Email updates

Keep up to date with the latest news and content from BMC Research Notes and BioMed Central.

Open Access Technical Note

ngLOC: software and web server for predicting protein subcellular localization in prokaryotes and eukaryotes

Brian R King1, Suleyman Vural2, Sanjit Pandey23, Alex Barteau1 and Chittibabu Guda23*

Author Affiliations

1 Department of Computer Science, Bucknell University, One Dent Drive, Lewisburg, PA, 17837, USA

2 Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA

3 Center for Bioinformatics and Systems Biology, University of Nebraska Medical Center, Omaha, NE 68198, USA

For all author emails, please log on.

BMC Research Notes 2012, 5:351  doi:10.1186/1756-0500-5-351

Published: 10 July 2012

Abstract

Background

Understanding protein subcellular localization is a necessary component toward understanding the overall function of a protein. Numerous computational methods have been published over the past decade, with varying degrees of success. Despite the large number of published methods in this area, only a small fraction of them are available for researchers to use in their own studies. Of those that are available, many are limited by predicting only a small number of organelles in the cell. Additionally, the majority of methods predict only a single location for a sequence, even though it is known that a large fraction of the proteins in eukaryotic species shuttle between locations to carry out their function.

Findings

We present a software package and a web server for predicting the subcellular localization of protein sequences based on the ngLOC method. ngLOC is an n-gram-based Bayesian classifier that predicts subcellular localization of proteins both in prokaryotes and eukaryotes. The overall prediction accuracy varies from 89.8% to 91.4% across species. This program can predict 11 distinct locations each in plant and animal species. ngLOC also predicts 4 and 5 distinct locations on gram-positive and gram-negative bacterial datasets, respectively.

Conclusions

ngLOC is a generic method that can be trained by data from a variety of species or classes for predicting protein subcellular localization. The standalone software is freely available for academic use under GNU GPL, and the ngLOC web server is also accessible at http://ngloc.unmc.edu webcite.

Keywords:
Bayesian method; ngLOC; Protein subcellular localization prediction; N-gram-based approach; Protein sequence classification; Machine learning algorithm