CNR ExploRA

Abstract in atti di convegno, 2015, ENG

MiRNATIP: miRNA-Target Interaction Predictor

Antonino Fiannaca, Laura La Paglia, Massimo La Rosa, Antonio Messina, Riccardo Rizzo, Pietro Storniolo, Mario Tripiciano, Salvatore Vaglica, Alfonso Urso

ICAR-CNR, National Research Council of Italy, viale delle Scienze Ed.11, 920128, Palermo, Italy.

MicroRNAs (miRNAs) are small non coding RNAs with regulatory functions to post-transcriptional level. They play an important role in molecular and cellular mechanisms, thanks to their ability to bind and interact with many RNA messengers (mRNAs) of coding product involved in a wide range of biological pathways, cellular status, and conditions. We present miRNA Target Interaction Predictor (miRNATIP), a Self Organizing Map (SOM) based method for the miRNA target prediction. miRNATIP is composed of four steps (see Fig. 1): in the first step, a set of miRNA seeds (8 nt) is used for the training of a SOM. The second step is the projection of a mRNA sequence over the trained SOM. For this reason, we extracted all the 8-length mRNA fragments through a 8-mer sliding window. The result of this step is, for each neural unit (cluster), a list of miRNA_seed-mRNA_fragment. Each cluster can be considered as a preliminary list of predicted miRNAs-mRNAs interaction. Then we computed a dissimilarity measure based on normalised euclidean distance between the remaining part of both miRNA and mRNA sequences, and we retained only the couples whose distance is below a certain threshold. Finally, in the fourth step we performed another filtering to the miRNA-mRNA interaction list, by computing the free-energy of the miRNA-target site duplex. We tested our method by predicting the miRNA target interactions of the C. elegans and human species. miRNA mature sequences were downloaded from miRBase, while verified 3'UTR mRNA sequences were extracted from Ensembl. Experimentally validated miRNA-target interaction were taken from mirTarBase and Tarbase. We compared our results with other target predictors: PITA, miRanda, TargetScan, Pictar, Diana-microT. Prediction results, in terms of sensitivity and specificity, demonstrated that outperforms or is comparable to the other six state-of-the-art methods, in terms of validated target and non-target interactions, respectively.

Meeting in Biotecnologie, ricerca di base interdisciplinare traslazionale in ambito biomedico, pp. 30–31, Palermo, 17-18/12/2015