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

微生物群落，或称微生物群落，是地球上生命的重要组成部分。自然环境中的微生物群，包括那些与动植物相关的微生物群，有数千个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，例如肠道中的营养处理和地球海洋中的碳封存。最近，通过使用先进的测序技术，微生物组的研究发生了革命性的变化。然而，大规模的测序举措，如人类微生物组项目和地球微生物组项目，正在产生数拍字节(10的15字节的幂)的数据，超出了现有分析工具所能处理的范围。该项目的目标是开发变革性的计算方法，并实施能够分析这些非常大的数据集的软件工具。具体地说，这些工具将提供改进的方法，将社区基因表达数据(元转录)组织到代谢途径中，从而为预测生化过程如何转化物质和能量提供信息。为了最大限度地扩大其影响，开发的软件工具将作为独立的开源包提供给研究界，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，并促进妇女和代表性不足的群体参与生物信息学研究和社区一级序列(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，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，以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，解决代谢途径活动的元译码组组装和推断方面的当前挑战。该项目的具体目标包括：(I)开发高度可扩展的算法，用于从多个元翻译样本进行从头组装和量化，(Ii)开发用于估计代谢途径活性水平和差异活性测试的高精度算法，(Iii)开发和验证所开发方法的原型实现。所开发的方法的一个显著特点将是它们能够联合分析多个相关的元翻译样本。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，在新兴的单细胞基因组学领域。该项目的结果，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