CELLO is a subCELlular LOcalization predictive system for Gram-negative bacteria. CELLO utilizes a learning machine, Support Vector Machine, which is based on the multiple n-peptide composition (Yu et al, Proteins: Struct. Funct. Genet. 2003:50:531-536). For multi-class SVM classification, we use the one-against-one (OAO) method. For the 5 classes of subcellular locations, we construct 5*(5-1)/2=10 SVM classifiers and each classifier is trained with proteins from two different subcellular locations. For each penalty parameter and kernel parameter, cross validation combining with the OAO method is used for estimating the performance of the model. Therefore, for each model, 10 decision functions share the same parameter. Each protein in the test set will always get a vote from each binary classifier. We used the jury voting to determine the final assignment of locations to each sequence in the test set. After some preliminary experiments, we settle on the C+D+SP4+F3X5, denoting a combined SVM classifier trained with C, D, F3X5 and the combined training vectors SP4 , respectively. In the end we combine votes from C, D, SP4 and F3X5 for final judgment. In the case of identical votes, we will give more weight to the votes from SP4. C : amino acids composition D : di-peptide amino acids composition SP4 : partitioned amino acids composition F3X5 : slice sequence averagely into five parts, and reduced amino acid composition in which 20 amino acids are classified into four groups (charged, polar, aromatic and nonpolar)