Alexey M. Kozlov

The paper introduces a new method to discretize Gamma rate distributions in phylogenetics, reducing branch length bias compared to the standard DGM.

RAxML-NG 2.0 introduces new features for phylogenetic inference, including automatic model selection and faster branch support metrics, with significant speed improvements.

The paper introduces an adaptive tree search heuristic in RAxML-NG that adjusts search thoroughness based on dataset difficulty, achieving significant speedups while maintaining accuracy.

The paper evaluates phylogenetic inference tools on simulated and empirical data, finding poor performance on difficult alignments and highlighting discrepancies between simulated and empirical accuracy.

This study shows that adjusting numerical thresholds in phylogenetic inference software can significantly speed up tree calculations without affecting tree quality.

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

CellPhy infers single-cell phylogenies from scDNA-seq data using a maximum likelihood framework, showing robustness to errors and superior accuracy and speed compared to existing methods.

The paper evaluates fault tolerance mechanisms in RAxML-NG for phylogenetic inference, showing a 2% average slowdown from recovery mechanisms and 8% with fault-tolerant MPI.

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.

The paper presents a faster and more memory-efficient implementation of the Transfer Bootstrap Expectation method for phylogenetic analysis.

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

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.