Back

A first order approximation of a joint Potts and phylogenetic model

Bouckaert, R.

2026-09-25 bioinformatics
10.64898/2026.09.22.753644 bioRxiv
Show abstract

This study introduces a novel model that integrates the capacity of Potts models to capture structural dependencies with the computational efficiency afforded by Felsenstein's algorithm in phylogenetics. While the present work focuses on protein models, the PhyloPotts framework is equally applicable to alignments of discrete states, including RNA, DNA, and 3DI characters. Our findings indicate that, although phylogenetic estimates do not consistently exhibit reduced bias relative to standard models, the approach substantially enhances the accuracy of ancestral sequence reconstruction. The method is freely available via an open source implementation in the PhyloPotts package for BEAST 2 from https://github.com/rbouckaert/potts.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.