2021-01-15
Triplet-based similarity score for fully multilabeled trees with poly-occurring labels
Publication
Publication
Bioinformatics , Volume 37 - Issue 2 p. 178- 184
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.
| Additional Metadata | |
|---|---|
| doi.org/10.1093/bioinformatics/btaa676 | |
| Bioinformatics | |
| Pan-genome Graph Algorithms and Data Integration | |
| Organisation | Evolutionary Intelligence |
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Ciccolella, S., Bernardini, G., Denti, L., Bonizzoni, P., Previtali, M., & Della Vedova, G. (2021). Triplet-based similarity score for fully multilabeled trees with poly-occurring labels. Bioinformatics, 37(2), 178–184. doi:10.1093/bioinformatics/btaa676 |
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