GeneRax

GeneRax infers species-tree-aware, maximum-likelihood gene trees by integrating sequence substitutions and gene-level events (duplication, transfer, loss) to model gene family evolution.


Key Features:

  • Species Tree-Aware Inference: Leverages information from a putative species tree to improve phylogenetic inference for individual homologous gene families.
  • Maximum Likelihood Framework: Employs full likelihood optimization to infer gene trees under an explicit probabilistic model.
  • Comprehensive Event Modeling: Simultaneously accounts for sequence substitutions and gene-level events—duplication, transfer, and loss—within the inference framework.
  • Parallelization: Supports parallel execution for large-scale analyses, demonstrated by completing tree inferences and reconciliations for 1,099 Cyanobacteria families in eight minutes using 512 CPU cores.
  • Performance: Empirical evaluations report it as the fastest method among tested approaches starting from aligned sequences and consistently producing the highest likelihood scores under its model, with simulations showing approximately 90% accuracy relative to true trees by relative Robinson–Foulds distance.

Scientific Applications:

  • Modeling gene family evolution across species: Reconstruction of rooted gene trees that incorporate duplication, transfer, and loss for comparative genomics and evolutionary biology studies.

Methodology:

Directly infers rooted phylogenetic trees from per-gene sequence alignments and a rooted, undated species tree, avoiding computation of bootstrap trees and not relying on approximations or heuristics that limit exploration of tree space.

Topics

Details

License:
AGPL-3.0
Tool Type:
command-line tool
Programming Languages:
C++
Added:
11/14/2019
Last Updated:
12/3/2020

Operations

Publications

Morel B, Kozlov AM, Stamatakis A, Szöllősi GJ. GeneRax: A tool for species tree-aware maximum likelihood based gene family tree inference under gene duplication, transfer, and loss. Unknown Journal. 2019. doi:10.1101/779066.