Complete Characterization of Incorrect Orthology Assignments in Best Match Graphs

TL;DR

This paper characterizes false-positive orthology assignments in best match graphs, providing a polynomial-time algorithm to identify most incorrect assignments solely based on graph structure, improving orthology inference accuracy.

Contribution

It introduces a method to detect unambiguous false-positive orthology edges in best match graphs without relying on gene or species trees.

Findings

At least 75% of incorrect orthology assignments can be detected.

Provides a polynomial-time algorithm for identifying false positives.

Results depend only on the structure of best match graphs.

Abstract

Genome-scale orthology assignments are usually based on reciprocal best matches. In the absence of horizontal gene transfer (HGT), every pair of orthologs forms a reciprocal best match. Incorrect orthology assignments therefore are always false positives in the reciprocal best match graph. We consider duplication/loss scenarios and characterize unambiguous false-positive (u-fp) orthology assignments, that is, edges in the best match graphs (BMGs) that cannot correspond to orthologs for any gene tree that explains the BMG. Moreover, we provide a polynomial-time algorithm to identify all u-fp orthology assignments in a BMG. Simulations show that at least $75\%$ of all incorrect orthology assignments can be detected in this manner. All results rely only on the structure of the BMGs and not on any a priori knowledge about underlying gene or species trees.