title: SVD-phy: improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles.
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.
  
  
  
  The software is available under the open-source BSD license at https://bitbucket.org/andrea/svd-phy
  
  
  
  lars.juhl.jensen@cpr.ku.dk
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland, Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode, Lausanne, 1015, Switzerland.
authors.forename: Andrea
authors.initials: A
authors.lastname: Franceschini
authors.affiliation: Department of Computer Science, University of Milan, via Comelico 39, Milan, 20135, Italy and.
authors.forename: Jianyi
authors.initials: J
authors.lastname: Lin
authors.affiliation: Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland, Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode, Lausanne, 1015, Switzerland.
authors.forename: Christian
authors.initials: C
authors.lastname: von Mering
authors.affiliation: Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen N, 2200, Denmark.
authors.forename: Lars Juhl
authors.initials: LJ
authors.lastname: Jensen
mesh_terms: D000465:Algorithms; D019143:Evolution, Molecular; D010802:Phylogeny; D011506:Proteins; D012984:Software
chemical_list: D011506:Proteins