CAREER: Towards Scalable and Robust Inference of Phylogenetic Networks

职业：走向可扩展和稳健的系统发育网络推理

基本信息

批准号：
2144367
负责人：
Claudia Solis-Lemus
金额：
$ 171.12万
依托单位：
University of Wisconsin-Madison
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-02-01 至 2027-01-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144367&HistoricalAwards=false
关键词：
CAREER Towards Scalable Robust Inference

项目摘要

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Scientists world-wide are engaged in efforts to understand how all planetary biodiversity evolved. This diversification process is represented through the Tree of Life. Achieving the goal of a complete estimate of the Tree of Life would allow us to fully understand the development and evolution of important biological traits in nature, for example, those related to resilience to extinction when exposed to environmental threats such as climate change. It would also provide information about the emergence and evolution of novel human pathogens that pose severe threats to human health. Thus, the development of statistical and computational tools to reconstruct the Tree of Life are paramount in evolutionary biology, systematics, conservation efforts, and human health research. Existing tree reconstruction methods, however, are limited because they do not account for important biological processes such as species hybridization, introgression or horizontal gene transfer, and thus, recent years have seen an explosion of methods to reconstruct phylogenetic networks rather than trees. Existing network reconstruction methods lack statistical guarantees ensuring the detection of reticulate signals in data, are not scalable enough for big data, and are tailored to reconstruct simple networks. Thus, they are not sufficient to tackle the complexity of reticulate evolution in fungi, prokaryotes, or viruses. This project will develop novel network inference methods with strong statistical guarantees that are robust enough to infer complex networks and scalable enough to accommodate big data. The methods will allow the integration of all organisms into the Tree of Life and thus help to complete a broader picture of evolution across all domains of life. The project will produce open source software and data science modules for K-16 outreach, and includes a strong focus on training underrepresented groups in STEM.This project will contribute to the fundamental research of the Network of Life by producing four entirely novel scientific outcomes with broad scientific outreach: 1) the first phylogenomics inference method tailored to metagenomic data that adequately propagates statistical error on every step of the pipeline starting on raw reads to estimate the evolutionary history of complex fungal, prokaryotic or viral communities, 2) the first statistical theory on identifiability of complex phylogenetic networks, 3) the first divide-and-conquer algorithms to produce the most scalable to date inference procedures to meet the ever growing needs of biological big data, and 4) open-source easy-to-use publicly available software with broad applicability within the evolutionary biology, systematics, conservation and human health communities.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.

该奖项是根据2021年《美国救援计划法》的全部或部分资助的（公共法117-2）。全球范围内的Scientists都在努力了解所有行星生物多样性如何发展。这种多元化过程通过生命树表示。实现对生命之树进行完整估计的目标将使我们能够充分了解自然界重要生物学特征的发展和演变，例如，与灭绝相关的生物学特征在暴露于环境威胁（例如气候变化）时。它还将提供有关对人类健康构成严重威胁的新型人类病原体的出现和演变的信息。因此，重建生命之树的统计和计算工具的开发在进化生物学，系统学，保护工作和人类健康研究中至关重要。然而，现有的树木重建方法受到限制，因为它们不考虑重要的生物学过程，例如物种杂交，渗入或水平基因转移，因此，近年来已经看到了重建系统发育网络而不是树而不是树木的方法的爆炸。现有的网络重建方法缺乏统计保证，从而确保在数据中检测网状信号，不足以扩展大数据，并且针对重建简单的网络进行了量身定制。因此，它们不足以应对真菌，原核生物或病毒中网状演变的复杂性。该项目将开发具有强大统计保证的新型网络推理方法，这些方法足以推断复杂的网络，并且足以容纳大数据。这些方法将允许将所有生物的整合到生命之树中，从而有助于完成整个生命领域的进化。该项目将为K-16外展生产开源软件和数据科学模块，并重点关注STEM中代表性不足的群体。该项目将通过产生四个完全新颖的科学成果，并为生活网络提供基本研究，并通过广泛的科学外展：1）量身定制的第一个系统基因组学推理方法，该方法是针对宏基因组数据量身定制的，在原始读取时，在管道的每个步骤中都充分传播了统计误差，以估计复杂真菌，原核生物或病毒群社区的进化历史，2）第一个统计理论，第一个统计理论关于复杂系统发育网络的可识别性，3）第一个生成迄今为止最可扩展的推理程序以满足生物学大数据需求不断增长的推理程序，以及4）开源易于公开使用的推理程序，这是第一个划分和诱导算法在进化生物学，系统，保护和人类健康社区中具有广泛适用性的软件。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。