Classification of short biosequences

Classifying sequences is one of the central problems in computational biosciences. Several tools have been released to map an unknown molecular entity to one of the known classes using solely its sequence data. However, all of the existing tools are problem- specific and restricted to an alphabet constrained by relevant biological structure. Here, we introduce TRAINER, a new online tool designed to serve as a generic sequence classification platform to enable users provide their own training data with any al phabet therein defined. TRAINER is implemented by using java programming language. TRAINER allows users to select among several feature representation schemes and supervised machine learning methods with relevant parameters. Trained models can be saved for future use without retraining by other users. Three case studies are reported for effective use of the system for DNA, RNA and protein sequences; Candidate effector prediction, microRNA target prediction and nucleolar localization signal prediction. Biological relevance of the results is discussed.