Computational Tools and Statistical Analysis of Co-Varying Rates of Different Mutation Types

位于大学公园的宾夕法尼亚州立大学获得了一项资助，通过对完全测序的哺乳动物基因组和人类重测序数据的比较，研究不同突变率的区域（主要是染色体内）变异和共变异。突变是自然种群遗传变异的来源，并为分子进化提供了材料，但迄今为止，突变的机制尚不完全清楚。该项目将首先描述核苷酸替换、小插入和缺失以及微卫星重复数变化等突变类型的区域变异和共变异，在多个尺度上检查它们的基因组共发生和线性关联。这将导致对共同发生的突变类型的见解，并在基因组尺度上，他们的共同变异盛行。其次，该团队将研究这些突变类型的区域速率变异和共变异的潜在原因，同时在多个尺度上用线性方法将它们与基因组景观特征联系起来。这将有助于深入了解基因组特征在解释不同类型突变的区域速率变异和共变异中的作用。第三，他们将评估和实施非线性分析技术和回归方法的必要性。第四，突变率和共现的结果将用于通过同时利用几种突变类型的背景校正来改进功能区的计算预测。局部修正将基于多种变异类型的比率，并用于提高功能元素预测算法的性能。最后，本项目开发的计算和统计工具将在独立的基因组分析平台Galaxy （http://galaxyproject.org）的易于访问的软件套件中实施。检测突变、估计和分配突变率和窗口基因组特征的工具，以及应用多变量、多尺度和非线性技术的工具将被整合到一个基于网络的平台中，这将使它们易于用于任何测序基因组的分析。最终的框架将是高度交互的，基于经过验证的方法，并且不需要编程经验的其他研究人员和教育工作者也可以轻松访问。参与该项目的研究生将获得生物学、计算机科学和统计学的跨学科培训。该项目将通过宾夕法尼亚州立大学现有的项目招募来自代表性不足群体、女性和少数族裔的本科生。

The Pennsylvania State University at University Park is awarded a grant to study regional (primarily intra-chromosomal) variation and co-variation in rates of different mutation types from comparisons of completely sequenced mammalian genomes and human re-sequencing data. Mutations are the source of genetic variation in natural populations and provide material for molecular evolution, yet the mechanisms of mutagenesis are, to date, not completely understood. The project will first characterize the regional variation and co-variation of mutation types such as nucleotide substitutions, small insertions and deletions, and changes in microsatellite repeat number, examining their genomic co-occurrence and linear association at multiple scales. This will lead to insights on co-occurring mutation types and on genomic scales at which their co-variation prevails. Secondly, the team will investigate potential causes of regional rate variation and co-variation for these mutation types, simultaneously relating them to genome landscape features with linear approaches at multiple scales. This will lead to insights on the role of genomic features in explaining regional rate variation and co-variation for mutations of different types. Thirdly, they will assess the need for, and implement, non-linear analysis techniques and regression methods. Fourth, the results on mutation rates and co-occurrence will be used to improve computational predictions of functional regions by means of background corrections exploiting several mutation types simultaneously. Local corrections will be based on rates of multiple mutation types, and employed to improve the performance of functional element prediction algorithms. Finally, the computational and statistical tools developed in this project will be implemented in readily accessible software suites in Galaxy, a free-standing genome analysis platform (http://galaxyproject.org). Tools for detecting mutations, estimating and apportioning mutation rates and genomic features in windows, and applying multivariate, multi-scale and non-linear techniques will be integrated into a web-based platform that will make them readily available for the analysis of any sequenced genomes. The resulting framework will be highly interactive, based on proven methodology, and easily accessible by other researchers and educators with no need for programming experience. Graduate students working on this project will acquire interdisciplinary training in biology, computer science, and statistics. Undergraduate students from underrepresented groups, women and minorities, will be recruited for the project through existing programs at Penn State.