New Methods and Enhanced Software for Predicting Functional SNPs

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部分遗传差异。非同义编码SNPs （nsSNPs）导致蛋白质序列中的氨基酸替换，与c/s调控SNPs一起影响转录和剪接，被认为共同解释了对复杂疾病的易感性、对药物的反应和其他表型的个体差异的大部分遗传成分。基于蛋白质多序列比对、三维结构和序列注释分析的计算预测可以促进功能性nssnp的鉴定。该分析早先在计算机程序PolyPhen中自动化，这是我们实验室维护的在线工具。目前，众多研究人员在不同领域使用PolyPhen来预测非单核苷酸多态性对蛋白质结构和功能的影响。然而，越来越需要更精确的计算方法来改进这种预测，并将PolyPhen的适用性扩展到所有类型的多态性。该建议的重点是改进方法，以预测纳入PolyPhen的人类基因组中snp的功能影响，并将PolyPhen转化为可扩展的用户友好的跨平台软件。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，从而提高PolyPhen的准确性（Specific Aim 1）。方法学上的创新将包括多序列比对管道的发展，以抑制由不一致引起的错误预测。一种新的方法可以消除同源序列中代偿性替换引起的假阴性预测。我们将基于蛋白质序列和结构的多个特征，使用结构优化的贝叶斯分类器来预测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 （Specific Aim 2）。我们计划利用已经产生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的计算方法来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，这将满足对强大的跨平台工具的重大需求，这种工具可以被不同的研究人员轻松地应用于人类snp的功能分析问题（Specific Aim 3）。这个新版本的PolyPhen将被纳入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。

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.