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

我的研究计划涉及五个主要组成部分，涉及用于分析和分类进化和生物医学数据的新算法和软件的开发。*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。我们建议设计新颖有效的最大似然算法，用于推断和统计验证完全和部分HGT事件，以及确定基因转移的类型(即，被转移的基因是相加的、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)。拟议的算法将被用来估计HGT对细菌对抗生素耐药性的影响，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致力于抗生素耐药性基因转移网络的最新数据库。*第二，我们将设计一个新的生物信息学框架，在不同的系统发育和生态水平上估计和验证原核生物中完全和部分HGT的比率。它将使研究人员能够确定原核基因组中马赛克基因的比例，确定原核生物家族和栖息地是遗传物质的主要供体和接受者，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方法来识别二倍体杂交事件，包括通过Bootstrap分析对检测到的杂交种及其亲本进行统计验证。这一方法将被扩展到确定给定物种之间的关系是否应该由系统发育树或杂交网络来表示。这样的方法将引起植物和鱼类生物学家的极大兴趣。*第四，我们将设计新的算法和新的统计测试来分析和校正实验高通量筛选(HTS)数据。这项测试将确定影响给定HTS分析的系统偏差的类型(即，相加或相乘偏差)。新的算法将被用来检测和消除实验高温超导中乘性类型的系统偏差。此外，还将引入一种新的数据处理协议来优化命中选择过程。建议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，从而改进潜在候选药物的选择。*最后，我们将开发开源软件，允许世界各地的学术和行业研究人员执行我们的新算法，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性HTS分析。

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.