New algorithms and software for analyzing and classifying evolutionary and biomedical data

用于分析和分类进化和生物医学数据的新算法和软件

• 批准号: RGPIN-2016-06557
• 负责人: Makarenkov, Vladimir
• 金额: $ 2.77万
• 依托单位: Université du Québec à Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710316
• 关键词: New; algorithms; software; analyzing; classifying

My research proposal involves five major components related to the development of new algorithms and software for analyzing and classifying evolutionary and biomedical data. First, we will continue to investigate the phenomenon of reticulate evolution in the context of horizontal gene transfer (HGT). We propose to design original and effective maximum likelihood algorithms for inferring and validating statistically both complete and partial HGT events as well as for determining types (i.e., whether the transferred gene is additive, replacing or recombining) and units (i.e., whether gene transfer involves gene fragments, whole genes or entire operons) of gene transfers. The proposed algorithms will be used to estimate the impact of HGT on the resistance of bacteria to antibiotics a topic of particular interest to the Canadian healthcare system and pharmaceutical industry. Furthermore, we will develop and maintain an up-to-date database dedicated to gene transfer networks of antibiotic resistance genes. Second, we will design a novel bioinformatics framework for estimating and validating the rates of complete and partial HGT among prokaryotes at different phylogenetic and ecological levels. It will allow researchers to determine the proportion of mosaic genes in prokaryotic genomes, to identify prokaryotic families and habitats being the major donors and recipients of genetic material, and to assess the ages of the detected HGT events. Third, we will propose a novel maximum likelihood method for identifying diploid hybridization events, including statistical validation of the detected hybrids and their parents by bootstrap analysis. This method will be extended to determine whether the relationship among the given species should be represented by a phylogenetic tree or by a hybridization network. Such methods will be of significant interest to a large community of plant and fish biologists. Fourth, we will design novel algorithms and a new statistical test for analyzing and correcting experimental high-throughput screening (HTS) data. This test will identify the type of systematic bias affecting a given HTS assay (i.e., additive or multiplicative bias). The new algorithms will be used to detect and eliminate multiplicative type of systematic bias in experimental HTS. Moreover, a novel data processing protocol for optimizing hit selection process will be introduced. The proposed methods will allow researchers to minimize the impact of systematic error in experimental HTS and thus improve the selection of potential drug candidates. Finally, we will develop open-source software to allow academic and industrial researchers throughout the world to carry out our new algorithms for detecting, validating and visualizing HGT and hybridization events, inferring and examining gene transfer networks of antibiotic resistant genes, and correcting and analyzing experimental HTS assays.

我的研究计划包括五个主要组成部分，涉及开发新的算法和软件，用于分析和分类进化和生物医学数据。 首先，我们将继续研究水平基因转移（HGT）背景下的网状进化现象。我们建议设计原始和有效的最大似然算法，用于推断和验证统计上的完整和部分HGT事件以及用于确定类型（即，转移的基因是加性的、替换的还是重组的）和单位（即，基因转移是否涉及基因片段、整个基因或整个操纵子）。所提出的算法将用于估计HGT对细菌对抗生素的耐药性的影响，这是加拿大医疗保健系统和制药行业特别感兴趣的话题。此外，我们将开发和维护一个最新的数据库，专门用于抗生素抗性基因的基因转移网络。 第二，我们将设计一个新的生物信息学框架，用于估计和验证不同系统发育和生态水平的原核生物之间的完全和部分HGT的比率。它将使研究人员能够确定原核基因组中嵌合基因的比例，确定原核家庭和栖息地是遗传物质的主要供体和受体，并评估检测到的HGT事件的年龄。 第三，我们将提出一种新的最大似然法识别二倍体杂交事件，包括统计验证检测到的杂交种和它们的父母的自助分析。这种方法将被扩展，以确定是否应该由一个系统发育树或杂交网络表示的给定物种之间的关系。这种方法将是显着的兴趣，一个大的社区的植物和鱼类生物学家。 第四，我们将设计新的算法和新的统计测试，用于分析和校正实验高通量筛选（HTS）数据。该测试将识别影响给定HTS测定的系统偏倚类型（即，加性或乘性偏差）。新算法将用于检测和消除实验HTS中的倍增型系统偏差。此外，一个新的数据处理协议，优化命中选择过程将被引入。所提出的方法将使研究人员能够最大限度地减少实验HTS中系统误差的影响，从而改善潜在候选药物的选择。 最后，我们将开发开源软件，使世界各地的学术和工业研究人员能够执行我们的新算法，用于检测，验证和可视化HGT和杂交事件，推断和检查抗生素耐药基因的基因转移网络，以及校正和分析实验HTS检测。