translate: Translate between different identifiers...

Description

Translate between different identifiers

Usage

translate(values, from, to, reduce=c("all", "first", "last"), return.list=TRUE, remove.missing=TRUE, simplify=FALSE, ...)

Arguments

values

Vector of annotations that needs translation. Coerced to character vector.

from

Type of annotation values are given in. NB! take care in the orientation of the package, ie. if you have RefSeq annotations, use org.Bt.egREFSEQ2EG or (in some cases) revmap(org.Bt.egREFSEQ).

Desired goal, eg. org.Bt.egENSEMBLPROT. If NULL (default), goal if the packages primary annotation (eg. entrez gene for org.Bt.eg.db). Throws a warning if the organisms in from and to are not the same.

reduce

Reducing method, either return all annotations (one-to-many relation) or the first or last found annotation. The reducing step is applied after translating to the goal: all: returns all annotations first or last: choose first or last of arbitrarily ordered list.

return.list

Logical, when TRUE, returns the translation as a list where names

remove.missing

Logical, whether to remove non-translated values, defaults TRUE.

simplify

Logical, unlists the result. Defaults to FALSE. Usefull when using translate in a lapply or sapply.

...

Additional arguments sent to pickGO if from returns GO set.

Value

values and the elements are the translated elements, or NULL if not translatable with remove.missing = TRUE.

Details

Function for translating from one annotation to another, eg. from RefSeq to Ensemble. This function takes a vector of annotation values and translates first to the primary annotation in the Biocore Data Team package (ie. entrez gene identifier for org.Bt.eg.db) and then to the desired product, while removing non-translated annotations and optionally reducing the result so there is only a one-to-one relation.

If you want to do some further mapping on the result, you will have to use either unlist og lapply, where the first returns all the end-products of the first mapping, returning a new list, and the latter produces a list-within-list.

If from returns GO identifiers (e.g. from = org.Bt.egGO), then the returned resultset is more complex and consists of several layers of lists instead of the usual list of character vectors. If to has also been specified, the GO IDs must be extracted (internally) and you have the option of filtering for evidence and category at this point. See pickGO.

Examples

Run this code

genes <- c(280705, 280706, 100327208)
translate(genes, org.Bt.egSYMBOL)

symbols <- c("SERPINA1","KERA","CD5")
refseq <- translate(symbols, from=org.Bt.egSYMBOL2EG, to=org.Bt.egREFSEQ)
# Pick the proteins:
pickRefSeq(refseq, priorities=c('NP','XP'), reduce='all')

# If you wanted do do some further mapping on the result from 
# translate, simply use lapply.

library(GO.db)
GO <- translate(genes, org.Bt.egGO)
# Get all biological processes:
pickGO(GO, category='BP')
# Get all ontologies with experimental evidence:
pickGO(GO, evidence=c('IMP','IGI','IPI','ISS','IDA','IEP','IEA'))