ABI Innovation: Collaborative Research: Computational framework for inference of metabolic pathway activity from RNA-seq data

ABI Innovation：协作研究：从 RNA-seq 数据推断代谢途径活性的计算框架

• 批准号: 1564559
• 负责人: Frank Stewart
• 金额: $ 29.98万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1564559&HistoricalAwards=false
• 关键词: ABI; Innovation; Collaborative; Research; Computational

Microbial communities, or microbiomes, are an essential part of life on Earth. Microbiomes in the natural environment, including those associated with animals and plants, have thousands of interacting microbial species. Microbial communities influence key aspects of host health and behavior. They drive basic biochemical processes in their hosts, such as nutrient processing in the guts and sequestration of carbon in the Earth's oceans. The study of microbiomes has been recently revolutionized by the use of advanced sequencing technologies. However, large-scale sequencing initiatives, such as the Human Microbiome Project and the Earth Microbiome Project, are generating Petabytes (10 to the power of 15 bytes) of data, more than existing analysis tools can handle. The goal of this project is to develop transformative computational methods and implement software tools that enable the analysis of these very large datasets. Specifically, these tools will provide improved methods to organize community gene expression data (metatranscriptomes) into metabolic pathways, which informs predictions of how biochemical processes transform matter and energy. To maximize its impact, the developed software tools will be made available to the research community as stand-alone open source packages and deployed on common cloud computing environments. The project will provide opportunities for mentoring undergraduate and graduate students at Georgia State University, University of Connecticut, and Georgia Tech and promote participation of women and underrepresented groups in bioinformatics research and empirical analysis of community-level sequence (DNA/RNA) datasets. Selected aspects of the proposed research will be incorporated in courses at the three universities, and form the basis of innovative curriculum and educational materials, including the creation of mobile applications. This project brings together an interdisciplinary team of computer scientists and environmental microbiologists to develop and implement computational tools that enable de novo analysis of large multi-sample microbiome sequencing datasets, addressing current challenges in metatranscriptome assembly and inference of metabolic pathway activity. Specific aims of the project include: (i) developing highly scalable algorithms for de novo assembly and quantification from multiple metatranscriptomic samples, (ii) developing highly accurate algorithms for estimation of metabolic pathway activity level and differential activity testing, (iii) developing and validating prototype implementations of developed methods. A distinguishing feature of the developed methods will be their ability to jointly analyze multiple related metatranscriptomic samples. This joint assembly and quantification paradigm is likely to find applications beyond microbiome research, e.g., in the emerging area of single cell genomics. The results of the project, including software packages, research publications, and educational materials, will be made available at http://alan.cs.gsu.edu/NGS/?q=software and http://dna.engr.uconn.edu/?page_id=719

微生物群落或微生物组是地球生命的重要组成部分。自然环境中的微生物组，包括与动物和植物相关的微生物组，有数千种相互作用的微生物物种。微生物群落影响宿主健康和行为的关键方面。它们驱动宿主的基本生化过程，例如肠道中的营养处理和地球海洋中的碳封存。最近，先进测序技术的使用彻底改变了微生物组的研究。然而，人类微生物组计划和地球微生物组计划等大规模测序计划正在生成 PB（10 的 15 字节次方）数据，超出了现有分析工具的处理能力。该项目的目标是开发变革性的计算方法并实现能够分析这些非常大的数据集的软件工具。具体来说，这些工具将提供改进的方法，将群落基因表达数据（元转录组）组织到代谢途径中，从而预测生化过程如何转化物质和能量。为了最大限度地发挥其影响力，开发的软件工具将作为独立的开源包提供给研究社区，并部署在常见的云计算环境中。该项目将为佐治亚州立大学、康涅狄格大学和佐治亚理工学院的本科生和研究生提供指导机会，并促进女性和代表性不足的群体参与生物信息学研究和社区级序列（DNA/RNA）数据集的实证分析。拟议研究的选定方面将纳入三所大学的课程中，并构成创新课程和教育材料的基础，包括创建移动应用程序。该项目汇集了计算机科学家和环境微生物学家的跨学科团队，开发和实施计算工具，能够对大型多样本微生物组测序数据集进行从头分析，解决当前宏转录组组装和代谢途径活性推断方面的挑战。该项目的具体目标包括：(i) 开发高度可扩展的算法，用于从多个宏转录组样本中进行从头组装和定量，(ii) 开发高度准确的算法，用于估计代谢途径活性水平和差异活性测试，(iii) 开发和验证开发方法的原型实现。所开发方法的一个显着特征是它们能够联合分析多个相关的宏转录组样本。这种联合组装和定量范式可能会在微生物组研究之外找到应用，例如在单细胞基因组学的新兴领域。该项目的成果，包括软件包、研究出版物和教育材料，将在 http://alan.cs.gsu.edu/NGS/?q=software 和 http://dna.engr.uconn 上提供.edu/?page_id=719