III: Small: Topology-based approaches to integrated analysis of transcriptomic, protein interactomic and phenotypic data

III：小：基于拓扑的方法对转录组、蛋白质相互作用组和表型数据进行综合分析

• 批准号: 1218201
• 负责人: Jianhua Ruan
• 金额: $ 45.27万
• 依托单位: University of Texas at San Antonio
• 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: 2012-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218201&HistoricalAwards=false
• 关键词: III; Small; Topology; based; approaches

High-throughput technology now allows measuring the activities and interactions of tens of thousands of molecules in the cell simultaneously, opening new doors to systems-level scientific exploration in biology. Advanced computational methods are in development to analyze the huge amount of data to extract patterns that represent knowledge and to construct predictive models that have the promise to determine the characteristics of an organism, such as cancer outcomes or plant growth phenotypes. To help achieve these goals, this project aims at developing efficient and effective computational algorithms and tools to integrate heterogeneous and noisy high-throughput data, and to analyze them in ways that treat genes as inter-connected rather than independent components of the cell. Biological networks are mathematical models that describe the interactions among molecules in the cell and are critical to the modeling and understanding of complex biological systems. However, the large sizes and complexity of biological networks as well as the noisy and incomplete data pose critical challenges to network-based data analysis. To tackle these challenges, a practical and intuitive strategy is to analyze/utilize such networks on the level of functional pathways, i.e., genes/proteins involved in similar biological processes, which would significantly reduce the complexity of biological networks and improve the understanding of complex phenotypes. As the current knowledge of functional pathways is rather limited for most species, this project will develop a set of algorithms and software tools for fully automated discovery of dense subnetworks as candidate functional modules, and develop functional module-oriented algorithms for analyzing/utilizing biological networks for several real applications. First, this project will develop algorithms to improve network quality and network module discovery using information embedded in network topology. For networked (e.g. protein-protein interaction) data, topology is utilized to improve edge reliability, and subsequently module discovery, using a novel topological similarity measurement based on random walks on graphs. For non-networked (e.g., transcriptomic) data, global network topology is utilized to construct an "optimal" network that enables fully automated module discovery without any user-specified parameters. Second, this research will develop computational methods to systematically investigate the relationship between network topology and biological functions, which is expected to advance the current understanding of the organizing principles of biological networks, and facilitate prioritizing genes in disease studies. Finally, this project proposes a novel Steiner tree based algorithm for identifying potential causal genes associated with cancer phenotypes, and a novel similarity metric to compare patients based on pathway/subnetwork-level gene expression patterns, which can be easily combined with existing clustering/classification algorithms for network-based prediction of cancer outcomes. The final outputs of this project will include both bioinformatics tools for integrative data analysis and databases of biological knowledge discovered from different input datasets. These tools and resources will be made freely available on the web, which can be used by a broad range of researchers who are interested in bioinformatics algorithm development or applications. These tools and resources will be applied to study several biological processes of central interests to collaborators, who have committed to validate some of the computational predictions. These include identifying novel plant hormone response genes, predicting and characterizing DNA damage response genes, and predicting metastasis potentials for breast cancer patients, by integrating protein-protein interaction and transcriptomic data. This project will also contribute to the advancement of computing with the development of novel network link prediction and module discovery algorithms and network-constrained clustering/classification methods that are expected to have immediate applications in other domains besides biological sciences. The activities undertaken as part of this research will be incorporated into several courses and will expand the educational and research opportunities available at the University of Texas at San Antonio, a minority-serving institute where the majority of undergraduates are from under-represented minorities, and is expected to increase the geographic and ethnic diversity and encourage the participation of minority groups in bioinformatics and computational biology research.

高通量技术现在允许同时测量细胞中数万个分子的活动和相互作用，为生物学中的系统级科学探索打开了新的大门。先进的计算方法正在开发中，用于分析大量数据，以提取代表知识的模式，并构建预测模型，这些模型有望确定生物体的特征，例如癌症结果或植物生长表型。为了帮助实现这些目标，该项目旨在开发高效和有效的计算算法和工具，以整合异质和嘈杂的高通量数据，并以将基因视为细胞的相互连接而不是独立组件的方式对其进行分析。生物网络是描述细胞中分子之间相互作用的数学模型，对于复杂生物系统的建模和理解至关重要。然而，生物网络的大规模和复杂性以及噪声和不完整的数据对基于网络的数据分析提出了严峻的挑战。为了应对这些挑战，一个实用和直观的策略是在功能途径的水平上分析/利用这样的网络，即，基因/蛋白质参与类似的生物过程，这将大大降低生物网络的复杂性，提高对复杂表型的理解。由于目前对大多数物种的功能通路的知识相当有限，本项目将开发一套算法和软件工具，用于全自动地发现作为候选功能模块的密集子网络，并开发面向功能模块的算法，用于分析/利用生物网络的几个真实的应用。首先，这个项目将开发算法，以提高网络质量和网络模块发现使用嵌入在网络拓扑结构中的信息。对于网络（例如蛋白质-蛋白质相互作用）数据，拓扑结构被用来提高边缘的可靠性，并随后模块发现，使用一种新的拓扑相似性测量的基础上随机游走图。对于非联网的（例如，转录组学）数据，利用全局网络拓扑结构来构建“最佳”网络，该网络能够实现完全自动化的模块发现，而无需任何用户指定的参数。其次，本研究将开发计算方法，系统地研究网络拓扑结构和生物功能之间的关系，这有望促进目前对生物网络组织原理的理解，并有助于在疾病研究中优先考虑基因。最后，该项目提出了一种新的基于Steiner树的算法，用于识别与癌症表型相关的潜在因果基因，以及一种新的相似性度量，用于基于通路/子网络水平的基因表达模式来比较患者，该相似性度量可以很容易地与现有的聚类/分类算法相结合，用于基于网络的癌症结局预测。该项目的最终产出将包括用于综合数据分析的生物信息学工具和从不同输入数据集中发现的生物知识数据库。这些工具和资源将在网络上免费提供，可供对生物信息学算法开发或应用感兴趣的广泛研究人员使用。这些工具和资源将被应用于研究合作者感兴趣的几个生物过程，他们致力于验证一些计算预测。这些包括识别新的植物激素反应基因，预测和表征DNA损伤反应基因，并预测乳腺癌患者的转移潜力，通过整合蛋白质-蛋白质相互作用和转录组学数据。该项目还将通过开发新的网络链接预测和模块发现算法以及网络约束聚类/分类方法来促进计算的进步，这些方法预计将在生物科学之外的其他领域中立即应用。作为这项研究的一部分而开展的活动将纳入若干课程，并将扩大德克萨斯大学圣安东尼奥分校的教育和研究机会，这是一所为少数群体服务的学院，大部分本科生来自代表性不足的少数群体，预计将增加地理和种族多样性，并鼓励少数群体参与生物信息学和计算生物学research.