Triplet-based similarity score for fully multilabeled trees with poly-occurring labels

• Published in: Bioinformatics Vol. 37; no. 2; pp. 178 - 184
• Main Authors: Ciccolella, Simone, Bernardini, Giulia, Denti, Luca, Bonizzoni, Paola, Previtali, Marco, Della Vedova, Gianluca
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 19-04-2021
• Subjects: Algorithms; Biological Evolution; Original Papers; Phylogeny; Sequence Analysis; Software; Trees
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btaa676

Abstract
Motivation
The latest advances in cancer sequencing, and the availability of a wide range of methods to infer the evolutionary history of tumors, have made it important to evaluate, reconcile and cluster different tumor phylogenies. Recently, several notions of distance or similarities have been proposed in the literature, but none of them has emerged as the golden standard. Moreover, none of the known similarity measures is able to manage mutations occurring multiple times in the tree, a circumstance often occurring in real cases.
Results
To overcome these limitations, in this article, we propose MP3, the first similarity measure for tumor phylogenies able to effectively manage cases where multiple mutations can occur at the same time and mutations can occur multiple times. Moreover, a comparison of MP3 with other measures shows that it is able to classify correctly similar and dissimilar trees, both on simulated and on real data.
Availability and implementation
An open source implementation of MP3 is publicly available at https://github.com/AlgoLab/mp3treesim.
Supplementary information
Supplementary data are available at Bioinformatics online.