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piRPred
Provides a multiple kernel-based support vector machine (SVM) algorithm to predict piRNAs. piRPred combines heterogeneous types of piRNA features. It allows to use heterogeneous features, each kernel implementing a class of features. This tool provides specificities: (1) several kernels that represent heterogeneous feature sets are built and used, (2) a new type of feature is explored, (3) the characteristic of piRNAs to occur in clusters on the chromosome is coded in a kernel to use it in a supervised way.
PILFER / PIrna cLuster FindER
Offers a platform dedicated to the detection and prediction of PIWI-interacting RNA (piRNAs), including piRNA clusters, from small RNA sequencing data. PILFER is an open source program that uses both expression and spatial information of the piRNA hits for its clusters prediction. The application first determines regions of the genome with a high expression value from the mapping file and then uses a sliding window approach to form a cluster around the localized region.
Pibomd
Captures the real features of piRNA and other RNA sequences. Pibomd is based on a support vector machine (SVM) algorithm that allows motif discovery for piRNA. It enables prediction of piRNAs in mouse with high specificity and sensitivity when only using 258 motifs appearing frequently. This tool can only use variable-length motifs that frequently appear in RNA sequences as features. It offers a web interface that allows to submit a single small RNA sequence or multiple small RNA sequences.
Piano / piRNA annotation
Allows users to predict piRNAs using piRNA-transposon interaction information. Piano classifies real piRNAs and pseudo piRNAs thank to a support vector machine (SVM) method. It was applied to detect piRNAs for an important rice pest, the rice striped stem borer, Chilo suppressalis. This tool is able to achieve a high sensitivity, specificity and accuracy of over 90 per cent. It is useful for large-scale piRNA prediction from small RNA sequencing data or for genome-wide annotation of piRNAs.
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