Integrating context-specific networks to predict ataxia genes

整合上下文特定网络来预测共济失调基因

• 批准号: 8606905
• 负责人: Margit Burmeister
• 金额: $ 19.24万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8606905
• 关键词: Address; Algorithms; Animal Model; Ataxia; Basic Science; Bioinformatics; Biological Process; Brain; Candidate Disease Gene; Categories; Cerebellum; Clinical; Complement; DNA; Data; Data Set; Disease; Disease Pathway; Drug Formulations; Future; Gene Proteins; Genes; Genomics; Hand; Hereditary Disease; Human; Imagery; Inflammatory; Internet; Knowledge; Lead; Learning; Malignant Neoplasms; Methodology; Mining; Modeling; Mutation; Network-based; Phenotype; Probability; Process; Proteins; Research; Route; Specificity; Testing; Time; Tissues; Variant; Work; base; data integration; disease phenotype; functional genomics; human data; human disease; novel; protein B; prototype; public health relevance; tool

DESCRIPTION (provided by applicant): Integrating large-scale genomics data has huge potential to accelerate the identification of disease genes in human. Three major challenges lie in the current integrative approach for predicting disease genes. First, previous integrations in general limit genomic data input to one species at a time, while disease datasets are often generated in multiple model organisms. Second, public functional genomic datasets are dominated and biased by certain data types and accessible tissues, which can be addressed by expert curation of input datasets. Third, when multiple tissue-specific networks have been generated, a mathematical formulation is lacking to prioritize among these competing networks for the specific disease under consideration. This collaborative proposal aims at addressing the above challenges by exploring a prototype of bioinformatics tools to integrate multiple relevant global and tissue-specific networks across mammalian species targeting a specific disease, here ataxia. This proposal is based on our preliminary data in developing both global and cerebellum-specific networks to prioritize ataxia associated genes, and on the two PIs' complementary expertise in genomic data integration and experimental ataxia gene confirmation. We will 1) use domain-specific and multiple species data to establish global, brain, cerebellum, related tissue, and ataxia-specific networks, and develop web tools to explore these networks; and 2) develop multiple kernel learning algorithms to weigh and integrate multiple networks to predict ataxia-associated genes. Although the algorithms will be developed targeting ataxia only, we envision that this expert-driven integrative approach will be adaptable to other disease gene identification scenarios.

描述（由申请人提供）： 整合大规模基因组学数据对于加速人类疾病基因的识别具有巨大的潜力。目前预测疾病基因的综合方法面临三大挑战。首先，以前的集成一般限制基因组数据输入到一个物种的时间，而疾病数据集通常是在多个模型生物体中生成的。其次，公共功能基因组数据集被某些数据类型和可访问的组织所主导和偏见，这可以通过输入数据集的专家策展来解决。第三，当产生多个组织特异性网络时，缺乏数学公式来优先考虑这些竞争网络中的特定疾病。这项合作提案旨在通过探索生物信息学工具的原型来解决上述挑战，以整合跨哺乳动物物种的多个相关全球和组织特异性网络，针对特定疾病，这里是共济失调。该提议基于我们开发全球和小脑特异性网络以优先考虑共济失调相关基因的初步数据，以及两名PI在基因组数据整合和实验性共济失调基因确认方面的互补专业知识。我们将1）使用特定领域和多个物种的数据来建立全球，大脑，小脑，相关组织和共济失调特异性网络，并开发网络工具来探索这些网络; 2）开发多个内核学习算法来权衡和整合多个网络来预测共济失调相关基因。虽然这些算法将仅针对共济失调开发，但我们设想这种专家驱动的综合方法将适用于其他疾病基因识别方案。