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Introduction

 

Epitopes Toolkit (EpiT) is a platform for developing epitope prediction tools. An EpiT developer can distribute his predictor as a serialized Java object (model file). This allows other researchers to use the developed predictor on their own machines, rebuild the predictor on other data sets, or combine the predictor with other predictors to obtain a customized hybrid or consensus predictor. EpiT has two main components: i) model builder, an application for building and evaluating epitope predictors and serializing these models in a binary format (model files); ii) predictor, an application for applying a model to test data (e.g., set of epitopes or protein sequences).

Although EpiT was designed for developing epitope prediction tools, some of EpiT components can be used in different sequence classification tasks. For example, EpiT can be used for residue-based classification tasks (e.g., post translational site, protein-protein interface residues, and protein-DNA/RNA interface residue predictions). Moreover, some data pre-processors (filters) in EpiT can be applied to a whole protein sequence allowing for developing basic tools for predicting protein functions, protein subcellular localization, and other protein sequence classifications.

 
REFERENCES

EL-Manzalawy, Y. & Honavar, V. (2014). Building Classifier Ensembles for B-Cell Epitopes Prediction. In De Rajat K. & Tomar, N. (Eds.), Immunoinformatics, Methods in Molecular Biology (pp. 285-294). Springer New York.

El-Manzalawy, Y., & Honavar, V. (2010). A framework for developing epitope prediction tools. In Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology (pp. 660-662). ACM.

EL-Manzalawy, Y., & Honavar, V. (2010). Recent advances in B-cell epitope prediction methods. Immunome Research, 6, 1-9.