Welcome to the microRNA-target prediction tools guide

A wide range of MicroRNA-target predictions tools have been proposed so far. This interactive guide provides several simplified mechanisms for prospective end-users to select candidate tools among the large set of publications suitable for a pre-defined research study set up. To this end, an interface to a rich set of features about 98 in silico miRNA-target prediction methods is offered, which is designed to complement the accompanied review article.
Please start by using the buttons below or navigating through the links in the top-bar to refer to individual pages.

Answer a six-step questionnaire to peform tool selection using our pre-defined and recommended scheme.

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Use the full catalogue of tool features to answer an expanded questionnaire where you can select each question that should appear next until you met required conditions.

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Directly access the full-featured table containing more than 3000 informative cells.

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How-to cite:
If you are using our tool for your research purposes, please cite the respective publication:
Kern, F. et al.
What’s the target: understanding two decades of in silico microRNA-target prediction. Brief Bioinform.. https://doi.org/10.1093/bib/bbz111 (2019).

More materials
Polar tree dendrogram of the hierarchical clustering of microRNA-target prediction tools.
Hierarchical clustering of 98 target prediction methods based on 16 categorical variables illustrated as polar tree dendrogram. Pairwise dissimilarities were calculated using the gower coefficient as distance metric from the CRAN R-package cluster67. Each leaf in the tree is labelled with a corresponding tool name or the first author of the publication if no name is given. Tool labels are colored according to the last year of publication, i.e. original or latest update publication where yellow color indicates very recent and dark blue colors older publications. Partial sub-trees were colored at the cut k = 8 to highlight groups of similar methods. Circular annotations around the tree provide additional information about categories being enriched among even larger groups of tools that possibly span multiple sub-trees.