As you already mentioned there are number of prediction programs available: miRnada, microcosm, Targer Scan, Pic Tar. All programs use seed-pairing as a main criteria. PicTar allows perfect and imperfect seed complementarity. Both TargetScana nd PicTar take into account the evolutionary conservation. Target Scan adds a "context score" which improves predictions for non-conserved sites. Those miRNAs that have hight context score + and multiple binding sites have the highest chance to be true targets. miRanda allows for GU wobbles and mismatches and filters results based on the deltaG - e.g, miRanda predictes sites for let7 gene lin-41 which contains a bulge or a GU wobble in a seed region.

I personally run TargetScan+PicTar and look for the common conserved targets to select candidates for validation. Depending on how long is the list of potential targets I look in the first place at those which have multiple sites, 8 or 7mer, and are relevant to my research.

Here is a good paper on that topic: http://www.nature.com/nmeth/journal/v3/n11/full/nmeth954.html

Hi Véronique and everyone

I agree with Anna about the degree of "optimism" of some predictors. To avoid that I would suggest to use the so called "multiple" predictors, which are basically platforms that will scan for several prediction algorithms and present you the results. The more algorithms predict a particular target, the more reliable will be this prediction.

I have compiled some of them in my Mirnatools page.

https://sites.google.com/site/mirnatools2/multiple-predictors

Hope it helps. Best luck

Paco

Thank you very much to everyone for your prompt answers. I will look at these algorithms in combination.

I really appreciate your help,

Regards,

Veronique

I agree with most of above Pacos' site is very handy.

We use the ones he listed in his homepage from which we use most often miRWalk and DIANA. In the miRwalk we use the number of "hits" for each target as a measure of scoring targets for follow up validations.