CiC: EAGER: Inferring Pattern and Processes of Genome Evolution Through Cloud Computing

佛罗里达大学获得一笔拨款，用于在微软Azure云计算平台上实施和评估识别基因重复和丢失进化历史的最新方法，并将利用这些方法重建植物全基因组重复的历史。 进化生物学最大的挑战之一是确定适应性变化和物种多样化的遗传机制。 来自许多不同物种的大规模基因组数据集的可用性为识别这种重要的遗传变化提供了前所未有的机会。 基因复制在获得新的基因功能和适应性创新中起着关键作用。 然而，为了将基因复制与适应性变化联系起来，有必要确定在进化历史中复制发生的时间。 最近开发的基于模型的方法使科学家能够在物种进化中绘制基因复制和丢失事件的位置。然而，这些方法是计算密集型的，并且因此仅针对小数据集实施。通过Microsoft Azure平台的云计算提供了一个理想的系统，可以扩展这些方法的实现，以整合来自许多生物体的完整基因组数据集，并跟上新基因组序列的快速积累。计算生物学的教育和培训是该项目的一个主要组成部分。 这项工作不仅将激励对基因进化建模的新研究，并使大量的分析能够识别潜在的基因组创新，它还将为博士后和研究生提供跨学科培训的独特机会。此外，还将为课堂和互联网开发关于使用云计算进行大规模生物信息学分析的教育资源，并将结合进化生物学家会议举办关于云计算进行进化分析的讲习班。

The University of Florida is awarded a grant to implement and evaluate the latest methods for identifying the evolutionary history of gene duplications and losses on the Microsoft Azure cloud computing platform, and will use these methods to reconstruct the history of whole genome duplications in plants. One of the greatest challenges in evolutionary biology is to identify genetic mechanisms responsible for adaptive changes and species diversification. The availability of large-scale genomic data sets from many diverse species provides unprecedented opportunities to identify such important genetic changes. Gene duplication plays a key role in gaining new gene functions and, consequently, adaptive innovations. However, in order to link gene duplications with adaptive changes, it is necessary to determine when in evolutionary history the duplications took place. Recently developed model-based methods enable scientists to map the locations of gene duplications and loss events within a species phylogeny. However, these methods are computationally intensive, and consequently, have only been implemented for small data sets. Cloud computing through the Microsoft Azure platform offers the ideal system in which to extend the implementations of these methods to incorporate full genomic data sets from many organisms and to keep pace with the rapid accumulation of new genome sequences. Education and training in computational biology are a major component of this project. Not only will this work motivate new research into modeling gene evolution and enable enormous analyses to identify potential genomic innovations, it also will provide unique opportunities for cross-disciplinary training for a post-doc and graduate student. Furthermore, educational resources on the uses of cloud computing for large-scale bioinformatics analyses will be developed for the classroom and internet, and a workshop on cloud computing for evolutionary analyses will be held in conjunction with a conference of evolutionary biologists.