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Issue No.05 - September/October (2011 vol.8)
pp: 1196-1207
Simone Battagliero , IBM Italia S.p.A., GBS BAO Advanced Analytics Services and MBLab, Bari
Giuseppe Puglia , IBM Italia S.p.A., GBS BAO Advanced Analytics Services and MBLab, Bari
Saverio Vicario , Consiglio Nazionale delle Ricerche-Istituto di Tecnologie Biomediche-Sede di Bari, Bari
Francesco Rubino , IBM Italia S.p.A., GBS BAO Advanced Analytics Services and MBLab, Bari
Gaetano Scioscia , IBM Italia S.p.A., GBS BAO Advanced Analytics Services and MBLab, Bari
Pietro Leo , IBM Italia S.p.A., GBS BAO Advanced Analytics Services and MBLab, Bari
The increasing use of phylogeny in biological studies is limited by the need to make available more efficient tools for computing distances between trees. The geodesic tree distance—introduced by Billera, Holmes, and Vogtmann—combines both the tree topology and edge lengths into a single metric. Despite the conceptual simplicity of the geodesic tree distance, algorithms to compute it don't scale well to large, real-world phylogenetic trees composed of hundred or even thousand leaves. In this paper, we propose the geodesic distance as an effective tool for exploring the likelihood profile in the space of phylogenetic trees, and we give a cubic time algorithm, GeoHeuristic, in order to compute an approximation of the distance. We compare it with the GTP algorithm, which calculates the exact distance, and the cone path length, which is another approximation, showing that GeoHeuristic achieves a quite good trade-off between accuracy (relative error always lower than 0.0001) and efficiency. We also prove the equivalence among GeoHeuristic, cone path, and Robinson-Foulds distances when assuming branch lengths equal to unity and we show empirically that, under this restriction, these distances are almost always equal to the actual geodesic.
Analysis of algorithms, phylogeny, tree distance, geodesic, discrete mathematics.
Simone Battagliero, Giuseppe Puglia, Saverio Vicario, Francesco Rubino, Gaetano Scioscia, Pietro Leo, "An Efficient Algorithm for Approximating Geodesic Distances in Tree Space", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.8, no. 5, pp. 1196-1207, September/October 2011, doi:10.1109/TCBB.2010.121
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