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Bioinformatics Advance Access originally published online on February 4, 2005
Bioinformatics 2005 21(10):2522-2524; doi:10.1093/bioinformatics/bti309
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

PSLpred: prediction of subcellular localization of bacterial proteins

Manoj Bhasin , Aarti Garg and G. P. S. Raghava *

Institute of Microbial Technology Sector 39A, Chandigarh, India

*To whom correspondence should be addressed.

Summary: We developed a web server PSLpred for predicting subcellular localization of gram-negative bacterial proteins with an overall accuracy of 91.2%. PSLpred is a hybrid approach-based method that integrates PSI-BLAST and three SVM modules based on compositions of residues, dipeptides and physico-chemical properties. The prediction accuracies of 90.7, 86.8, 90.3, 95.2 and 90.6% were attained for cytoplasmic, extracellular, inner-membrane, outer-membrane and periplasmic proteins, respectively. Furthermore, PSLpred was able to predict ~74% of sequences with an average prediction accuracy of 98% at RI = 5.

Availability: PSLpred is available at http://www.imtech.res.in/raghava/pslpred/

Contact: raghava{at}imtech.res.in

Supplementary information: http://www.imtech.res.in/raghava/pslpred/supl.html


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