For given mRNA (input is set of mRNA), how can I predict which microRNA will target my set of mRNA? Could you guys give me a hint? Because currently most cases need both mRNA expression file and miRNA database as inputs for orthogonal prediction, but I'm studying in mitochondrial, no miRNA database is available, thus I want to try putative tools as a start.
As per my understanding, miRNA seeds bind to the 3'UTR of mRNA to block the subsequent translation. Human MT(assuming that you organism of interest is Human) genome has single exon genes (without UTR's), so I assume miRNA's will have no expression control over mitochondrial proteins. Also, it would not be possible for miRNA's from other chromosomes to have any role in MT-mRNA::miRNA interaction, since this will need extra carrier activity to transfer miRNA's to mitochondrial compartment, & stabilization of miRNA as mature sequences have smaller half life than mRNA (??) . Irrespective of the biology here (as we don't know the situation particularly regarding mitochondrial genome) , even predictive methods are to be approached step by step.
[EDIT: As suggested by @Khader ]
Most of the TargetPrediction implementations are dependent on seed complementarity & conservation. To make it more sensitive , there have been studies showing usefulness of thermodynamics of mRNA::miRNA interactions (which is more useful in this particular case as MT sequences are highly conserved, might give more FP's than normal). Please go throgh following paper microRNA Target Prediction under Relaxed Seed and No Conservation Requirements. targetThermo is one such implementation (Never tried it myself). Also you can use PITA (it just needs target sequences & mature miRNA sequences - both in fasta format). Of cource one can always use substring matching functions for reverse complemented seed sequences against target sequences (Perl-Python-Shell anything will do).
Hope this helps! Cheers!!
You should look to see if your microRNAs of interest have been detected in the mitochondria. One paper discussing this subject is here. There are others, some dealing with the subject in general or in review, and others dealing with specific microRNAs. If a predicted miR-mRNA interaction involves a miR that has been reported in mitochondrial extracts, you and your colleagues are much more likely to believe it could be real.
After a small discussion with my senior, she gave me a brief reply, and I hope the additional answers will help anyone who came across the similar questions.
To scan the mitochondrial genome for potential miRNA target sites, I suggest to use several independent algorithms like RNA22, Target Scan, miRanda, PITA and miRWalk. miRanda algorithm, which relies on both the complementarity of miRNA and target sequences and on the conservation of the target site. Target Scan algorithm (http://www.targetscan.org/) searches for the presence of conserved target sites that match the seed region of each miRNA and assesses the structural accessibility of the predicted target site. RNA22 is based on the Teiresias algorithm, which relies on a pattern-based approach without using conservation filters. The miRWalk algorithm is based on a computational approach starting with a heptamer seed of miRNA and identifies possible complementary on the complete mitochondrial genome (as input mitochondrial mRNA sequence). A probability distribution of random matches of a subsequence (miRNA 5′ end sequence) in the given sequence was calculated by using Poisson distribution where a low probability implies a significant hit. All predictions algorithms were run under default parameters.
My goal is simple, I want to predict whether miRNA will target given mRNA, just based on prediction, even though the pure mathematical prediction will not be 100% positive. Finally I try PITA for such a simple prediction.
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To complete your answer, you could also suggest an ideal tool for detection seed complementarity.