SVD-phy: Improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles

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Abstract

Summary: A successful approach for predicting functional associations between non-homologous genes is to compare their phylogenetic distributions. We have devised a phylogenetic profiling algorithm, SVD-Phy, which uses truncated singular value decomposition to address the problem of uninformative profiles giving rise to false positive predictions. Benchmarking the algorithm against the KEGG pathway database, we found that it has substantially improved performance over existing phylogenetic profiling methods. Availability and implementation: The software is available under the open-source BSD license at https://bitbucket.org/andrea/svd-phy.

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Franceschini, A., Lin, J., Von Mering, C., & Jensen, L. J. (2016). SVD-phy: Improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles. Bioinformatics, 32(7), 1085–1087. https://doi.org/10.1093/bioinformatics/btv696

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