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

微生物群落或微生物组是地球上生命的重要组成部分。自然环境中的微生物群，包括与动物和植物有关的微生物群，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。它们驱动宿主体内的基本生化过程，比如肠道中的营养物质处理和地球海洋中的碳封存。微生物组的研究最近由于使用先进的测序技术而发生了革命性的变化。然而，大规模的测序计划，如人类微生物组计划和地球微生物组计划，正在产生pb（10的15次方字节）的数据，超过现有的分析工具所能处理的。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来组织社区基因表达数据（亚转录组）进入代谢途径，这有助于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，开发的软件工具将作为独立的开源软件包提供给研究社区，并部署在通用的云计算环境中。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，并促进妇女和代表性不足的群体参与生物信息学研究和社区水平序列（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，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。该项目汇集了一个由计算机科学家和环境微生物学家组成的跨学科团队，开发和实施计算工具，使大型多样本微生物组测序数据集能够从头分析，解决当前在超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，用于从多个亚转录组样本中进行从头组装和定量，（ii）开发高度精确的算法，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。开发方法的一个显著特征将是它们联合分析多个相关的亚转录组样本的能力。这种联合组装和量化范例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.edu/?page_id=719上提供

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