IIBR Informatics: Innovative Software and Databases to Leverage RNA Polymerase as a Phylogenetic Marker in Metagenomic Data

提高我们对地球上生物多样性的范围和性质的了解是生物学中的一个根本挑战。地球上最大的、没有特征的系统发育多样性储存库存在于细菌和古生菌的神秘谱系中，这些细菌和古生菌通常被称为“微生物暗物质”，不能在实验室培养，只有通过独立于培养的分子方法才能知道存在。了解这些群体的生理、进化历史和环境影响是未来研究的主要前沿，这一领域的研究已经在生物地球化学、进化生物学和细胞生理学方面取得了重要发现。高通量DNA测序的进步以及元基因组学领域的发展提供了前所未有的数量的元基因组数据，现在可以挖掘这些数据来发现新的微生物谱系，但需要新的计算方法来利用这些“大数据”来实现这些生物学见解。该项目专注于开发计算工具，通过分析RNA聚合酶(RNAP)序列来评估元基因组数据中的微生物多样性。RNAP是一种高分辨率的系统发育标记基因，它为细菌和古生物提供了准确的分类分配，并可用于识别和分类生物圈中神秘的微生物谱系。这项工作还将涉及对研究生和本科生进行计算生物学和生物信息学方面的培训，以及将这些生物信息学方法纳入大学课程。提高我们对地球上生物多样性的范围和性质的了解是生物学的一个基本目标，目前地球上最大的未被表征的系统发育多样性储存库由神秘的细菌和古生菌谱系组成，这些细菌和古生菌通常被称为“微生物暗物质”。高通量DNA测序和元基因组学的最新进展提供了前所未有的数据量，可用于评估这些谱系在环境中的多样性和分布，但由于序列组装产生的复杂性，依赖小亚单位rRNA基因的传统方法与这些数据不兼容。鉴于现有的大量元基因组数据，迫切需要开发准确和标准化的生物信息学工作流程，以分析与当前元基因组数据集兼容的微生物多样性。这项工作的目标是将研究和教学活动结合起来，重点开发用于元基因组数据集中RNAP序列的预测和系统发育表征的软件。这将通过1)开发和验证开源软件来解决，该软件使用定制的隐马尔可夫模型、基因邻域评估和机器学习算法从元基因组数据中准确地预测RNAP基因，以及2)编辑RNAP数据库以快速和准确地对新的RNAP序列进行系统发生分类。RNAP是用于元基因组数据的最好的可用的系统发育标记，因此这项工作将为评估微生物的系统发育多样性和进化关系提供一个关键的框架。拟议工作的影响将通过在GitHub和弗吉尼亚理工学院图书馆上开放获取软件和数据产品来最大化。该软件的实施将被整合到弗吉尼亚理工大学的研究生和本科课程中，以增加学生的参与度和参与度。此外，这项研究将由博士生和本科生进行，他们中的一些人是代表不足的少数民族。这些学生的专业发展将通过私人投资机构的密切指导以及参加科学会议和职业发展讲习班来优先考虑。这项研究的结果将在艾尔沃德实验室网站(www.aylwardLab.com)和GitHub网站上提供(https://github.com/faylward)This奖项反映了美国国家科学基金会的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Advancing our knowledge of the extent and nature of biodiversity on Earth is a fundamental challenge in Biology. The largest uncharacterized reservoir of phylogenetic diversity on the planet resides in cryptic lineages of Bacteria and Archaea, often referred to as "microbial dark matter", that cannot be cultivated in the laboratory and are only known to exist from cultivation-independent molecular methods. Understanding the physiology, evolutionary history, and environmental impact of these groups is a major frontier for future research, and studies in this area have already led to important discoveries in biogeochemistry, evolutionary biology, and cellular physiology. Advances in high-throughput DNA sequencing together with developments in the field of metagenomics have provided an unprecedented amount of metagenomic data that can now be mined to discover novel microbial lineages, but new computational methods are required for leveraging this "big data" to achieve these biological insights. This project focuses on developing computational tools to assess microbial diversity in metagenomic data through analysis of sequences of RNA polymerase (RNAP). RNAP is a high-resolution phylogenetic marker gene that provides accurate taxonomic assignments for Bacteria and Archaea and can be leveraged to identify and classify cryptic microbial lineages in the biosphere. This work will also involve the training of graduate and undergraduate students in computational biology and bioinformatics as well as the integration of these bioinformatic approaches into university curricula. Advancing our knowledge of the extent and nature of biodiversity on Earth is a fundamental goal in biology, and currently the largest uncharacterized reservoir of phylogenetic diversity on the planet consists of cryptic lineages of Bacteria and Archaea that are often referred to as "microbial dark matter". Recent advances in high-throughput DNA sequencing and metagenomics provide an unprecedented amount of "'-omic" data that can be used to assess the diversity and distributions of these lineages in the environment, but traditional approaches relying on small subunit rRNA genes are not compatible with these data due to complications that arise from sequence assembly. Given the massive quantity of metagenomic data currently available, there is an urgent need for the development of accurate and standardized bioinformatic workflows for the analysis of microbial diversity that are compatible with current metagenomic datasets. The goal of this work is to integrate research and teaching activities focused on developing software for the prediction and phylogenetic characterization of RNAP sequences in metagenomic datasets. This will be addressed this by 1) development and validation of open source software that uses custom Hidden Markov Models, gene neighborhood assessment, and machine learning algorithms to accurately predict RNAP genes from metagenomic data, and 2) compilation of an RNAP database for the fast and accurate phylogenetic classification of novel RNAP sequences. RNAP is the best available phylogenetic marker to use in metagenomic data, and this work will therefore provide a critical framework for the assessment of microbial phylogenetic diversity and evolutionary relationships. The impact of the proposed work will be maximized through the open-access distribution of software and data products on GitHub and the Virginia Tech library. Implementation of the software will be integrated into graduate and undergraduate courses at Virginia Tech to increase student involvement and participation. Additionally, this research will be conducted by PhD and undergraduate students, several of whom are underrepresented minorities. The professional development of these students will be prioritized through close mentorship by the PIs and attendance of scientific conferences and career-building workshops. Results of this research will be provided on the Aylward Lab website (www.aylwardlab.com) and GitHub site (https://github.com/faylward)This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.