Escaping from the Felsenstein zone by detecting long branches in phylogenetic data

Area of Science:

• Evolutionary Biology
• Phylogenetics
• Bioinformatics

Background:

• Long branches in phylogenetic trees can disrupt hierarchical character covariation, leading to inaccurate evolutionary relationship estimations.
• Existing methods struggle to identify taxa affected by long-branch attraction due to masked branch lengths and altered branching orders.

Purpose of the Study:

• To develop a simple, tree-independent method for detecting taxa prone to long-branch attraction.
• To improve the accuracy of phylogenetic inference by identifying and mitigating the influence of problematic taxa.

Main Methods:

• The study extends the RASA (Reconciled Average Shape Analysis) framework for phylogenetic data exploration.
• A novel diagnostic tool, the taxon variance plot, is introduced to compare cladistic and phenetic variances contributed by individual taxa.
• Algorithms with polynomial time complexity are employed for efficient identification of long branches.

Main Results:

• Long branches leave distinct footprints in character state distributions, detectable through RASA regression error structures.
• The taxon variance plot effectively identifies taxa whose placement is difficult due to long-branch attraction.
• The method demonstrated efficacy on simulated data and empirical sequence data, confirming its ability to detect long branches.

Conclusions:

• The developed method provides a robust approach for identifying taxa susceptible to long-branch attraction.
• Detecting and addressing the influence of long-branch taxa can significantly enhance the accuracy of evolutionary trees.
• This approach is applicable to various data types, including morphological, molecular, and mixed characters.