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

TY - JOUR

T1 - SVD-phy

T2 - improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles

AU - Franceschini, Andrea

AU - Lin, Jianyi

AU - von Mering, Christian

AU - Jensen, Lars Juhl

N1 - © The Author(s) 2015. Published by Oxford University Press.

PY - 2016/4/1

Y1 - 2016/4/1

N2 - 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.

AB - 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.

U2 - 10.1093/bioinformatics/btv696

DO - 10.1093/bioinformatics/btv696

M3 - Journal article

C2 - 26614125

SN - 1367-4803

VL - 32

SP - 1085

EP - 1087

JO - Bioinformatics

JF - Bioinformatics

IS - 7

ER -