Title

Authors

Affiliation

Centre for Development of Advanced Computing (C-DAC), Pune University Campus, Ganeshkind, Pune-411 007, India.

Email

jayaramanv@cdac.in; *Corresponding author

Article Type

Server

Date

Received April 11, 2012; Accepted April 16, 2012; Published April 30, 2012

Bacterial lipoproteins have many important functions owing to their essential nature and roles in pathogenesis and represent a class of possible vaccine candidates. The prediction of bacterial lipoproteins from sequence is thus an important task for computational vaccinology. A Support Vector Machines (SVM) based module for predicting bacterial lipoproteins, LIPOPREDICT, has been developed. The best performing sequence model were generated using selected dipeptide composition, which gave 97% accuracy of prediction. The results obtained were compared very well with those of previously developed methods. Here, we describe LIPOPREDICT, a web server for bacterial lipoprotein prediction; available at www.lipopredict.cdac.in

Availabilty

www.lipopredict.cdac.in

Keywords

Bacterial lipoproteins, Support Vector Machine (SVM), compositional features, prediction server

Citation

Kumari et al. Bioinformation 8(8): 394-398(2012)
 

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.