Collaborative Research: ABI Development: Improving the stability, usability, and speed of the RevBayes platform for phylogenetic analysis

合作研究：ABI 开发：提高 RevBayes 系统发育分析平台的稳定性、可用性和速度

• 批准号: 1759811
• 负责人: John Huelsenbeck
• 金额: $ 54.68万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1759811&HistoricalAwards=false
• 关键词: Collaborative; Research; ABI; Development; Improving

All species are related to one another through an unknown genealogical tree: the `Tree of Life.' Genealogical trees for small parts of the Tree of Life are called `phylogenies' by biologists; there is more interest in phylogenies today than at any time in the past. Phylogenies are crucial in fields such as epidemiology where they are used to track the spread of infectious disease and molecular biology where they are used to understand how molecular pathways developed. The `Tree of Life' can be discovered by comparing the characteristics of different organisms. Traditionally, phylogenies were reconstructed by comparing anatomical traits across different species. More recently, DNA sequence information from individual genes, or even full genomes, are compared to estimate phylogenies. This project will continue the development of the RevBayes computer program that biologists use to reconstruct phylogenies. The program takes as input the comparative information from the organisms of interest. The output of the program is the probabilities of the best phylogenies that explain the comparative data. Specifically, the project will improve the speed, usability, and reliability of the program.RevBayes is the successor of the MrBayes program which is widely used by biologists to estimate phylogeny. However, the RevBayes program represents a significant departure from MrBayes. The RevBayes program implements an R-like language to describe statistical models. The model is represented in computer memory as a graph in which the vertices of the graph are the parameters and the edges represent the dependencies between parameters. RevBayes uses Markov chain Monte Carlo to approximate the posterior probability distribution of parameters. This project will improve the RevBayes program in several signficant ways: (1) unit and integration testing will be implemented to improve the reliability of the program; (2) computational resources, such as multiple cores or GPUs, will be taken better advantage of, to improve the speed of the program; (3) a cross-platform graphical user interface will be developed to improve the usability of the program; (4) output will be bundled to improve the reproducibility of the program; and (5) the program will be made to work with other software programs, to improve its interoperability. Finally, several workshops will be hosted by the participants in which phylogenetic theory will be described with emphasis of application of the theory to real-world problems using RevBayes. The source code can be found on the Github site at http://revbayes.github.io/about.htmlThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

所有物种都通过一个未知的家谱树相互关联：“生命之树”。“生命之树的一小部分的系谱树被生物学家称为‘遗传学’;今天对遗传学的兴趣比过去任何时候都多。系统发育在流行病学和分子生物学等领域至关重要，流行病学用于跟踪传染病的传播，分子生物学用于了解分子途径如何发展。“生命之树”可以通过比较不同生物体的特征来发现。传统上，通过比较不同物种的解剖学特征来重建基因组。最近，将来自单个基因甚至全基因组的DNA序列信息与估计的基因组进行比较。该项目将继续开发RevBayes计算机程序，生物学家使用该程序重建遗传学。该程序将来自感兴趣的生物体的比较信息作为输入。程序的输出是解释比较数据的最佳概率。具体来说，该项目将提高程序的速度，可用性和可靠性。RevBayes是MrBayes程序的继承者，该程序被生物学家广泛用于估计遗传学。然而，RevBayes程序代表了与MrBayes的重大偏离。RevBayes程序实现了一种类似R的语言来描述统计模型。该模型在计算机存储器中表示为图，其中图的顶点是参数，并且边表示参数之间的依赖性。RevBayes使用马尔可夫链蒙特卡罗来近似参数的后验概率分布。该项目将在几个方面对RevBayes程序进行改进：（1）通过单元测试和集成测试提高程序的可靠性;（2）充分利用多核或GPU等计算资源，提高程序的运行速度;（3）开发跨平台的图形用户界面，提高程序的可用性;（4）输出将被捆绑，以提高程序的可再现性;以及（5）该程序将与其他软件程序一起工作，以提高其互操作性。最后，几个研讨会将主办的参与者，其中系统发育理论将被描述与应用的理论，以现实世界的问题，使用RevBayes的重点。源代码可以在Github网站http://revbayes.github.io/about.htmlThis奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估而被认为值得支持。