ngLOC: an n-gram-based Bayesian method for estimating the subcellular proteomes of eukaryotes
1 Department of Computer Science, State University of New York at Albany, Washington Ave, Albany, New York 12222, USA
2 Gen*NY*sis Center for Excellence in Cancer Genomics, State University of New York at Albany, Discovery Drive, Rensselaer, New York 12144-3456, USA
3 Department of Epidemiology and Biostatistics, State University of New York at Albany, Discovery Drive, Rensselaer, New York 12144-3456, USA
Citation and License
Genome Biology 2007, 8:R68 doi:10.1186/gb-2007-8-5-r68Published: 1 May 2007
We present a method called ngLOC, an n-gram-based Bayesian classifier that predicts the localization of a protein sequence over ten distinct subcellular organelles. A tenfold cross-validation result shows an accuracy of 89% for sequences localized to a single organelle, and 82% for those localized to multiple organelles. An enhanced version of ngLOC was developed to estimate the subcellular proteomes of eight eukaryotic organisms: yeast, nematode, fruitfly, mosquito, zebrafish, chicken, mouse, and human.