Samuel Blanquart | PubMed Health

PubMed2025

FUSE-PhyloTree: linking functions and sequence conservation modules of a protein family through phylogenomic analysis.

SUMMARY: FUSE-PhyloTree is a phylogenomic analysis software for identifying local sequence conservation associated with the different functions of a multi-functional (e.g. paralogous or multi-domain) protein family. FUSE-PhyloTree introduces an original approach that combines advanced sequence analysis with phylogenetic methods. First, local sequence conservation modules within the family are identified using partial local multiple sequence alignment. Next, the evolution of the detected modules and known protein functions is inferred within the family's phylogenetic tree using three-level phylogenetic reconciliation and ancestral state reconstruction. As a result, FUSE-PhyloTree provides a gene tree annotated with both predicted sequence modules and ancestral gene functions, enabling the association of functions with specific sequence regions based on their co-emergence. AVAILABILITY AND IMPLEMENTATION: FUSE-PhyloTree is provided as Docker and Singularity images including all the required software tools. Images, source code, test data, and documentation are available at https://github.com/OcMalde/fuse-phylotree and https://zenodo.org/records/15855068.

Phylogeny↗

PubMed2006

A Bayesian compound stochastic process for modeling nonstationary and nonhomogeneous sequence evolution.

Variations of nucleotidic composition affect phylogenetic inference conducted under stationary models of evolution. In particular, they may cause unrelated taxa sharing similar base composition to be grouped together in the resulting phylogeny. To address this problem, we developed a nonstationary and nonhomogeneous model accounting for compositional biases. Unlike previous nonstationary models, which are branchwise, that is, assume that base composition only changes at the nodes of the tree, in our model, the process of compositional drift is totally uncoupled from the speciation events. In addition, the total number of events of compositional drift distributed across the tree is directly inferred from the data. We implemented the method in a Bayesian framework, relying on Markov Chain Monte Carlo algorithms, and applied it to several nucleotidic data sets. In most cases, the stationarity assumption was rejected in favor of our nonstationary model. In addition, we show that our method is able to resolve a well-known artifact. By Bayes factor evaluation, we compared our model with 2 previously developed nonstationary models. We show that the coupling between speciations and compositional shifts inherent to branchwise models may lead to an overparameterization, resulting in a lesser fit. In some cases, this leads to incorrect conclusions, concerning the nature of the compositional biases. In contrast, our compound model more flexibly adapts its effective number of parameters to the data sets under investigation. Altogether, our results show that accounting for nonstationary sequence evolution may require more elaborate and more flexible models than those currently used.

Animals↗