RT Journal Article
SR Electronic
T1 The Free Lunch is not over yet – Systematic Exploration of Numerical Thresholds in Phylogenetic Inference
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.07.13.499893
DO 10.1101/2022.07.13.499893
A1 Julia Haag
A1 Lukas Hübner
A1 Alexey M. Kozlov
A1 Alexandros Stamatakis
YR 2022
UL http://biorxiv.org/content/early/2022/07/14/2022.07.13.499893.abstract
AB Motivation Maximum Likelihood (ML) is a widely used model for inferring phylogenies. The respective ML implementations heavily rely on numerical optimization routines that use internal numerical thresholds to determine convergence. We systematically analyze the impact of these threshold settings on the log-likelihood (LnL scores) and runtimes for ML tree inferences with RAxML-NG, IQ-TREE, and FastTree on empirical datasets.Results We provide empirical evidence that we can substantially accelerate tree inferences with RAxML-NG and IQ-TREE by changing the default values of two such numerical thresholds. At the same time, changing these settings does not significantly influence the quality of the inferred trees according to statistical significance tests. For RAxML-NG, increasing the likelihood thresholds ϵLnL and ϵbrlen to 10 and 103 respectively results in an average speedup of 1.9 ± 0.6 on Data collection 1 and 1.8 ± 1.3 on Data collection 2. Increasing the likelihood threshold ϵLnL to 10 in IQ-TREE results in an average speedup of 1.3 ± 0.4 on Data collection 1 and 1.3 ± 0.9 on Data collection 2.Availability and Implementation All MSAs and results our analyses are based on are available for download at https://cme.h-its.org/exelixis/material/freeLunch_data.tar.gz. Our data generation scripts are available at https://github.com/tschuelia/ml-numerical-analysis.Contact julia.haag{at}h-its.orgSupplementary information Supplementary data are available online.Competing Interest StatementThe authors have declared no competing interest.