A support vector machine based test for incongruence between sets of trees in tree space.

Area of Science:

• Phylogenetics
• Computational Biology
• Evolutionary Biology

Background:

• Multi-locus phylogenetic analyses require methods to identify gene trees deviating from expected patterns.
• Deviations may indicate evolutionary processes such as selection, gene duplication, or horizontal gene transfer.

Purpose of the Study:

• To propose a nonparametric goodness-of-fit test for comparing empirical distributions of gene trees.
• To develop the GeneOut software for estimating p-values for this test.

Main Methods:

• Mapping gene trees into a multi-dimensional vector space.
• Utilizing support vector machines (SVMs) to measure separation between tree sets.
• Employing permutation tests to assess the statistical significance of SVM separation.

Main Results:

• GeneOut successfully detected small differences between gene trees simulated under different species trees.
• The test framework accommodates various phylogenetic reconstruction criteria.
• Receiver operating characteristic (ROC) curves demonstrated GeneOut's accuracy in detecting differences and controlling false positive/negative rates.

Conclusions:

• The nonparametric test offers fast and efficient analyses for diverse evolutionary scenarios.
• GeneOut is applicable to situations where evolutionary factors create distinct multi-dimensional tree distributions.
• The GeneOut software is freely available.