TERA

TERA reconstructs gene trees by reconciling samples of gene trees (G) with a dated species tree (S) using a parsimony-based reconciliation algorithm to improve gene tree accuracy and distinguish statistically equivalent relationships.

Key Features:

• Species Tree Awareness: Uses a dated species tree (S) alongside a set of gene trees (G) to inform gene tree reconstruction and improve accuracy.
• Parsimony-Based Approach: Employs a parsimony framework that is computationally efficient and requires prior estimates of parameters and statistical support, yielding faster performance than probabilistic methods on large datasets.
• Scoring Scheme: Combines costs for gene duplications, transfers, and losses with an estimate of sequence likelihood to compute the score of the most parsimonious reconciliation (MPR).
• Comprehensive Exploration: Explores all reconciled gene trees that can be amalgamated from a sample of gene trees to consider alternative reconciliations.

Scientific Applications:

• Phylogenomics: Improves reconstruction of gene trees for studies of evolutionary relationships and gene family histories across taxa.
• Horizontal Gene Transfer Assessment: Reduces erroneous inference of transfer events, as demonstrated by a two-thirds reduction in apparent transfers across 1,099 homologous gene families from complete cyanobacterial genomes.
• Method Benchmarking: Achieves accuracy comparable to probabilistic methods on large simulated datasets while providing greater computational efficiency.

Methodology:

Uses a parsimony-based framework that integrates sequence data with a dated species phylogeny (S), computes scores by combining duplication/transfer/loss event costs with an estimate of sequence likelihood to identify the most parsimonious reconciliation (MPR), and systematically explores reconciled gene trees amalgamable from a sample of gene trees (G); the method requires prior parameter estimates and statistical support.