Heuristic Algorithms for Best Match Graph Editing

TL;DR

This paper develops and evaluates heuristic algorithms for editing best match graphs, which are important in phylogenetics, to correct errors efficiently despite NP-completeness.

Contribution

It introduces novel heuristics based on rooted triples and set partitioning, improving practical BMG editing for biological data analysis.

Findings

Community detection algorithms perform best in BMG editing.

Heuristics based on rooted triples are effective.

Algorithms are consistent, preserving BMG properties.

Abstract

Best match graphs (BMGs) are a class of colored digraphs that naturally appear in mathematical phylogenetics and can be approximated with the help of similarity measures between gene sequences, albeit not without errors. The corresponding graph editing problem can be used as a means of error correction. Since the arc set modification problems for BMGs are NP-complete, efficient heuristics are needed if BMGs are to be used for the practical analysis of biological sequence data. Since BMGs have a characterization in terms of consistency of a certain set of rooted triples, we consider heuristics that operate on triple sets. As an alternative, we show that there is a close connection to a set partitioning problem that leads to a class of top-down recursive algorithms that are similar to Aho's supertree algorithm and give rise to BMG editing algorithms that are consistent in the sense that they leave BMGs invariant. Extensive benchmarking shows that community detection algorithms for the partitioning steps perform best for BMG editing.