CAREER: Large-scale biological network integration with applications to automated function annotation

职业：大规模生物网络集成与自动化功能注释的应用

• 批准号: 1652815
• 负责人: Jian Peng
• 金额: $ 78.32万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-15 至 2022-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1652815&HistoricalAwards=false
• 关键词: CAREER; Large; scale; biological; network

This project aims to develop new methods for integrating large amounts of high resolution data arising from different types of molecules and measurement methods; the goal is to ascertain how the molecules interact over time to carry out essential biological functions. Biological functions are carried out through the myriad interactions of biological molecules, such as when proteins bind to other proteins to modulate their activity or to nucleic acids to regulate genes. DNA sequencing has led to an explosive growth in data reporting genomic sequences and their variations, and gene expression through transcript profiling; the data streams from high-throughput technologies for protein and metabolite profiling are quickly catching up. This has led to ever-expanding repositories that archive, organize and share the resulting data: by also connecting experimental conditions to the molecular profiles, researchers come to understand which molecular interactions occur and, from these, deduce many of the biological functions in living cells. Extraction of meaningful biological insights from these data sets is challenging in two ways: the data sets are very large so they require computational methods for basic handling, and each type of data differs from the others in many ways (type of noise, source of error, completeness, etc.) so they may require different statistical modeling to standardize them correctly prior to merging them. Carried out correctly, the resulting high-dimensional data sets are suitable for a variety of predictive analytics that reveal functional modules in the molecular interactomes. Results from this project will be made available through webservers and open source software. The integrated research and educational activities include interdisciplinary bioinformatics curriculum development, outreach to high school students and research opportunities for students in underrepresented groups.Comprehensively understanding various functional aspects of a gene or a protein, such as involvement in a particular biological process, physical/genetic interactions, or disease association, is critical for both biology and translational medicine research. Since exhaustively characterizing genes or proteins through biological experiments is often intractable, systems-level integration of knowledge and computational hypothesis generation have garnered great interest in the field as an effective way to guide experiments. In this project, we will develop a novel computational framework for data integration and dimensionality reduction of heterogeneous network and functional genomic data to obtain informative data representations in a low-dimensional vector space. To utilize both molecular networks and evolutionary information, we will apply the proposed dimensionality reduction techniques to effectively integrate sequence data and network data across multiple species for predicting gene function. Our approaches will enable large-scale, integrated, cross-species, genome-scale gene function annotation. Through this integration, our methods can also infer functional homology or analogy between genes, which share weak sequence similarity but relevant biological functions, from different species. Results, software and additional information will be available at http://jianpeng.cs.illinois.edu.

该项目旨在开发新的方法来整合来自不同类型分子和测量方法的大量高分辨率数据；目标是确定这些分子是如何随着时间的推移相互作用以实现基本的生物功能的。生物功能是通过生物分子的无数相互作用来实现的，例如当蛋白质与其他蛋白质结合以调节其活性或与核酸结合以调节基因。DNA测序导致了基因组序列及其变异的数据报告的爆炸式增长，以及通过转录谱分析的基因表达；来自蛋白质和代谢物分析的高通量技术的数据流正在迅速赶上。这导致了存档、组织和共享结果数据的知识库不断扩大：通过将实验条件与分子谱联系起来，研究人员开始了解发生了哪些分子相互作用，并从中推断出活细胞中的许多生物功能。从这些数据集中提取有意义的生物学见解在两个方面具有挑战性：数据集非常大，因此它们需要基本处理的计算方法，并且每种类型的数据在许多方面与其他数据不同（噪声类型，错误来源，完整性等），因此它们可能需要不同的统计建模，以便在合并它们之前正确地标准化它们。正确地执行，所得的高维数据集适用于揭示分子相互作用组中功能模块的各种预测分析。该项目的结果将通过网络服务器和开源软件提供。综合研究和教育活动包括跨学科生物信息学课程开发，向高中学生推广以及为代表性不足群体的学生提供研究机会。全面了解基因或蛋白质的各种功能方面，例如参与特定的生物过程，物理/遗传相互作用或疾病关联，对于生物学和转化医学研究都至关重要。由于通过生物学实验详尽地描述基因或蛋白质通常是棘手的，因此系统级知识集成和计算假设生成作为指导实验的有效方法在该领域引起了极大的兴趣。在这个项目中，我们将开发一个新的计算框架，用于异构网络和功能基因组数据的数据集成和降维，以获得低维向量空间中的信息数据表示。为了同时利用分子网络和进化信息，我们将应用所提出的降维技术有效地整合多物种的序列数据和网络数据，以预测基因功能。我们的方法将实现大规模、集成、跨物种、基因组尺度的基因功能注释。通过这种整合，我们的方法还可以推断不同物种基因之间的功能同源性或相似性，这些基因具有较弱的序列相似性，但具有相关的生物学功能。结果、软件和其他信息可在http://jianpeng.cs.illinois.edu上获得。

项目成果

Large-scale integration of heterogeneous pharmacogenomic data for identifying drug mechanism of action

大规模整合异质药物基因组数据以识别药物作用机制

• DOI: 10.1142/9789813235533_0005
• 发表时间: 2018
• 期刊: Proceedings of the Pacific Symposium
• 作者: Luo, Yunan;Wang, Sheng;Xiao, Jinfeng;Peng, Jian
• 通讯作者: Peng, Jian

Integrating thermodynamic and sequence contexts improves protein-RNA binding prediction

• DOI: 10.1371/journal.pcbi.1007283
• 发表时间: 2019-09
• 期刊: PLoS Computational Biology
• 影响因子: 4.3
• 作者: Yufeng Su;Yunan Luo;Xiaoming Zhao;Yang Liu;Jian Peng