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.
DOI: 10.1101/779066