miRNAtap (version 1.6.0)

getPredictedTargets: Get aggregated ordered list of predicted targets for miRNA

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 four 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"), 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 frour sources, i.e. c('pictar','diana','targetscan','miranda')

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

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.

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 four default input sources, recommended values are 2 or 3

References

Friedman, R. C., Farh, K. K.-H., Burge, C. B., and Bartel, D. P. (2009). Most mammalian mRNAs are conserved targets of microRNAs. Genome research, 19(1):92-105.

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.

Maragkakis, M., Vergoulis, T., Alexiou, P., Reczko, M., Plomaritou, K., Gousis, M., ... Hatzigeorgiou, A. G. (2011). DIANA-microT Web server upgrade supports Fly and Worm miRNA target prediction and bibliographic miRNA to disease association. Nucleic Acids Research, 39(Web Server issue), W145-8.

Examples

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