Recent progress on methods for estimating and updating large phylogenies.

Philos Trans R Soc Lond B Biol Sci

Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.

Published: October 2022

AI Article Synopsis

  • The increase in sequence data availability has led biologists to aim for accurate phylogeny estimations for very large datasets, even involving hundreds of thousands of sequences.
  • Constructing these extensive phylogenies involves complex analytical and computational challenges, especially with high quantities of sequences.
  • Recent advancements include innovative methods for multiple sequence alignment, estimating species trees from multi-locus datasets, and integrating new sequences into existing trees, paving the way for future improvements in this field.

Article Abstract

With the increased availability of sequence data and even of fully sequenced and assembled genomes, phylogeny estimation of very large trees (even of hundreds of thousands of sequences) is now a goal for some biologists. Yet, the construction of these phylogenies is a complex pipeline presenting analytical and computational challenges, especially when the number of sequences is very large. In the past few years, new methods have been developed that aim to enable highly accurate phylogeny estimations on these large datasets, including divide-and-conquer techniques for multiple sequence alignment and/or tree estimation, methods that can estimate species trees from multi-locus datasets while addressing heterogeneity due to biological processes (e.g. incomplete lineage sorting and gene duplication and loss), and methods to add sequences into large gene trees or species trees. Here we present some of these recent advances and discuss opportunities for future improvements. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9393559PMC
http://dx.doi.org/10.1098/rstb.2021.0244DOI Listing

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