Computational methods for human genomic data integration: demography, selection,

人类基因组数据整合的计算方法：人口统计学、选择、

• 批准号: 9198019
• 负责人: Adam Charles Siepel
• 金额: $ 32.98万
• 依托单位: COLD SPRING HARBOR LABORATORY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9198019
• 关键词: Address; Algorithms; Binding Sites; Biological; Biological Assay; Biology; Catalogs; Communities; Computer software; Computing Methodologies; Data; Data Analyses; Data Collection; Data Set; Demography; Detection; Development; Disease; Elements; Evolution; Genealogy; Genetic; Genetic Polymorphism; Genetic Recombination; Genome; Genomics; Graph; Human; Human Genome; Individual; Investments; Knowledge; Measures; Medicine; Methods; Natural Selections; Pattern; Phylogenetic Analysis; Population; Population Genetics; Process; Research; Resources; Running; Sample Size; Software Engineering; Software Tools; Statistical Methods; Statistical Models; Structure; Untranslated RNA; Variant; Work; base; comparative; comparative genomics; computerized tools; data integration; data to knowledge; follow-up; functional genomics; genetic signature; genome browser; genome-wide; genome-wide analysis; genomic data; human data; human genomics; improved; innovation; insight; public health relevance; transcription factor; validation studies

DESCRIPTION (provided by applicant): Project Description Recent investments in data collection have produced rich catalogs of information about genomic function, human variation, and mammalian evolution, but improved computational tools are needed to integrate and interpret these catalogs, in order to permit the acquisition of new biological knowledge and advances in medicine. Here we outline an ambitious project to develop computational methods that will integrate publicly available data catalogs to provide deep new insights about the evolution and function of sequences in the human genome. Our proposal focuses in particular on noncoding sequences, which remain the most sparsely annotated and poorly understood regions of the genome. The proposal addresses three fundamental and closely related problems: (1) inference of human demography, to provide improved "null models" for statistical genetics and reveal local signatures of gene flow, natural selection, and other phenomena; (2) detection of natural selection on interspersed noncoding sequences such as transcription factor binding sites, to provide information about their function and the evolutionary processes that have shaped them; and (3) genome-wide prediction of "functional potential" based on integrated data sets, to identify new functional elements and prioritize candidate disease loci for experimental follow-up. Our proposal includes innovative statistical modeling, new algorithms for inference, the development of freely available software tools and browser resources, and detailed analyses of the latest genomic data sets. To our knowledge this will be the most comprehensive effort yet undertaken to integrate comparative, population, and functional genomic data in addressing fundamental questions about the function and evolution of sequences in the human genome. Our software and the results of our data analysis will be freely available to the research community.

描述（由申请人提供）： 项目描述 最近对数据收集的投资已经产生了有关基因组功能、人类变异和哺乳动物进化的丰富信息目录，但需要改进的计算工具来整合和解释这些目录，以便获得新的生物学知识和进展。药品。在这里，我们概述了一个雄心勃勃的项目，旨在开发计算方法，该方法将整合公开可用的数据目录，以提供有关人类基因组序列的进化和功能的深入新见解。我们的建议特别关注非编码序列，它们仍然是基因组中注释最稀疏且了解最少的区域。该提案解决了三个基本且密切相关的问题：（1）人类人口统计学的推断，为统计遗传学提供改进的“零模型”，并揭示基因流、自然选择和其他现象的局部特征； (2) 检测分散的非编码序列（例如转录因子结合位点）的自然选择，以提供有关其功能和形成它们的进化过程的信息； （3）基于综合数据集的全基因组“功能潜力”预测，以识别新的功能元件并优先考虑候选疾病位点以进行实验随访。我们的建议包括创新的统计模型、新的推理算法、免费软件工具和浏览器资源的开发，以及最新基因组数据集的详细分析。据我们所知，这将是迄今为止为整合比较、群体和功能基因组数据以解决有关人类基因组序列的功能和进化的基本问题而进行的最全面的努力。我们的软件和数据分析结果将免费提供给研究界。