Bacterial small RNA target detection software tools | Non-coding RNA data analysis
Bacterial sRNAs are a class of small regulatory RNAs involved in regulation of expression of a variety of genes. Most sRNAs act in trans via base-pairing with target mRNAs, leading to repression or activation of translation or mRNA degradation.
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 methodology designed to identify bacterial sRNAs by incorporating the knowledge encoded by different sRNA prediction methods and optimally aggregating them as potential predictors. Because some of these methods emphasize specificity, whereas others emphasize sensitivity while detecting sRNAs, their optimal aggregation constitutes trade-off solutions between these two contradictory objectives that enhance their individual merits.
Computes shapes for the query-sequence and compares them to a precalculated shape-index. RNAsifter is a filtering approach that exploits the family specific secondary structure and reduces the number of covariance models (CMs) searches. This approach is based on the use of family shape spectra, query shape spectra, and shape abstraction levels, each of which can be computed in different ways.
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.
A web server for genome-scale prediction of bacterial sRNA targets. sRNATarget is based on a recently published model which uses Naive Bayes method as the supervised method and take RNA secondary structure profile as the feature. It provides a quick and labor-saving way for experimental validation of sRNA targets.
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