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Bioinformatics. 2016 Apr 1;32(7):1085-7. doi: 10.1093/bioinformatics/btv696. Epub 2015 Nov 26.

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

Author information

1
Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland, Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode, Lausanne, 1015, Switzerland.
2
Department of Computer Science, University of Milan, via Comelico 39, Milan, 20135, Italy and.
3
Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen N, 2200, Denmark.

Abstract

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

CONTACT:

lars.juhl.jensen@cpr.ku.dk

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
26614125
PMCID:
PMC4896368
DOI:
10.1093/bioinformatics/btv696
[Indexed for MEDLINE]
Free PMC Article

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