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Review
. 2020 Aug 28;10(9):1252.
doi: 10.3390/biom10091252.

Computational Methods and Software Tools for Functional Analysis of miRNA Data

Adrian Garcia-Moreno  1 , Pedro Carmona-Saez  1   2
Affiliations
Review

Computational Methods and Software Tools for Functional Analysis of miRNA Data

Adrian Garcia-Moreno et al. Biomolecules. .

Abstract

miRNAs are important regulators of gene expression that play a key role in many biological processes. High-throughput techniques allow researchers to discover and characterize large sets of miRNAs, and enrichment analysis tools are becoming increasingly important in decoding which miRNAs are implicated in biological processes. Enrichment analysis of miRNA targets is the standard technique for functional analysis, but this approach carries limitations and bias; alternatives are currently being proposed, based on direct and curated annotations. In this review, we describe the two workflows of miRNAs enrichment analysis, based on target gene or miRNA annotations, highlighting statistical tests, software tools, up-to-date databases, and functional annotations resources in the study of metazoan miRNAs.

Keywords: databases; enrichment; functional analysis; miRNA; ncRNA; tools.

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Conflict of interest statement

The authors of this review are responsible for GeneCodis. The authors declare no further conflict of interest.

Figures

Figure 1
Figure 1
Overview of workflow for functional analysis of miRNAs. Given a list of miRNAs, functional annotations can be retrieved via direct (in red) or indirect (in blue) schemas. Direct annotations are obtained from dedicated databases (i.e., MNDR, miRCancer, HMDD, SM2miR), in which functional terms are directly associated with miRNAs. In the indirect annotations, schema miRNAs are annotated with terms associated with target genes via gene-centered databases (i.e., Gene Ontology, KEGG, WikiPathways, HPO). Then, miRNAs are transformed to their target genes using prediction algorithms (TarPMir, TargetScan, mirTarget, microT-CDS) or experimentally validated targets databases (mirTarBase, TarBase). Functional terms associated with miRNAs, can be grouped by an MEA approach before statistical analysis. Different statistical tests can be applied, SEA and MEA use the same tests to evaluate the enrichment of annotations in the input list with respect to the reference list. Alternatively, threshold-free-based approaches from GSEA tests can be used to analyze the annotations distribution in the entire ranked list. Finally, p-values assigned to each annotation can be used to define over-represented and significant annotations.
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