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）包括人类个体之间的大部分遗传差异。非同义编码SNP（nsSNP），其导致蛋白质序列中的氨基酸替换，连同影响转录和剪接的顺式调节SNP一起被认为共同地解释了个体对复杂疾病的易感性、对药物的反应和其他表型的变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的分析，可以通过计算预测来促进功能性nsSNP的鉴定。这种分析是早期在计算机程序PolyPhen中自动化的，PolyPhen是我们实验室维护的一种在线工具。目前，不同领域的许多研究人员使用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善这种预测，并扩大PolyPhen的适用性，所有类别的多态性。该提案的重点是改进预测PolyPhen中人类基因组中SNP的功能效应的方法，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：首先，我们建议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，以提高PolyPhen的准确性（具体目标1）。方法上的创新将包括开发一个多序列比对管道，以抑制由未对齐引起的错误预测。一种新的方法将消除由同源序列中的补偿性取代引起的假阴性预测。我们将使用结构优化的贝叶斯分类器来预测nsSNP的功能效应，基于来自蛋白质序列和结构的多个特征。接下来，我们建议将预测方法扩展到非编码SNP（具体目标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。最后，我们计划将这些发展纳入一个新版本的PolyPhen软件系统，这将解决显着的需求，一个强大的，跨平台的工具，可以很容易地应用于不同的研究人员的问题，人类SNP的功能分析（具体目标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.