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Get microRNA targets

Source: R/targets.R
getTargets.Rd

This function allows to obtain human miRNA-target interactions using two databases, namely miRTarBase v9, which contains experimentally validated interactions, and the microRNA Data Integration Portal (mirDIP) database, which aggregates miRNA target predictions from 24 different resources by using an integrated score inferred from different prediction metrics. In this way, as demonstrated by Tokar et al. 2018, mirDIP reports more accurate predictions compared to those of individual tools. However, for species other than Homo sapiens only validated interactions are returned, since mirDIP is only available for human miRNAs.

Usage

getTargets(
  mirnaObj,
  organism = "Homo sapiens",
  score = "High",
  includeValidated = TRUE,
  evidence = "all"
)

Arguments

mirnaObj

A MirnaExperiment object containing miRNA and gene data

organism

The specie for which you are retrieving miRNA target genes. Available species are: Homo sapiens (default), Mus musculus, Rattus norvegicus, Arabidopsis thaliana, Bos taurus, Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, Gallus gallus, Sus scrofa

score

The minimum mirDIP confidence score. It must be one of Very High, High (default), Medium, Low, which correspond to ranks among top 1%, top 5% (excluding top 1%), top 1/3 (excluding top 5%) and remaining predictions, respectively

includeValidated

Logical, whether to include validated interactions from miRTarBase or not. Default is TRUE in order to retrieve both predicted and validated targets. Note that for species other than Homo sapines only validated interactions are considered.

evidence

The support evidence required for miRTarBase validated interactions. The possible options are strong, to only include targets with strong experimental support, and all (default) to also include validated interactions with less strong evidence.

Value

A MirnaExperiment object containing miRNA targets stored in the targets slot. Results can be accessed with the mirnaTargets() function.

Details

To define miRNA target genes, we can consider both experimentally validated and computationally predicted interactions. Interactions of the former type are generally preferred, since they are corroborated by biomolecular experiments. However, they are often not sufficient, thus making it necessary to consider the predicted interactions as well. The downside of miRNA target prediction algorithms is the scarce extend of overlap existing between the different tools. To address this issue, several ensemble methods have been developed, trying to aggregate the predictions obtained by different algorithms. Initially, several researchers determined as significant miRNA-target pairs those predicted by more than one tool (intersection method). However, this method is not able to capture an important number of meaningful interactions. Alternatively, other strategies used to merge predictions from several algorithms (union method). Despite identifying more true relationships, the union method leads to a higher proportion of false discoveries. Therefore, other ensemble methods including mirDIP started using other statistics to rank miRNA-target predictions obtained by multiple algorithms. For additional information on mirDIP database and its ranking metric check Tokar et al. 2018 and Hauschild et al. 2023.

This function defines miRNA targets by considering both validated interactions present in miRTarBase (version 9), and predicted interactions identified by mirDIP. Please note that for species other than Homo sapiens, only miRTarBase interactions are available. Further, it is possible to include all validated miRNA-target interactions, or limit the retrieval to interactions with strong supporting evidence.

Note

To access mirDIP database at https://ophid.utoronto.ca/mirDIP/, this function directly use mirDIP API through R.

References

Tomas Tokar and others, mirDIP 4.1—integrative database of human microRNA target predictions, Nucleic Acids Research, Volume 46, Issue D1, 4 January 2018, Pages D360–D370, https://doi.org/10.1093/nar/gkx1144.

Anne-Christin Hauschild and others, MirDIP 5.2: tissue context annotation and novel microRNA curation, Nucleic Acids Research, Volume 51, Issue D1, 6 January 2023, Pages D217–D225, https://doi.org/10.1093/nar/gkac1070.

Hsi-Yuan Huang and others, miRTarBase update 2022: an informative resource for experimentally validated miRNA–target interactions, Nucleic Acids Research, Volume 50, Issue D1, 7 January 2022, Pages D222–D230, https://doi.org/10.1093/nar/gkab1079.

Author

Jacopo Ronchi, jacopo.ronchi@unimib.it

Examples

# \donttest{
# load example MirnaExperiment object
obj <- loadExamples()

# retrieve targets
obj <- getTargets(mirnaObj = obj)
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#> 
#> Loading miRTarBase from cache...
#> Merging predicted and validated results...
#> 13605 miRNA-target pairs have been identified for the 40 differentially expressed miRNAs.

# access targets
tg <- mirnaTargets(obj)

# }