MTar: a computational microRNA target prediction architecture for human transcriptome

Abstract

BackgroundMicroRNAs (miRNAs) play an essential task in gene regulatory networks by inhibiting the expression of target mRNAs. As their mRNA targets are genes involved in important cell functions, there is a growing interest in identifying the relationship between miRNAs and their target mRNAs. So, there is now a imperative need to develop a computational method by which we can identify the target mRNAs of existing miRNAs. Here, we proposed an efficient machine learning model to unravel the relationship between miRNAs and their target mRNAs.ResultsWe present a novel computational architecture MTar for miRNA target prediction which reports 94.5% sensitivity and 90.5% specificity. We identified 16 positional, thermodynamic and structural parameters from the wet lab proven miRNA:mRNA pairs and MTar makes use of these parameters for miRNA target identification. It incorporates an Artificial Neural Network (ANN) verifier which is trained by wet lab proven microRNA targets. A number of hitherto unknown targets of many miRNA families were located using MTar. The method identifies all three potential miRNA targets (5' seed-only, 5' dominant, and 3' canonical) whereas the existing solutions focus on 5' complementarities alone.ConclusionMTar, an ANN based architecture for identifying functional regulatory miRNA-mRNA interaction using predicted miRNA targets. The area of target prediction has received a new momentum with the function of a thermodynamic model incorporating target accessibility. This model incorporates sixteen structural, thermodynamic and positional features of residues in miRNA: mRNA pairs were employed to select target candidates. So our novel machine learning architecture, MTar is found to be more comprehensive than the existing methods in predicting miRNA targets, especially human transcritome.

Computational Prediction of microRNAs and their Targets

“…4. Base pairing pattern: Base pairing patterns of with miRNA:mRNA duplex on the basis of different types of targets-e 5' dominant seed only targets, 5' dominant canonical targets, and the 3' complimentary targets [71].…”

A Comprehensive Study of Target Prediction Algorithms for Animal MicroRNAs(miRNAs)

“…The target sequence typically resides in the 3'UTR region of the mRNA, although some have been found in the 5'UTRs and in coding regions. The function of a miRNA is ultimately defined by the genes it targets and the effects it has on their expression [10], [5].…”

“…MicroRNAs, which were initially determined as moderate biological modifiers, have now emerged as powerful regulators of diverse cellular processes with important roles in tissue remodeling [9]. It throws light into the causes of diseases like lymphoma, leukemia, cancers and many cardiac problems where miRNA:mRNA pairing is found to play crucial roles [5]. Many computational methods are being developed to identify the relationship between the animal miRNAs and their target mRNAs.…”

HTar: Hidden Markov Model Based MicroRNA Binding Site Prediction

“…But, mutation/insertions in seed region are allowed, provided the free energy level does not increase. Based on base pairing, targets can be divided into three groups, namely 5' dominant seed only targets, 5' dominant canonical targets, and the 3' complimentary targets [8]. In the cases of 5' dominant canonical and 3' complimentary targets, mismatches in seed region are compensated by additional base pairing at 3' end.…”

“…Among the algorithms MiRanda, [10] TargetScan, [11] PicTar [6] and MTar [8] used a test for conserved regions as the initial screening step in the process of target prediction, whereas RNA22 [12] MicroTar [13] and TargetSpy [14] have considered factors other than conservation during this step. TargetScan, miRanda and PicTar perform an extensive search in the 3' UTR of mRNAs for probable targets.…”