Disjoint Tree Mergers for large-scale maximum-likelihood tree estimation
Park, Minhyuk
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https://hdl.handle.net/2142/110713
Description
Title
Disjoint Tree Mergers for large-scale maximum-likelihood tree estimation
Author(s)
Park, Minhyuk
Issue Date
2021-04-23
Director of Research (if dissertation) or Advisor (if thesis)
Warnow, Tandy J
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
phylogeny estimation
maximum likelihood
RAxML
IQ-TREE
FastTree
cox1
heterotachy
disjoint tree mergers
Tree of Life
Abstract
Gene tree estimation is a biological problem that garners a lot of interest due to its ability to uncover evolutionary relationships in different genes which provides valuable insight into the hidden mechanisms of evolution. However, large-scale gene tree estimation has largely been unexplored, partially due to the limited available methods that can run on large datasets. Instead, a lot of effort has been focused on developing methods that are accurate on small to medium-sized datasets. We present a re-evaluation of divide-and-conquer pipelines on a variety of model conditions, including fragmentary sequences and heterogeneous evolution patterns, and show that our design of divide-and-conquer pipelines can consistently match or outperform FastTree and IQ-TREE with little overhead in runtime while matching or outperforming RAxML except on small datasets with very challenging model conditions. Furthermore, our divide-and-conquer pipeline is able to run on datasets that are too large for IQ-TREE or RAxML to handle.
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