Algorithmic developments to unravel the evolution of viruses and other microbes

揭示病毒和其他微生物进化的算法发展

• 批准号: RGPIN-2016-04181
• 负责人: ArisBrosou, Stephane
• 金额: $ 2.33万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=614895
• 关键词: Algorithmic; developments; unravel; evolution; viruses

Our research program focuses on developing methods to address outstanding questions in ecology and evolution. As such, our contributions range from microbes to seabirds, with a particular emphasis on viral evolution. Seemingly disparate, these contributions are all united by the design and implementation of ingenious algorithms to address novel biological questions. This proposal will further develop this theme by addressing 4 main hypotheses/research questions. First, we recently developed an algorithm to detect correlated evolution (epistasis) from genetic data. We plan to further develop this tool to analyze multiple genes (and perform whole-genome scans), assess how knowledge of protein structure can guide our algorithm, allow the analysis of multi-state characters (4 for DNA, 20 for proteins) and multiway interactions. With a collaborator, we will test some of the predictions in an in vitro system (Pseudomonas aeruginosa). Second, we recently uncovered a link between selection and epistasis in the Ebola virus. An immediate question is: how general is this? For this, we will mine large datasets to contrast viruses in which selection and epistasis are unlikely (double stranded DNA viruses) with viruses where both selection and epistasis are expected (unsegmented RNA viruses). Third, our recent progress in employing artificial intelligence (machine learning) to address biological questions is motivating us to explore this area further. We will explore ways to better understand evolutionary rate change in influenza viruses, and employ two bacterial models to identify the genomic determinants of phenotypic traits (host restrictions, drug resistance, mercury detoxification) and even fitness. Fourth, we will develop an approach to reconstruct viral transmission networks, employing Approximate Bayes Computing, a technique developed in population genetics but never used in this context. Simulations and a large data set from the Swiss HIV epidemics will be used to validate and test our predictive power. The end-goal of this direction is to provide us with a tool that can assess changes in public health policies. Altogether, this highly interdisciplinary program will provide us with tools that will not only increase our understanding of fundamental evolutionary processes (epistasis & selection), but also help us manage public health policies and help with bioremediation at sites contaminated with mercury. Knowledge will be transferred by means of publications, conference presentations and seminars. End users will range from academics to policy makers (Environment, Public Health). Over the next 5 years, this program will also train at least eight HQP (four undergraduates + four graduate students) in state-of-the-art methods in computational biology, using large datasets ("Big Data", but with a careful design) and state-of-the-art computing environments (local cluster, HPCVL).

我们的研究计划专注于开发方法来解决生态学和进化论中的突出问题。因此，我们的贡献范围从微生物到海鸟，特别强调病毒的进化。这些贡献看似不同，但都是通过设计和实施巧妙的算法来解决新的生物学问题而结合在一起的。该提案将通过解决4个主要假设/研究问题来进一步发展这一主题。 首先，我们最近开发了一种从遗传数据中检测相关进化(上位性)的算法。我们计划进一步开发这一工具来分析多个基因(并执行全基因组扫描)，评估蛋白质结构知识如何指导我们的算法，允许分析多状态特征(DNA为4个，蛋白质为20个)和多路相互作用。与合作者一起，我们将在体外系统(铜绿假单胞菌)中测试一些预测。其次，我们最近发现了埃博拉病毒的选择和上位性之间的联系。一个迫在眉睫的问题是：这有多普遍？为此，我们将挖掘大型数据集，以对比不太可能选择和上位性的病毒(双链DNA病毒)和同时预期选择和上位性的病毒(未分段RNA病毒)。第三，我们最近在利用人工智能(机器学习)解决生物问题方面的进展促使我们进一步探索这一领域。我们将探索更好地了解流感病毒进化率变化的方法，并使用两个细菌模型来确定表型特征(宿主限制、抗药性、汞解毒)甚至适应性的基因组决定因素。第四，我们将开发一种重建病毒传播网络的方法，使用近似贝叶斯计算，这是一种在群体遗传学中开发的技术，但从未在本文中使用过。模拟和来自瑞士艾滋病毒流行的大量数据将被用来验证和测试我们的预测能力。这一方向的最终目标是为我们提供一个能够评估公共卫生政策变化的工具。 总而言之，这一高度跨学科的项目将为我们提供工具，不仅将增加我们对基本进化过程(上位和选择)的理解，还将帮助我们管理公共卫生政策，并帮助我们在汞污染的地点进行生物修复。将通过出版物、会议报告和研讨会等方式转让知识。最终用户将从学者到政策制定者(环境、公共卫生)。在接下来的5年里，该项目还将培训至少8名HQP(4名本科生+4名研究生)，学习计算生物学中最先进的方法，使用大数据集(大数据，但经过精心设计)和最先进的计算环境(本地集群，HPCVL)。