IQ-TREE: A Fast and Effec3ve Stochas3c Algorithm for Es3ma3ng - - PowerPoint PPT Presentation

IQ-TREE: A Fast and Effec3ve Stochas3c Algorithm for Es3ma3ng Maximum-Likelihood Phylogenies

Lam-Tung Nguyen, Heiko A. Schmidt, Arndt von Haeseler, Bui Quang Minh Mia Schoening November 27, 2018

Background

• Phylogene3c inference by maximum likelihood

– Es3ma3on of subs3tu3on model parameters, branch lengths, and tree topology

• Finding op3mal tree topology is an NP-hard

combinatorial op3miza3on problem

Exis3ng Methods

• ML tree searches apply local tree rearrangements

to improve current tree

– Nearest neighbor interchange (NNI) – Subtree pruning and regraOing (SPR) – Tree bisec3on and reconnec3on (TBR)

• Only “uphill” moves allowed

– Prone to be stuck in local op3ma

Stochas3c Algorithms

• Introduced to overcome the problem of local
• p3ma encountered by hill-climbing algorithms
• Allow “downhill” moves or maintain a popula3on
• f candidate trees to avoid local op3ma
• Found not to perform as well as SPR-based hill-

climbing algorithms

IQ-TREE

• Fast and effec3ve stochas3c algorithm to find ML

trees

• Perform an efficient sampling of local op3ma in

the tree space

• Best local op3mum found represents the

reported ML tree

NNI Moves

Hill-Climbing NNI

• For a given tree, compute the approximate

likelihoods of each NNI-tree

• Create a list of non-conflic3ng NNIs
• Ini3alize the list with the best NNI
• Add the next best NNI to the list if it does not

conflict with any exis3ng NNI

• Repeat un3l all NNIs have been processed

Hill-Climbing NNI

• Apply all NNIs to current tree and compute likelihood of

resul3ng tree

• If worse than that of the best NNI tree, discard all

topological modifica3ons except that of best NNI in the list

• Replace current tree with new tree with higher likelihood
• Conduct reduced NNI search on the new current tree

– Only compute NNI trees on inner branches at most two branches away from tagged branches – If list is empty, a locally op3mal tree has been found and the hill-climbing search is finished – If list is not empty, combine the reduced NNI search with the beXer tree as described previously

Discussion

• Success of IQ-TREE due to:

– Tree search strategy helps to escape local op3ma – Phylogene3c likelihood library reduces the 3me for the likelihood computa3on

Reference

Lam-Tung Nguyen, Heiko A. Schmidt, Arndt von Haeseler, Bui Quang Minh; IQ-TREE: A Fast and Effec3ve Stochas3c Algorithm for Es3ma3ng Maximum-Likelihood Phylogenies, Molecular Biology and Evolu2on, Volume 32, Issue 1, 1 January 2015, Pages 268-274, hXps://doi.org/10.1093/molbev/msu300