SVDquest: Improving SVDquartets species tree estimation using exact optimization within a constrained search space.

Area of Science:

• Phylogenomics
• Computational Biology
• Evolutionary Biology

Background:

• Species tree estimation from multi-locus data is challenged by incomplete lineage sorting (ILS), leading to conflicting gene trees.
• Summary methods combining gene trees are popular but sensitive to gene tree estimation errors.
• Site-pattern-based methods avoid gene tree estimation issues.

Purpose of the Study:

• Introduce SVDquest*, a novel method for species tree reconstruction using site patterns.
• Ensure SVDquest* guarantees at least as many compatible quartet trees as the SVDquartets + PAUP* method.
• Evaluate SVDquest*'s performance against leading summary methods like ASTRAL and ASTRID.

Main Methods:

• Utilizes site patterns directly for species tree inference, bypassing gene tree estimation.
• Employs a heuristic search to combine quartet trees derived from site patterns.
• Compares topological accuracy against ASTRAL and ASTRID under varying gene tree error levels.

Main Results:

• SVDquest* is competitive with ASTRAL and ASTRID in topological accuracy.
• SVDquest* demonstrates higher accuracy than ASTRAL and ASTRID when gene tree estimation error is high.
• The method is guaranteed to satisfy at least as many quartet trees as SVDquartets + PAUP*.

Conclusions:

• SVDquest* offers a robust alternative for species tree estimation, particularly when gene tree accuracy is a concern.
• The method provides a reliable approach for phylogenomic studies by leveraging site patterns.
• SVDquest* is available as open-source software for broader scientific application.