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MiRNATIP: a SOM-based miRNA-target interactions predictor

Antonino Fiannaca¹, Massimo La Rosa², Laura La Paglia²

¹National Research Council of Italy, ICAR-CNR, via Ugo La Malfa 153, Palermo, 90146, Italy. fiannaca@pa.icar.cnr.it.

BMC Bioinformatics

|February 11, 2017

Abstract

Background:

MicroRNAs (miRNAs) are small non-coding RNA sequences with regulatory functions to post-transcriptional level for several biological processes, such as cell disease progression and metastasis. MiRNAs interact with target messenger RNA (mRNA) genes by base pairing. Experimental identification of miRNA target is one of the major challenges in cancer biology because miRNAs can act as tumour suppressors or oncogenes by targeting different type of targets. The use of machine learning methods for the prediction of the target genes is considered a valid support to investigate miRNA functions and to guide related wet-lab experiments. In this paper we propose the miRNA Target Interaction Predictor (miRNATIP) algorithm, a Self-Organizing Map (SOM) based method for the miRNA target prediction. SOM is trained with the seed region of the miRNA sequences and then the mRNA sequences are projected into the SOM lattice in order to find putative interactions with miRNAs. These interactions will be filtered considering the remaining part of the miRNA sequences and estimating the free-energy necessary for duplex stability.

Results:

We tested the proposed method by predicting the miRNA target interactions of both the Homo sapiens and the Caenorhbditis elegans species; then, taking into account validated target (positive) and non-target (negative) interactions, we compared our results with other target predictors, namely miRanda, PITA, PicTar, mirSOM, TargetScan and DIANA-microT, in terms of the most used statistical measures. We demonstrate that our method produces the greatest number of predictions with respect to the other ones, exhibiting good results for both species, reaching the for example the highest percentage of sensitivity of 31 and 30.5 %, respectively for Homo sapiens and for C. elegans. All the predicted interaction are freely available at the following url: http://tblab.pa.icar.cnr.it/public/miRNATIP/ .

Conclusions:

Results state miRNATIP outperforms or is comparable to the other six state-of-the-art methods, in terms of validated target and non-target interactions, respectively.

Insights

The novel miRNATIP algorithm accurately predicts microRNA (miRNA) targets using Self-Organizing Maps (SOMs), outperforming existing methods in identifying gene interactions for cancer research.

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

MicroRNAs (miRNAs) are key post-transcriptional regulators involved in various biological processes, including cancer progression.
Identifying miRNA targets is crucial for understanding their roles as tumor suppressors or oncogenes.
Experimental validation of miRNA-target interactions is challenging, necessitating computational approaches.

Purpose of the Study:

To develop and validate a novel machine learning algorithm, miRNATIP, for predicting microRNA (miRNA) target interactions.
To improve the accuracy and efficiency of miRNA target prediction compared to existing methods.
To provide a valuable tool for cancer biology research and guide experimental investigations.

Main Methods:

The miRNATIP algorithm utilizes Self-Organizing Maps (SOMs) trained on miRNA seed regions.
mRNA sequences are projected onto the SOM lattice to identify potential miRNA binding sites.
Interactions are further refined by analyzing the full miRNA sequence and estimating duplex stability (free energy).

Main Results:

miRNATIP demonstrated strong performance in predicting miRNA target interactions for both Homo sapiens and Caenorhabditis elegans.
The algorithm achieved high sensitivity, reaching 31% for H. sapiens and 30.5% for C. elegans.
miRNATIP generated a greater number of predictions compared to other state-of-the-art predictors like miRanda, PITA, and TargetScan.

Conclusions:

The miRNATIP algorithm is a robust and effective tool for miRNA target prediction.
It outperforms or is comparable to existing state-of-the-art methods in identifying validated and non-validated interactions.
The freely available miRNATIP predictions can significantly aid researchers in cancer biology and related fields.

Read full analysis

Keywords:

SOM Target prediction mRNA miRNA miRNA-mRNA interactions

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