Parsimony analysis of phylogenomic datasets (II): evaluation of PAUP*, MEGA and MPBoot

Abstract

This paper examines the implementation of parsimony methods in the programs PAUP*, MEGA and MPBoot, and compares them with TNT. PAUP* implements standard, well-tested algorithms, and flexible search strategies and options for handling trees; its main drawback is the lack of advanced search algorithms, which makes it difficult to find most parsimonious trees for large and complex datasets. In addition, branch-swapping can be much slower than in TNT for datasets with large numbers of taxa, although this is only occasionally a problem for phylogenomic datasets given that they typically have small numbers of taxa. The parsimony implementation of MEGA has major drawbacks. MEGA often fails to find parsimonious trees because it does not perform all possible branch swapping subtree pruning regrafting (SPR)/tree bisection-reconnection (TBR) rearrangements. It furthermore fails to properly handle ambiguity or multiple equally parsimonious trees, and it uses the same addition sequence for all bootstrap replicates. The latter yields values of group support that depend on the order in which taxa are listed in the dataset. In addition, tree searches are very slow and do not facilitate the exploration of different starting points (as random seed is fixed). MPBoot searches for optimal trees using the ratchet, but it is based on SPR instead of TBR (and only evaluates by default a subset of the SPR rearrangements). MPBoot approximates bootstrap frequencies by first finding a sample of trees and then selecting from those trees for every replicate, without performing a tree-search. The approximation is too rough in many cases, producing serious under-or overestimations of the correct support values and, for most kinds of datasets, slower estimations than can be obtained with TNT. In addition, bootstrapping with PAUP*, MEGA or MPBoot can attribute strong supports to groups that have no support at all under any meaningful concept of support, such as likelihood ratios or Bremer supports. In TNT, this problem is decreased by using the strict consensus tree to represent each replicate, or eliminated entirely by using different approximations of the Bremer support.

Returning to the roots: resolution, reproducibility, and robusticity in the phylogenetic inference of Dissorophidae (Amphibia: Temnospondyli)

“…Other studies not addressed by Marjanović & Laurin (2019) have reported different topologies recovered by heuristic TNT and PAUP* ( e.g ., Kurochkin et al, 2011 ; Han et al, 2016 ; Audo, Barriel & Charbonnier, 2021 ), but assessing whether these too might have failed to obtain all MPTs is beyond the scope of this study. A recent comparison of performance of different parsimony programs on phylogenomic data by Goloboff, Catalano & Torres (2021) noted that PAUP* recovered optimal trees in all datasets but one compared to TNT. Other paleontological studies have recovered the same number and length of MPTs between programs, both with large numbers of MPTs ( e.g ., Spaulding, O’Leary & Gatesy, 2009 ; Ford & Benson, 2020 ) and with small numbers of MPTs ( e.g ., Davesne et al, 2016 ; Villalobos-Segura, Underwood & Ward, 2021 ).…”

Peer Review #1 of "Returning to the roots: resolution, reproducibility, and robusticity in the phylogenetic inference of Dissorophidae (Amphibia: Temnospondyli) (v0.1)"

“…Other studies not addressed by Marjanović & Laurin (2019) have reported different topologies recovered by heuristic TNT and PAUP* (e.g., Kurochkin et al, 2009;Han et al, 2016;Audo, Barriel & Charbonnier, 2021), but assessing whether these too might have failed to obtain all MPTs is beyond the scope of this study. A recent comparison of performance of different parsimony programs on phylogenomic data by Goloboff, Catalano & Torres (2021) noted that PAUP* recovered optimal trees in all datasets but one compared to TNT. Other paleontological studies have recovered the same number and length of MPTs between programs, both with large numbers of MPTs (e.g., Spaulding, O'Leary & Gatesy, 2009;Ford & Benson, 2020) and with small numbers of MPTs (e.g., Davesne et al, 2016;Villalobos-Segura, Underwood & Ward, 2021).…”

An empirical test of the relationship between the bootstrap and likelihood ratio support in maximum likelihood phylogenetic analysis

“…In this regard, although they employ different philosophical foundations, S is the ML analogue of other direct measures of support like Goodman-Bremer (GB) and ratio of explanatory power (REP) values in parsimony analysis (Grant & Kluge, 2007, 2010 and posterior odds ratio in Bayesian statistics (Bolstad, 2007;Grant & Kluge, 2008). As such, it also enjoys the advantageous properties of all those measures that have a clear and direct relationship to optimality, in contrast to clade credibility values (often referred to as posterior probabilities) in Bayesian phylogenetics and BS in parsimony, neither of which assesses the strength of evidence for competing hypotheses (Grant & Kluge, 2008; see also Goloboff et al, 2003;Goloboff et al, 2021).…”