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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.

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。全世界的科学家都在努力了解地球上所有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，那些与面对气候变化等环境威胁时对灭绝的复原力有关的特征。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进化的信息。因此，开发统计和计算工具来重建生命树在进化生物学、系统学、保护工作和人类健康研究中至关重要。然而，现有的树重建方法是有限的，因为它们没有考虑到物种杂交、渐渗或水平基因转移等重要的生物学过程，因此，近年来出现了重建系统发育网络而不是树的方法的爆炸性增长。现有的网络重建方法缺乏确保检测数据中的网状信号的统计保证，对于大数据的可扩展性不够，并且专为重建简单的网络而量身定做。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型网络推理方法，这些方法足够健壮，足以推断复杂的网络，并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有机体整合到生命树中，从而有助于完成生命所有领域的更广泛的进化图景。该项目将为K-16推广生产开放源码软件和数据科学模块，并将重点放在培训STEM中未被充分代表的群体。该项目将通过产生四个具有广泛科学影响的全新科学成果来促进生命网络的基础研究：1)第一个针对元基因组数据量身定做的系统基因组学推断方法，该方法充分传播从原始读数开始的管道中每一步的统计误差，以估计复杂真菌、原核生物或病毒群落的进化历史；2)关于复杂系统发育网络可识别性的第一个统计学理论，3)第一个分而治之的算法，产生到目前为止最具可扩展性的推理程序，以满足不断增长的生物大数据需求；以及4)开源的易于使用的公开可用的软件，在进化生物学、系统学、保护和人类健康社区中具有广泛的适用性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。