CAREER: Computational and Statistical Genomics of Gene Families

职业：基因家族的计算和统计基因组学

• 批准号: 0845494
• 负责人: Matthew Hahn
• 金额: $ 100.33万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845494&HistoricalAwards=false
• 关键词: CAREER; Computational; Statistical; Genomics; Gene

(This award is funded through the American Recovery and Reinvestment Act of 2009: Public Law 111-5).This is a CAREER award to support the research of Dr. Matthew Hahn in the Department of Biology and School of Informatics at Indiana University. Dr. Hahn is a third-year, tenure-track Assistant Professor. Genome sequencing projects have revealed large and frequent changes between species in the size of gene families. These changes have been shown to be responsible for morphological, physiological, and behavioral differences between species, and to contribute to much of the genetic and genomic diversity we observe in nature. To further understand the importance of these changes, researchers must be able to understand the mechanisms and modes by which gene families evolve. Despite the growing body of data on gene families, until recently we lacked a statistical framework that would allow for inferences regarding gene family evolution among species. In earlier work from his dissertation research, Dr. Hahn proposed such a framework for studying gene family evolution, and showed that it could be used for hypothesis testing, inference of ancestral states, and estimation of gene duplication and deletion rates. This project will be developing novel statistical and computational methods for studying gene families, and examining the biological mechanisms underlying gene family evolution. This work is enabling more refined estimates of gene duplication and loss rates, and will provide new ways for detecting and studying whole genome duplications. Methods for studying gene families from low-coverage genomes will also be developed. Gene duplication can distribute paralogous genes across the genome. Locations of individual genes will allow study of both within-genome and between-genome dynamics of gene families. Lineages differ in their rates of gene turnover which raises the question of how these differences come about. This research is identifying the biological factors determining observed rate variation among lineages and among individual gene families. Dr. Hahn is developing new computational models and free software, which will be available at http://www.bio.indiana.edu/~hahnlab/.This research will contribute to many fields, including studies of gene and genome duplication to studies of gene regulation, transposable elements, genetic robustness, and RNA interference. As a part of his CAREER project, the PI is integrating knowledge from these diverse fields at high school, undergraduate, and graduate levels to inform biological reasoning and to create new lines of scientific inquiry. Further, the PI will prepare and implement a curriculum for students at a local technology-focused high school. This curriculum will integrate computers into the biology classroom by introducing the basic principles of programming alongside the basic principles of biology.

(This该奖项是通过2009年美国复苏和再投资法案：公法111-5）资助的。这是一个职业奖，以支持印第安纳州大学生物系和信息学院的马修·哈恩博士的研究。哈恩博士是一个第三年，终身助理教授。基因组测序项目揭示了物种之间基因家族大小的巨大而频繁的变化。这些变化已被证明是物种之间形态、生理和行为差异的原因，并有助于我们在自然界中观察到的大部分遗传和基因组多样性。为了进一步理解这些变化的重要性，研究人员必须能够理解基因家族进化的机制和模式。尽管关于基因家族的数据越来越多，但直到最近，我们还缺乏一个统计框架，可以对物种之间的基因家族进化进行推断。在他的博士论文研究的早期工作中，Hahn博士提出了这样一个研究基因家族进化的框架，并表明它可以用于假设检验，祖先状态的推断以及基因复制和缺失率的估计。该项目将开发新的统计和计算方法来研究基因家族，并研究基因家族进化的生物学机制。这项工作使人们能够更精确地估计基因复制和丢失率，并将为检测和研究全基因组复制提供新的方法。还将制定从低覆盖率基因组研究基因家族的方法。基因复制可以在基因组中分布旁系同源基因。单个基因的位置将允许研究基因家族的基因组内和基因组间动态。不同的血统在他们的基因周转率，这提出了这些差异是如何产生的问题。这项研究是确定生物学因素，确定观察到的率变异之间的血统和个别基因家族。Hahn博士正在开发新的计算模型和免费软件，这些模型和软件将在http://www.bio.indiana.edu/~hahnlab/.This上提供，研究将有助于许多领域，包括基因和基因组复制的研究，基因调控，转座因子，遗传鲁棒性和RNA干扰的研究。作为他的职业生涯项目的一部分，PI正在整合高中，本科和研究生阶段这些不同领域的知识，以告知生物推理并创建新的科学探究路线。此外，PI将为当地以技术为重点的高中的学生准备和实施课程。本课程将通过介绍编程的基本原理以及生物学的基本原理，将计算机融入生物学课堂。