Genome-wide mutation models to decipher function

描述（申请人提供）：完整的脊椎动物基因组正在迅速积累，积累的速度只会越来越快。这是一个极好的消息，因为比较分析的效用在很大程度上取决于物种取样的多样性。然而，要充分利用这些广泛数据的潜力，存在着重大挑战：需要发展新的方法和分析方法。我们的目标是发展和扩展我们的能力，以分析动态进化过程（跨地区和时间），已经形成了现存的基因组。我们将使用我们最近开发的贝叶斯进化分析方法来实现这一目标，该方法使我们比竞争方法具有许多数量级的速度优势，并且可以很好地扩展模型复杂性和数据大小。我们提出的许多研究都是基于生物学上的现实范式，而这些范式以前由于计算限制而无法考虑或测试。我们建议全面描述一组选定的脊椎动物基因组的重复内容，包括对基因组“暗物质”（目前未注释的部分）的古代元素的注释。这组重复序列中的转座因子将用于构建上下文相关替代过程的第一个完整全基因组模型。我们将考虑诸如重组、重排、表达和局部核苷酸含量等环境，以及未知环境，并分析受这些环境影响的进化过程如何随时间而变化。这些依赖于上下文的替代模型将为鉴定和注释脊椎动物基因组种间比较中的功能区域，以及区分和表征蛋白质中基于适应度的效应提供强大的工具。核心概念是，如果我们更好地了解背景核苷酸替代的全基因组模式，那么我们将能够更准确地识别可能具有功能的基因组区域，并了解选择如何指导蛋白质的进化。

DESCRIPTION (provided by applicant): Complete vertebrate genomes are accumulating rapidly, and the pace of accumulation will only increase. This is excellent news, because the utility of comparative analysis depends heavily on the diversity of species sampling. There are, however, substantial challenges to exploiting the full potential of such extensive data: development of novel methods and analytical approaches is needed. We aim to develop and extend our capacity to analyze the dynamic evolutionary processes (across regions and through time) that have shaped extant genomes. We will achieve this goal using a Bayesian evolutionary analysis approach we recently developed that allows us many orders of magnitude speed advantage over competing approaches, and which scales well with model complexity and data size. Many of the studies we propose are based on biologically realistic paradigms that previously were impossible to consider or test because of computational limitations. We propose to comprehensively delineate the repetitive contents of a selected set of vertebrate genomes, including annotation of ancient elements from the "dark matter" of genomes (the currently unannotated portion). The transposable elements in this set of repeat sequences will be used to build the first complete genome-wide models of context-dependent substitution processes. We will consider contexts such as recombination, rearrangement, expression, and local nucleotide content, as well as unknown contexts, and analyze how the evolutionary processes influenced by these contexts have changed over time. These context- dependent substitution models will provide a powerful tool for identifying and annotating functional regions in interspecific comparisons of vertebrate genomes, and for differentiating and characterizing fitness-based effects in proteins. The core concept is that that if we better understand genome-wide patterns of background nucleotide substitution, then we will be able to more accurately identify genomic regions that are likely functional, and to understand how selection directs the evolution of proteins.