## miRNA-seq / GO enrichment for miRNA targets

### Description

Performs a statistical test for enrichment of Gene Ontology (GO) terms in the predicted gene targets of a list of miRNAs.

### Parameters

- Ontology category (all, biological process, molecular function, cellular component) [biological process]
- P-value threshold (0-1) [0.05]
- Minimum population (1-1000000) [5]
- Conditional testing (yes, no) [yes]
- P-value adjustment method (none, BH, BY) [BH]
- Search for over or under-represented genes (under, over) [over]
- Species (human, mouse, rat) [human]
- Dtabase (PicTar, TargetScan, both) [both]

### Details

This tool takes normalized data with miRNA IDs, fetches the target genes from miRBase, and runs a hypergeometric test for over-representation for GO terms in this list. Analysis can be restricted to GO terms which have a minimum, user-specified number of genes mapped to them (by default 5). The user needs to specify species for the miRNA data. The default behavior is that in order to avoid the identification of directly related GO terms with considerable overlap of genes, this tool uses a method which conditions on all child terms that are themselves significant at a specified p- value cutoff. First the leaves of the graph (those terms with no child terms) are tested. All genes annotated at significant children are then removed before testing the parent terms. This continues until all terms have been tested. It is also possible to run the tests unconditional by setting the appropriate parameter. In that case it is also possible to apply multiple testing correction. For human miRNA data it is possible to specify whether to combine gene predictions from both PicTar and TargetScan databases, in order to decrease the proportion of false positives, or to use each individually.

### Output

Output is a text file and an HTML file with a list of significant categories.