CAREER: Towards Scalable and Robust Inference of Phylogenetic Networks

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

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.