Dicer, an RNase III enzyme, plays vital role in RNA interference pathway, processes both pre-miRNAs (precursor microRNAs) and double-stranded (ds) RNAs to generate effector molecules. Thus it is very important to know true Dicer processing sites in the pre-miRNA molecules to understand the RNA interference pathway.
In this study we have developed a method to predict Dicer processing sites at 5' arm of pre-miRNAs using support vector machine (SVM) We have used the dataset of experimentally validated human miRNA hairpins from miRBase version 13 to extract fourteen nucleotides around Dicer cleavage sites. We have incorporated the nucleotide composition, and binary pattern features of Dicer cleavage site to develop models and these SVM models achieved highest accuracy of 60.72% for mononucleotide, and 66.13% for binary pattern. Furthermore, when we integrated the secondary structure feature of Dicer processing site, predicted by quikfold software, the SVM models achieved a highest accuracy of 82.07% for mononucleotide, and 86.22% for binary pattern. In addition, we also tested our models on an independent dataset that achieved an accuracy of ~78%. This can be implemented to make a better Dicer-substrate, which can trigger the RNAi more efficiently
The main aim of this server is to help users to predict Dicer processing sites in pre-miRNA of human.
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Ahmed, F., Kaundal, R. and Raghava, G.P.S. (2013) PHDcleav: a SVM based method for predicting human Dicer cleavage sites using sequence and secondary structure of miRNA precursors. BMC Bioinformatics 2013, 14(Suppl 14):S9