Mirroring co-evolving trees in the light of their topologies
Iman Hajirasouliha, Alexander Sch\"onhuth, David Juan, Alfonso, Valencia, S.Cenk Sahinalp

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…
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Taxonomy
TopicsGenomics and Phylogenetic Studies · Bioinformatics and Genomic Networks · Machine Learning in Bioinformatics
