Evolutionary Bioinformatics of Human Mutations

人类突变的进化生物信息学

• 批准号: 8323957
• 负责人: Sudhir Kumar
• 金额: $ 35.87万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-30 至 2014-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8323957
• 关键词: Acute; Address; Adoption; Affect; Anatomy; Bioinformatics; Biomedical Research; Communities; Complex; Computer Simulation; Computer software; DNA Sequence; Data; Data Set; Databases; Decision Support Systems; Diagnosis; Diagnostic; Disease; Failure; Frequencies; Functional RNA; Gene Mutation; Genetic Polymorphism; Genome; Genomics; Human; Human Genome; Individual; Investigation; Linux; Measures; Medicine; Methods; Molecular; Mutate; Mutation; Mutation Analysis; Nature; Operating System; Pattern; Plug-in; Point Mutation; Population; Population Analysis; Positioning Attribute; Property; Publications; Recording of previous events; Research; Research Infrastructure; Research Personnel; Running; Scientist; Sequence Alignment; Sequence Analysis; Software Design; Software Tools; Solutions; Somatic Cell; Source Code; Techniques; Time; Training and Education; Translating; Variant; Visual; Work; base; biological research; computerized tools; cost; direct application; experience; fundamental research; innovation; knowledge base; mutant; novel; population survey; programs; research and development; software development; sound; success; tool; user-friendly

DESCRIPTION (provided by applicant): An enduring impediment in translating genomic advances into biomedical solutions has been the lack of tools and techniques that enable biologists to [a] efficiently leverage the multitude of publically- available genome variation data in their research endeavors, and [b] effectively harness the long-term (inter-specific) evolutionary histories of mutant positions in diagnosing functional effects of novel mutations. This need has become more acute with the discovery of unprecedented numbers of novel mutations in personal genomes and population surveys. Therefore, we propose an integrated research and development project to address this need. First, we plan to develop unique, user-friendly, and robust software to investigate human mutations in the context of Long-Term Evolutionary (LTE) patterns on a genomic scale; LTE patterns are revealed by inter-specific comparisons at a position, and they provide sound baseline hypotheses for analyzing the nature of mutations and frequencies of contemporary variations. The proposed myPEG (Population Evolutionary Genomics) software will contain tools for automated data assembly and integration from primary genome alignment browsers and mutation databases (e.g., UCSC, 1000Genomes, dbSNP). myPEG will enable users to conduct integrative analysis across taxonomic scales via its cross-platform WebTop display and analysis framework that will seamlessly integrate species and population sequence alignments and analyses in traditional and novel ways. myPEG's approach to software design and development will be biologist- centric in which we emulate, rather than reinvent, biologists' favorite work practices. These software developments will be informed by the proposed fundamental research to develop direct applications of macro-evolutionary patterns to the diagnosis of mutations associated with disease (e.g., Mendelian, complex, and somatic-cell mutations), and the successes of their computational predictions using in silico tools. The proposed investigations will yield similarities and differences in evolutionary anatomies of disease-associated and other mutations (including population SNPs) as well as those of the success rates of all major in silico tools currently used for diagnosing functional effects of novel mutations. These discoveries will form the basis for developing a decision support system to choose the best in silico method for the type of mutation and purpose (type of disease), such that the Reliability of in silico Inference (RoI) is the highest. myPEG will contain this decision support system, along with facilities for prototyping and conducting high-throughput iterative analysis of large numbers of mutations. myPEG will run on all major platforms (Windows, Linux, and MacOS), will be useable as a plug-in into analysis pipelines natively in these operating systems, and will be available at no cost (including the source code) to all users, including those in research, education, and training.

描述（由申请人提供）： 将基因组进展转化为生物医学解决方案的一个持久障碍是缺乏工具和技术，这些工具和技术使得生物学家能够[a]在他们的研究努力中有效地利用大量可生物学获得的基因组变异数据，并且[B]有效地利用突变位置的长期（种间）进化历史来诊断新突变的功能效应。随着在个人基因组和人口调查中发现前所未有数量的新突变，这种需求变得更加迫切。因此，我们提出了一个综合研究和开发项目，以满足这一需求。首先，我们计划开发独特的，用户友好的，强大的软件来研究人类基因组规模的长期进化（LTE）模式的背景下突变; LTE模式是通过在一个位置的种间比较揭示的，它们提供了合理的基线假设，用于分析突变的性质和当代变异的频率。拟议的myPEG（群体进化基因组学）软件将包含用于自动化数据组装和整合的工具，这些工具来自主要基因组比对浏览器和突变数据库（例如，UCSC，1000Genomes，dbSNP）。myPEG将使用户能够通过其跨平台的WebTop显示和分析框架进行跨分类尺度的综合分析，该框架将以传统和新颖的方式无缝集成物种和种群序列比对和分析。myPEG的软件设计和开发方法将以生物学家为中心，我们模仿而不是重新发明生物学家最喜欢的工作实践。这些软件开发将由拟议的基础研究提供信息，以开发宏观进化模式在诊断与疾病相关的突变中的直接应用（例如，孟德尔的，复杂的，和体细胞突变），以及他们使用计算机工具的计算预测的成功。拟议的研究将产生疾病相关突变和其他突变（包括群体SNP）的进化解剖学的相似性和差异，以及目前用于诊断新突变功能效应的所有主要计算机工具的成功率。这些发现将成为开发决策支持系统的基础，以选择针对突变类型和目的（疾病类型）的最佳计算机模拟方法，从而使计算机模拟推断（RoI）的可靠性最高。myPEG将包含这个决策支持系统，沿着用于原型设计和对大量突变进行高通量迭代分析的设施。myPEG将在所有主要平台（Windows、Linux和MacOS）上运行，可作为这些操作系统中分析管道的本地插件使用，并将免费（包括源代码）提供给所有用户，包括研究、教育和培训用户。