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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.

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。世界各地的科学家都在努力了解所有行星生物多样性是如何演变的。这种多样化的过程通过生命之树表现出来。实现完整估计生命之树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，当暴露于气候变化等环境威胁时，与灭绝恢复力相关的生物特征。它还将提供关于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、系统分类学、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，因为它们不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，近年来已经看到了爆炸的方法来重建系统发育网络，而不是树。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，对于大数据来说不够可扩展，并且被定制为重建简单网络。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，这些方法足够强大，可以推断复杂网络，并且具有足够的可扩展性，可以容纳大数据。这些方法将允许所有生物体整合到生命之树中，从而有助于完成跨越生命所有领域的更广泛的进化图景。该项目将为K-16外联制作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的培训。该项目将通过产生四个全新的科学成果，为生命网络的基础研究做出贡献。第一章第一个针对宏基因组数据定制的宏基因组学推断方法，其在从原始读数开始的流水线的每个步骤上充分传播统计误差，以估计复杂真菌、原核生物或病毒群落的进化历史，2）关于复杂系统发育网络可识别性的第一个统计理论，3）第一个分而治之的算法，以产生迄今为止最可扩展的推理程序，以满足生物大数据不断增长的需求，以及4）在进化生物学中具有广泛适用性的开源易于使用的公开可用软件，该奖项反映了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，被认为值得支持。