序列注释的分析，可以通过计算预测来促进功能nsSNPs的识别。这一分析在我们实验室维护的在线工具PolyPhen中早先实现了自动化。目前，许多不同领域的研究人员使用PolyPhen来预测nsSNPs对蛋白质结构和功能的影响。然而，越来越需要更准确的计算方法来改进这样的预测，并将PolyPhen的适用性扩展到所有类别的多态。这项提议的重点是改进方法，以预测纳入PolyPhen的人类基因组中SNPs的功能效应，并将PolyPhen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：第一，我们建议通过引入新的计算策略来预测nsSNPs对蛋白质结构和功能的影响，从而提高PolyPhen的准确性(特定目标1)。方法学创新将包括开发一种多序列比对管道，以抑制因未比对而产生的错误预测。一种新的方法将消除由同源序列中的补偿性替换引起的假阴性预测。我们将使用结构优化的贝叶斯分类器，基于来自蛋白质序列和结构的多种特征来预测nsSNPs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将继续产生的广泛的比较基因组数据。我们将介绍一种基于概率进化模型预测非编码区功能SNPs的计算方法。最后，我们计划将这些开发纳入新版本的PolyPhen软件系统中，该软件系统将满足对强大的、跨平台的工具的巨大需求，该工具可以被不同的研究人员轻松地应用于人类SNPs的功能分析问题(特定目标3)。这一新版本的PolyPhen将被纳入由I2b2国家生物医学计算中心开发的临床研究图表，并与Vista可视化工具集成。

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.