Patterns of Evolution

在我们小组中，我们使用生物信息学来研究分子进化和基因组学。这是生物信息学的黄金时代。已知的细菌基因组有数千种，已知的真核基因组有数百种。但要分析这些海量数据，需要跨学科的“大数据”技能。从目前正在生成的数据中有一些令人兴奋的发现。其他发现保存在现有数据中，等待发现。在我们最近的发现中，有来自古代细菌基因组序列的证据，这些证据表明，一些古代细菌恰好属于现有基因组的系统发育结构，而另一些古代细菌似乎是进化的死胡同。还有其他细菌导致了人类社会的巨大变化，但它们并没有影响人类的遗传学。 我们的工作通过包括横向基因转移、重排、空间替换、选择和其他基因组事件的信息，构建了更完整的细菌基因组进化图景。在这项提议中，我们将进一步开发新的方法来模拟细菌基因组进化，并特别关注基因组中的重排。由于这些基因组中的许多都是不完整的，因此将使用一种方法，该方法包括我们开发的“缺失数据”的统计方法。 我们小组正在努力改进从元基因组样本中鉴定细菌物种。被环境DNA严重污染的元基因组样本需要捕获测序来分离和鉴定稀有类群。定向捕获测序避免了常见或无趣的分类群。但捕获测序中使用的探针类型可能会对结果产生偏差，并可能限制对分类群的正确识别。我们将创建软件来检查数百万个潜在的寡核苷酸，并从基因组中以分层的方式选择那些对菌株、物种和属独特的捕获测序。探针的层次性提高了鉴定的准确性，基因组周围可用探针数量的增加增加了捕获稀有序列的可能性。这使得非常有针对性和精确的测序得以发生。因此，在环境中或在脓毒症等情况下检测稀有细菌或稀有病原体成为可能。 我们保持着一个积极的研究小组，培训生物信息学跨学科性质的人员。这包括博士后研究员、研究助理、技术人员、研究生和本科生，帮助他们完成工作和进行自己的研究。我的团队成员接受了生物信息学、编程、算法、数学建模技术、机器学习和统计方法方面的培训。他们是一群才华横溢的人，可以在学术界或行业的任何使用计算机分析数据的部门找到工作。

In our group we use bioinformatics to study molecular evolution and genomics. This is a golden age for bioinformatics. There are thousands of bacterial genomes known and hundreds of many eukaryotic genomes known. But to analyze this huge mass of data requires interdisciplinary 'big data' skills. There are exciting discoveries to be made from the data currently being generated. Other discoveries sit in the existing data waiting to be uncovered. Among the discoveries we have made recently is evidence from ancient bacterial genomic sequences which indicate that some ancient bacteria fall neatly within the phylogenetic structure of extant genomes while other ancient bacteria appear to have been evolutionary dead ends. Still other bacteria have caused massive human social changes, and yet they have not impacted human genetics. Our work constructs a more complete picture of bacterial genome evolution by including information on lateral gene transfers, rearrangements, spatial substitutions, selection and other genomic events. In this proposal we will further develop new methods to model bacterial genome evolution with particular attention given to rearrangements in genomes. Since many of these genomes are incomplete, an approach that includes the statistical methods for 'missing data' that we have developed will be used. Our group is working to improve the identification of bacterial species from metagenomic samples. Metagenomic samples that are heavily contaminated with environmental DNA require capture sequencing to isolate and to identify rare taxa. Targeted capture sequencing avoids common or otherwise uninteresting taxa. But the type of probes used in the capture sequencing can bias the results and can limit the correct identification of taxa. We will create software to examine millions of potential oligos and select those for capture sequencing that are unique to strains, to species and to genera in a hierarchical manner from the genomes. The hierarchical nature of the probes improves accuracy of the identification and the increased number of probes available from all around the genome increases the probability of capturing rare sequences. This allows very targeted and precise sequencing to occur. Thus the detection of rare bacteria or rare pathogens in the environment or during conditions such as sepsis becomes possible. We maintain an active research group training personnel in the interdisciplinary nature of bioinformatics. This includes postdoctoral fellows, research associates, technicians, graduate students and undergraduates to help with the work and to do their own research. My group members are trained in bioinformatics, in programming, in algorithms, in mathematical modelling techniques, in machine learning and in statistical methods. They are a talented group of people that can claim jobs in any part of academia or industry that analyzes data using computers.