CAREER: Predicting and Mining Phenome-genome Association across Species

职业：预测和挖掘跨物种的表型组-基因组关联

• 批准号: 1149697
• 负责人: Rui Kuang
• 金额: $ 44.65万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1149697&HistoricalAwards=false
• 关键词: CAREER; Predicting; Mining; Phenome; genome

Understanding how genetic material determines the observable characteristics (phenotypes) of an organism relies on knowledge of phenotype-gene relations. From high-throughput genomic data, it is now possible to apply computational approaches to identify the associations between individual phenotypes and genes. Since the number of determined phenotype-gene associations is still very limited, no computational framework has been developed to perform large-scale cross-species analysis of the association between the whole collection of phenotypes (phenome) and genes (genome). The objective of this CAREER proposal is to develop new computational methods for predicting and understanding phenome-genome association across multiple species. With the prediction tools, a biologist or disease researcher could more reliably prioritize genes to test their association with phenotypes in the laboratory. The availability of the tools will greatly expedite the process of discovering new associations, especially for studying rare phenotypes. The developed methods will be applied to study phenome-genome associations for the analysis of several cancer tumor phenotypes and the growth phenotypes of Arabidopsis thaliana in collaboration with oncologists and biologists. The study of the plant growth phenotypes aims to identify genes that govern seedling de-etiolation and seed development. The collaboration should generate a potential increase in seed yield and concomitant increases in the contents of proteins and oil per seed for the crop plants. The study of the ovarian cancer and lung cancer tumor phenotypes will help reveal the driving pathways of chemoresistance, and result in useful prediction tools and drug targets for the treatment of ovarian cancer and lung cancer. The research in this proposal will deliver a web portal called Phenome-Genome Explorer with a collection of computational tools that utilize known phenotype-gene associations to predict new associations, find conserved associations and conserved modules of associations across species. The PI has a long-term commitment to teach a summer class in the BioSMART program for Minnesota high school students. He will also create a new course Computational Phenomics and Genomics to support two graduate programs for training students in biomedical/health informatics with knowledge in genomics and computer science. The education plan in the proposal aims to promote high school students' early interest in careers in computing science and biomedical/health informatics and integrate the research development on phenome-genome analysis into training graduate students to meet the need of workforce in the growing biomedical and health informatics industry, with a focus on recruiting students in minority and under-represented groups.This proposal targets a systematic computational study of phenome-genome association in a network context. The comparative analysis across multiple species will expand the current scope of understanding evolutionary relation between phenome and genome. The proposed research work focuses on 1) How to discover patterns and predict new associations by learning with the sparse connections in a large heterogeneous network composed of phenotype network, gene network and their association network; and 2) How to compare multiple heterogeneous networks to find conserved patterns and modules, and to infer new associations. Both scenarios require development of scalable new algorithms to deal with multiple large heterogeneous networks.

理解遗传物质如何决定生物体的可观察特征（表型）依赖于表型-基因关系的知识。从高通量基因组数据，现在可以应用计算方法来识别个体表型和基因之间的关联。由于确定的表型基因协会的数量仍然非常有限，没有计算框架已经开发出执行大规模的跨物种分析的整个集合的表型（表型组）和基因（基因组）之间的关联。这个CAREER提案的目标是开发新的计算方法来预测和理解跨多个物种的表型-基因组关联。有了预测工具，生物学家或疾病研究人员可以更可靠地优先考虑基因，以在实验室中测试它们与表型的关联。这些工具的可用性将大大加快发现新关联的过程，特别是对于研究罕见表型。所开发的方法将应用于研究表型-基因组关联，用于与肿瘤学家和生物学家合作分析几种癌症肿瘤表型和拟南芥生长表型。植物生长表型的研究旨在鉴定控制幼苗去黄化和种子发育的基因。这种合作将产生种子产量的潜在增加，并伴随着作物植物每粒种子的蛋白质和油含量的增加。对卵巢癌和肺癌肿瘤表型的研究将有助于揭示化疗耐药的驱动途径，并为卵巢癌和肺癌的治疗提供有用的预测工具和药物靶点。 该提案中的研究将提供一个名为Phenome-Genome Explorer的门户网站，其中包含一系列计算工具，这些工具利用已知的表型-基因关联来预测新的关联，找到保守的关联和跨物种关联的保守模块。PI长期致力于为明尼苏达州高中生教授BioSMART计划的暑期课程。他还将创建一个新的课程计算表型组学和基因组学，以支持两个研究生课程，培养学生在生物医学/健康信息学与基因组学和计算机科学的知识。 建议中的教育计划旨在促进高中生对计算机科学和生物医学/健康信息学职业的早期兴趣，并将表型-基因组分析的研究进展纳入培养研究生，以满足日益增长的生物医学和健康信息学行业的劳动力需求，重点是招收少数民族和代表性不足的群体的学生。这项建议的目标是在网络环境中对表型-基因组关联进行系统的计算研究。跨物种的比较分析将扩大目前对表型组与基因组之间进化关系的理解范围。本文的研究重点是：1）如何在由表型网络、基因网络及其关联网络组成的大型异构网络中，通过稀疏连接学习发现模式并预测新的关联; 2）如何比较多个异构网络，发现保守模式和模块，并推断新的关联。这两种情况都需要开发可扩展的新算法来处理多个大型异构网络。