CAREER: Next-generation inference of evolutionary paramaters from genome-wide sequence data

职业：从全基因组序列数据中推断进化参数的下一代

• 批准号: 1452622
• 负责人: Sohini Ramachandran
• 金额: $ 87.61万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-15 至 2021-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1452622&HistoricalAwards=false
• 关键词: CAREER; Next; generation; inference; evolutionary

The goal of this project is to develop a suite of novel statistical and computational methods for inferring detailed evolutionary histories from whole-genomes of multiple individuals. While advances in sequencing technology have made DNA sequencing routine in most laboratories, methods to infer evolutionary histories from large sequencing datasets have had to make restrictive or biologically unrealistic assumptions to achieve computational tractability. This project will overcome the limitations of current methods by relaxing such restrictive assumptions and it will provide users with ways to assess the accuracy of estimates produced by the methods. This research will be integrated with an education plan that consists of multiple activities intended to introduce young women in high school to computer science and biology research: offering summer research experiences in the PI's lab, inviting high school students to collaborate with undergraduates in the PI's courses that teach programming skills to biology majors, and teaching programming to a variety of high school audiences.The objectives of this project are to develop methods that accurately infer (1) changes in population size over time, (2) rates of migration between populations over time, and (3) genomic targets of natural selection --- all from sequence data alone, taken from multiple individuals within a single species. The methods developed will model recombination, produce measures of uncertainty for reported estimates, allow for complex population histories, and identify regions of the genome under selection. The methods developed in this project will also be applied to test hypotheses regarding the evolutionary histories of a range of organisms. Software produced and data analyzed in publications resulting from the research will be made available to the public on the lab's data repository (http://ramachandran-data.brown.edu/) and through R packages deposited on the Comprehensive R Archive Network (http://cran.r-project.org/).

该项目的目标是开发一套新的统计和计算方法，用于从多个个体的全基因组推断详细的进化历史。虽然测序技术的进步使DNA测序在大多数实验室中成为常规，但从大型测序数据集推断进化历史的方法必须做出限制性或生物学上不切实际的假设才能实现计算可处理性。 这一项目将通过放宽这种限制性假设来克服目前方法的局限性，并将向用户提供评估这些方法所产生的估计数的准确性的方法。这项研究将与一项教育计划相结合，该计划包括旨在向高中女生介绍计算机科学和生物学研究的多种活动：在PI实验室提供夏季研究经验，邀请高中生与本科生合作学习PI为生物专业教授编程技能的课程，该项目的目标是开发准确推断（1）人口规模随时间的变化，（2）人口之间随时间的迁移率，以及（3）自然选择的基因组目标--所开发的方法将模拟重组，产生报告估计的不确定性措施，允许复杂的人口历史，并确定基因组区域的选择。在这个项目中开发的方法也将被应用于测试假设有关的一系列生物体的进化历史。研究产生的软件和出版物中分析的数据将在实验室的数据存储库（http：//ramachandran-data.brown.edu/）上向公众提供，并通过存放在综合R档案网络（http：//cran.r-project.org/）上的R软件包提供。