getPredictedTargets: Get aggregated ordered list of predicted targets for miRNA

In miRNAtap: miRNAtap: microRNA Targets - Aggregated Predictions

Description

This method performs aggregation of target lists from multiple sources. Aggregated list is more accurate than any list from a single source. Multiple aggregation methods are available.Direct target data from five sources for Human and Mouse is supplied through miRNAtap.db package, for Rat targets are derived through homology translations whenever direct ones are not available.

Usage

getPredictedTargets(mirna, sources = c("pictar", "diana", 
"targetscan", "miranda","mirdb"), species = "mmu", min_src = 2, 
method = "geom", promote = TRUE, synonyms = TRUE, both_strands = FALSE, ...)

Arguments

mirna	miRNA in a standard format
sources	a list of sources to use for aggregation, default is all five sources, i.e. c('pictar','diana','targetscan','miranda','mirdb')
species	species in a standard three-letter acronym, 'mmu' and 'hsa' available as direct targets, 'rno' as homology translations, default 'mmu'
min_src	minimum number of sources required for a target to be considered, default 2
method	method of aggregation - choose from 'min', 'max', and 'geom'; 'min' is a minimum of ranks, 'max' is a maximum of ranks, and default 'geom' is based on geometric mean of the ranks which proves to be the most accurate method.
promote	add weights to improve accuracy of the method, default TRUE
synonyms	when searching for -3p miRNA automatically also searches for miRNA with the same name but ending with * (some databases list -3p miRNA this way) and other way around, similarly for -5p miRNA, default TRUE
both_strands	overrides synonyms and searches for targets of both -5p and -3p strands together
...	any optional arguments

Details

Tuning min_src parameter is an easy way of prioritising precision at the top of the list (high values) or total recall (low values). For the five default input sources, recommended values are 2, 3, or 4.

Value

data.frame object where row names are entrez IDs of target genes, ranks from individual sources and aggregated rank are shown in columns. If no targets are found in any of the sources NULL and a warning are returned.

Author(s)

Maciej Pajak [e-mail]

References

Agarwal V, Bell GW, Nam J, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. eLife, 4:e05005, (2015).

Griffiths-Jones, S., Saini, H. K., van Dongen, S., and Enright, A. J. (2008). miRBase: tools for microRNA genomics. Nucleic acids research, 36(Database issue):D154-8.

Lall, S., Grun, D., Krek, A., Chen, K., Wang, Y.-L., Dewey, C. N., ... Rajewsky, N. (2006). A genome-wide map of conserved microRNA targets in C. elegans. Current biology : CB, 16(5):460-71.

Paraskevopoulou MD, Georgakilas G, Kostoulas N, Vlachos IS, Vergoulis T, Reczko M, Filippidis C, Dalamagas T, Hatzigeorgiou AG., "DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows.", Nucleic Acids Res. 2013 Jul;41(Web Server issue):W169-73.

Wong N and Wang X (2015) miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Research. 43(D1):D146-152.

Examples

targets <- getPredictedTargets('let-7a',species='hsa', method = 'min') 
head(targets) #top of the list with minimum aggregation
targets2 <- getPredictedTargets('let-7a',species='hsa', method='geom') 
head(targets2) #top of the list with geometric mean aggregation

miRNAtap documentation built on Nov. 8, 2020, 8:12 p.m.