NEXT GENERATION APPROACHES TO NON-HUMAN PRIMATE BIOINFORMATICS

这个子项目是利用资源的许多研究子项目之一。 由NIH/NCRR资助的中心拨款提供。对子项目的主要支持 子项目的首席调查员可能是由其他来源提供的， 包括美国国立卫生研究院的其他来源。为子项目列出的总成本可能 表示该子项目使用的中心基础设施的估计数量， 不是由NCRR赠款提供给次级项目或次级项目工作人员的直接资金。 随着我们进入后基因组时代，新的技术已经变得可用，允许更多和更完整地了解非人类灵长类遗传学。这个项目利用这些新的机会来回答长期存在的问题，并改进现有的研究。具体地说，我们正在使用异种杂交方法来捕获恒河猴和其他非人类灵长类动物的完整外显子组，然后使用下一代技术完成重新测序。然后，我们将使用这些信息来改进现有的基因组，包括识别错误、填补空白和识别变异。这也将使我们能够在基因组水平上更好地描述进化变化，包括更好地注释非人类灵长类同源物，以及描述跨物种和基因的进化变化率。我们还利用这一信息来比较与特定人类疾病相关的基因组的进化速度；特别是精神分裂症和自闭症被假设为源于人类特有的大脑变化。与序列信息数量和质量的增长相结合的是，需要一个强大的生物信息学基础设施来访问数据并将其整合到正在进行的研究中。在这里，我们正在开发工具，允许将遗传信息快速而完整地整合到现有的表型中，确保研究人员可以立即将遗传学作为他们的非人类灵长类研究的辅助因素，并在新出现的特征中探索遗传学和环境之间的关系。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. As we move into the post-genomic era new technologies have become available that allow for a greater and more complete understanding of non-human primate genetics. This project exploits these new opportunities to answer questions that have long existed and to improve existing studies. Specifically, we are using xenohybridization approaches to capture the complete exomes of rhesus macaques as well as other non-human primates prior to complete resequencing using next generation technologies. We will then use this information to improve the existing genomes, including identifying mistakes, fill in gaps, and identify variation. This will also allow us to better characterize the evolutionary changes at the genomic level, including better annotation of non-human primate orthologs and characterization of evolutionary rates of change across species and genes. We also are using this information to compare evolutionary rates of change for groups of genes associated with specific human diseases; schizophrenia and autism in particular have been hypothesized to have been derived from human-specific changes to the brain. Coupling with this growth in the quantity and quality of sequence information is the need for a robust bioinformatic infrastructure for accessing and integrating the data into ongoing research. Here we are developing tools that allow for a rapid and complete incorporation of genetic information into existing phenotypes, ensuring that researchers can immediately include genetics as a cofactor in their non-human primate research and to explore the relationship between genetics and environment in emerging traits.