Benoit Morel

This paper uses machine learning and phylogenetic reconciliation to estimate the evolutionary timeline of Bacteria, linking transitions to aerobic lifestyles to the Great Oxidation Event.

The paper infers the order of amino acid recruitment into the genetic code by analyzing ancestral protein domain frequencies, finding smaller amino acids were added earlier and metal-binding/sulfur-containing amino acids earlier than previously thought.

AleRax estimates gene and species trees together using a probabilistic model of gene duplication, transfer, and loss, achieving higher speed and robustness than existing methods.

Asteroid is a new supertree algorithm that improves robustness to missing data and speed compared to existing methods like ASTRAL and ASTRID.

NetRAX infers maximum likelihood phylogenetic networks without incomplete lineage sorting, efficiently handling larger datasets than existing tools.

SpeciesRax infers species trees from gene family trees, accounting for duplication, transfer, and loss, and is accurate and faster than existing methods.

Treerecs is a phylogenetic software that performs duplication-loss reconciliation to root gene trees and correct alignment uncertainties.

GeneRax infers gene trees using a species tree-aware maximum likelihood approach, outperforming existing tools in accuracy and speed on both simulated and empirical data.

ModelTest-NG is a faster and more feature-rich tool for selecting DNA and protein evolutionary models, with improved accuracy and new functionalities.

The paper introduces HyperPhylo, a tool for efficient data distribution in phylogenetic inference with site repeats, showing improved performance over naive methods.

RAxML-NG improves maximum likelihood phylogenetic inference with better accuracy, speed, and features like terrace detection and transfer bootstrap support.

ParGenes performs parallel model selection and phylogenetic tree inference on thousands of genes using supercomputers, completing 20,000 gene trees in 28 hours.

The paper introduces EPA-O_SCPLOWNGC_SCPLOW, a faster and more scalable phylogenetic placement algorithm for metagenomic data, outperforming existing methods by up to 30 times in sequential mode.