权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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.

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。全世界的科学家都在努力了解地球上的生物多样性是如何进化的。这种多样化的过程通过生命之树表现出来。实现对生命之树的完整估计的目标，将使我们能够充分了解自然界中重要生物特征的发展和进化，例如，在面临气候变化等环境威胁时，与灭绝恢复能力有关的生物特征。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，发展统计和计算工具来重建生命之树在进化生物学、系统学、保护工作和人类健康研究中是至关重要的。然而，现有的树重建方法是有限的，因为它们没有考虑重要的生物过程，如物种杂交、基因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的方法的爆炸式增长。现有的网络重构方法缺乏对数据中网状信号检测的统计保证，对于大数据的可扩展性不足，并且是针对简单网络重构的定制化方法。因此，它们不足以解决真菌、原核生物或病毒中网状进化的复杂性。该项目将开发具有强大统计保证的新颖网络推理方法，这些方法足够健壮，可以推断复杂的网络，并且具有足够的可扩展性以适应大数据。这些方法将允许将所有生物体整合到生命之树中，从而有助于在所有生命领域完成更广泛的进化图景。该项目将为K-16外展提供开源软件和数据科学模块，并将重点放在培训STEM中代表性不足的群体上。本项目将为生命网络的基础研究做出贡献，产生四个全新的科学成果，具有广泛的科学外延：1)第一个为宏基因组数据定制的系统基因组推断方法，该方法从原始reads开始，在管道的每一步都充分传播统计误差，以估计复杂真菌，原核生物或病毒群落的进化史；2)第一个关于复杂系统发育网络可识别性的统计理论；3)第一种分而治之的算法，产生迄今为止最具可扩展性的推理程序，以满足生物大数据日益增长的需求；4)在进化生物学，系统学，保护和人类健康社区具有广泛适用性的开源易于使用的公开软件。该奖项反映了美国国家科学基金会的法定使命，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，被认为值得支持。