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Identifies mRNA targets of sRNA regulatory action in bacteria. TargetRNA2 uses several features to identify message targets of sRNA regulation, including conservation of regions of the sRNA, structural accessibility of regions of the sRNA, structural accessibility of regions of the mRNA and energy of hybridization between the two RNAs. When compared to other computational approaches, TargetRNA2 offers improved performance both in terms of the accuracy of its predictions and the speed of its execution.


A target prediction method for prediction of bacterial sRNA targets. The methodology of the program is based on a two-step model of hybridization between an sRNA and a target. In the first step, the sRNA seed binds the target seed by forming a consecutive base-pairing stretch. If the duplex is sufficiently stable, the initial hybrid elongates to form the complete sRNA-target interaction in the second step. Based on the two-step model, sTarPicker first screens seed regions based on an empirical energy value deduced from our training dataset. The program next extends the entire binding site, beginning at the seed regions, mimicking the second step of the model. Through an ensemble classifier trained using the Tclass system, sTarPicker then makes the final prediction regarding whether a sequence represents a target.