Collaborative Research: CIBR: CloudForest: A Portable Cyberinfrastructure Workflow To Advance Biological Insight from Massive, Heterogeneous Phylogenomic Datasets

合作研究：CIBR：CloudForest：一种便携式网络基础设施工作流程，可从海量、异质的系统发育数据集中推进生物学洞察

• 批准号: 1934156
• 负责人: Jeremy Brown
• 金额: $ 35.83万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934156&HistoricalAwards=false
• 关键词: Collaborative; Research; CIBR; CloudForest; Portable

Variation across inferred gene trees is arguably the most consistent and striking observation from empirical phylogenomic studies, yet many unanswered questions remain about the causes of this variation. The questions persist in part because modern phylogenetic inference is still deeply influenced by a decades-old paradigm. Data from one or a few genes were typically gathered at the same time, combined into a single dataset, and analyzed by a single program that estimated a shared tree. While the size and complexity of datasets has changed radically in recent years, many aspects of this general workflow pervade. Most current approaches do not naturally integrate inferences from different sources, whether different studies or software packages, and even cutting-edge methods that model differences in gene histories still summarize these histories as a single "species tree" topology. More versatile tools are needed to understand the heterogeneity inherent to modern genomic datasets. Key to this versatility is the ability to flexibly and seamlessly move between different stages of a phylogenetic workflow, from inference of individual gene trees to exploration of the genome-wide phylogenetic landscape and, ultimately, to learning about the biological processes that have shaped variation across the genome. Each of these stages may rely on different analytical tools and software.The major aim of this project is to develop a cyberinfrastructure workflow called Cloudforest to address outstanding challenges in phylogenomics and provide researchers with a set of streamlined tools to explore and understand variation in evolutionary history across different regions of the genome (i.e., gene tree variation). CloudForest will allow users to leverage diverse computing resources that range from laptops, to HPC clusters, to cloud-based resources like JetStream or Amazon Web Services. CloudForest will meet many of the outstanding needs of empirical phylogenomic studies, such as (1) visualizing variation across gene trees, (2) revealing structure in sets of trees (forests), (3) conducting hypothesis tests regarding the causes of gene-tree variation, and (4) detecting genes that may have outlying (and potentially aberrant) histories. By addressing these challenges in a consistent way across computing platforms, CloudForest will allow biologists to make efficient use of any computational resource at their disposal with workflows appropriate for addressing a variety of important, unresolved questions in both evolutionary biology and other applied fields. This project also aims to advance broader goals by (1) supporting broad educational and training opportunities for researchers from around the world in the use of advanced computing solutions, (2) actively promoting the involvement and achievements of researchers from underrepresented groups in computational biology, (3) providing unique, interdisciplinary training opportunities for graduate students at the intersection of computing, math, and biology, (4) contributing to the development of an interactive and visually rich website for learning about phylogenetics and phylogenomics, and (5) facilitating applied phylogenetic research that will advance human health and well-being. A public facing web site for this project can be found at https://github.com/jwilgenb/CloudForest.This 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.

推断的基因树之间的变异可以说是经验系统发育学研究中最一致和最引人注目的观察结果，但关于这种变异的原因仍有许多未解答的问题。 这些问题之所以持续存在，部分原因是现代系统发育推断仍然深受几十年前的范式的影响。来自一个或几个基因的数据通常同时收集，组合成单个数据集，并通过估计共享树的单个程序进行分析。尽管近年来数据集的大小和复杂性发生了根本性的变化，但这种一般工作流程的许多方面都渗透其中。目前的大多数方法并不能自然地整合来自不同来源的推论，无论是不同的研究还是软件包，甚至模拟基因历史差异的尖端方法仍然将这些历史总结为单个“物种树”拓扑。需要更多通用的工具来理解现代基因组数据集固有的异质性。这种多功能性的关键是能够在系统发育工作流程的不同阶段之间灵活、无缝地移动，从单个基因树的推断到全基因组系统发育景观的探索，并最终了解影响整个系统发育变异的生物过程。基因组。每个阶段都可能依赖于不同的分析工具和软件。该项目的主要目标是开发一个名为 Cloudforest 的网络基础设施工作流程，以解决系统基因组学中的突出挑战，并为研究人员提供一套简化的工具来探索和理解进化历史的变异。跨越基因组的不同区域（即基因树变异）。 CloudForest 将允许用户利用各种计算资源，从笔记本电脑到 HPC 集群，再到 JetStream 或 Amazon Web Services 等基于云的资源。 CloudForest 将满足实证系统发育学研究的许多突出需求，例如（1）可视化基因树之间的变异，（2）揭示树（森林）集合中的结构，（3）对基因树的原因进行假设检验变异，以及（4）检测可能具有异常（和潜在异常）历史的基因。通过跨计算平台以一致的方式应对这些挑战，CloudForest 将使生物学家能够有效利用他们掌握的任何计算资源，并通过适合解决进化生物学和其他应用领域中各种重要的、未解决的问题的工作流程。 该项目还旨在通过以下方式推进更广泛的目标：(1) 支持为世界各地的研究人员提供使用先进计算解决方案的广泛教育和培训机会，(2) 积极促进计算生物学领域代表性不足群体的研究人员的参与和成就， (3) 为研究生提供计算、数学和生物学交叉领域的独特的跨学科培训机会，(4) 有助于开发一个交互式且视觉丰富的网站，用于学习系统发育学和系统发育学，以及 (5) 促进应用系统发育研究表明将促进人类健康和福祉。 该项目的面向公众的网站可以在 https://github.com/jwilgenb/CloudForest 找到。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Investigating the Genomic Distribution of Phylogenetic Signal with CloudForest

使用 CloudForest 研究系统发育信号的基因组分布

• DOI: 10.1145/3437359.3465605
• 发表时间: 2021-07
• 期刊: Proceedings of the Practice and Experience on Advanced Research Computing
• 作者: Wagner, Reid;Toups, Benjamin S.;Deng, Zhifeng;Gallivan, Kyle A.;Brown, Jeremy M.;Wilgenbusch, James C.
• 通讯作者: Wilgenbusch, James C.