Table of Contents
MicroRNAs (miRNAs) are ∼22 nucleotide long endogenous RNA regulators of gene activity at the Post-transcriptional level. Since the discovery of miRNAs in 1993, miRNAs have been identified as key regulators of proliferation, differentiation, and cell death in both normal and aberrant pathways. miRNAs are found in plants and animals and some virus.
miRNA Target Prediction #
Identification and validation of miRNA:mRNA target interactions is the foundation for discerning the role of miRNAs in the broader context of miRNA regulatory networks governing biological processes.
Computational Target predictions #
Validate each miRNA and their targets in the experimental condition are tedious and more expensive in laboratory conditions. So the advent of the computational methods dominates this field of studies, which used to obtain the functions from the regulatory networks. Computational methods development were growing parallel to the miRNA:mRNA interaction mechanism evidences. When you look closely, the prediction mechanism and the targeting regions in the genome were different for plant and animals. Image taken from the wikipedia (http://en.wikipedia.org/wiki/MicroRNA)
In plants, miRNAs were bind in the coding regions and in animals it’s bind to the 3’ untranslated region (3’ UTR). Similar to this, there are so many features were derived for the sequences which are aid to computational prediction and to improve the prediction sensitivity and specificity 1.
Computational methods were developed based on below features #
1. Sequence based methods (Similarity Search for seed regions) 2. Thermodynamics (Free energy/ GIBBs Free energy) 3. Positions of the target
Based on this fundamental the author (Hui Lui et. al)2 derived 113 features for miRNA and 30 features for 3 UTR and developed the SVM based machine learning method to predict the miRNA:mRNA targets. Although the target prediction problems are not able to improve the sensitivity and specificity due to lack of the negative data set. Next levels of methods were also developed with the inclusion of miRNA and mRNA expression patterns 3.
List of few popular tools: #
Command Line: miranda miRNA.fasta 3UTR.fasta -out example2.txt
Output: seed match with energy value
Peterson SM, Thompson JA, Ufkin ML, Sathyanarayana P, Liaw L, Congdon CB: Common features of microRNA target prediction tools. Frontiers in genetics 2014, 5:23.
Liu H, Yue D, Chen Y, Gao SJ, Huang Y: Improving performance of mammalian microRNA target prediction. BMC bioinformatics 2010, 11:476.
Li Y, Goldenberg A, Wong KC, Zhang Z: A probabilistic approach to explore human miRNA targetome by integrating miRNA-overexpression data and sequence information. Bioinformatics 2014, 30(5):621-628.