Non-parametric correction of estimated gene trees using TRACTION

Area of Science:

• Phylogenetics
• Computational Biology
• Evolutionary Genomics

Background:

• Estimated gene trees often suffer from inaccuracies due to limited phylogenetic signal and biological processes like incomplete lineage sorting (ILS) and horizontal gene transfer (HGT).
• Existing gene tree correction methods primarily address gene duplication and loss (GDL), necessitating new approaches for ILS and HGT scenarios.
• Accurate gene trees are crucial for understanding evolutionary relationships and processes.

Purpose of the Study:

• To develop and evaluate a novel computational method for gene tree correction that accounts for ILS and HGT.
• To introduce TRACTION, an algorithm designed to optimize gene tree accuracy by minimizing Robinson-Foulds (RF) distance.
• To assess TRACTION's performance against established methods in simulated datasets reflecting common evolutionary scenarios.

Main Methods:

• Introduced TRACTION, a polynomial-time algorithm for solving the RF-optimal tree refinement and completion (RF-OTRC) problem for singly-labeled trees.
• Conducted extensive simulations on 68,000 estimated gene trees under varying conditions of ILS and HGT.
• Compared TRACTION's accuracy and speed against established gene tree correction methods.

Main Results:

• TRACTION achieved comparable or superior accuracy to existing methods when both HGT and ILS were present.
• TRACTION tied for the best accuracy under ILS-only conditions.
• TRACTION demonstrated competitive speed, tying for the fastest method in the simulation studies.

Conclusions:

• TRACTION provides an accurate and efficient solution for gene tree correction in the presence of ILS and HGT.
• The method effectively refines estimated gene trees to better reflect species tree topology.
• A direct generalization of the RF-OTRC problem to multi-labeled trees may yield misleading results for GDL-driven heterogeneity.