Mirroring co-evolving trees in the light of their topologies

TL;DR

This paper introduces a fast, deterministic algorithm for aligning phylogenetic trees to identify protein interaction partners, outperforming existing heuristics in speed and accuracy.

Contribution

The paper presents a novel, efficient algorithm for maximizing tree similarity in phylogenetic analysis, improving over heuristic methods in speed and precision.

Findings

Algorithm is 730 times faster than previous methods

Achieves higher precision than heuristic approaches

Provides a practical implementation available online

Abstract

Determining the interaction partners among protein/domain families poses hard computational problems, in particular in the presence of paralogous proteins. Available approaches aim to identify interaction partners among protein/domain families through maximizing the similarity between trimmed versions of their phylogenetic trees. Since maximization of any natural similarity score is computationally difficult, many approaches employ heuristics to maximize the distance matrices corresponding to the tree topologies in question. In this paper we devise an efficient deterministic algorithm which directly maximizes the similarity between two leaf labeled trees with edge lengths, obtaining a score-optimal alignment of the two trees in question. Our algorithm is significantly faster than those methods based on distance matrix comparison: 1 minute on a single processor vs. 730 hours on a supercomputer. Furthermore we have advantages over the current state-of-the-art heuristic search approach in terms of precision as well as a recently suggested overall performance measure for mirrortree approaches, while incurring only acceptable losses in recall. A C implementation of the method demonstrated in this paper is available at http://compbio.cs.sfu.ca/mirrort.htm