convertToOneBasedRanges {ORFik} | R Documentation |
There are 5 ways of doing this 1. Take 5' ends, reduce away rest (5prime) 2. Take 3' ends, reduce away rest (3prime) 3. Tile to 1-mers and include all (tileAll) 4. Take middle point per GRanges (middle) 5. Get original with metacolumns (None)
convertToOneBasedRanges( gr, method = "5prime", addScoreColumn = FALSE, addSizeColumn = FALSE, after.softclips = TRUE )
gr |
GRanges, GAlignment Object to reduce |
method |
the method to reduce, see info. (5prime defualt) |
addScoreColumn |
logical (FALSE), if TRUE, add a score column that sums up the hits per unique range This will make each read unique, so that each read is 1 time, and score column gives the number of hits. A useful compression. If addSizeColumn is FALSE, it will not differentiate between reads with same start and stop, but different length. |
addSizeColumn |
logical (FALSE), if TRUE, add a size column that for each read, that gives original width of read. Useful if you need original read lengths. This takes care of soft clips etc. |
after.softclips |
logical (TRUE), TRUE: include softclips in size |
You can also do multiple at a time, then output is GRangesList, where each list group is the operation (5prime is [1], 3prime is [2] etc)
Many other ways to do this have their own functions, like startSites and stopSites etc. To retain information on original width, set addSizeColumn to TRUE. To compress data, 1 GRanges object per unique read, set addScoreColumn to TRUE. This will give you a score column with how many duplicated reads there were in the specified region.
NOTE: Does not support paired end reads for the moment!
Converted GRanges object
Other utils:
bedToGR()
,
fimport()
,
findFa()
,
fread.bed()
,
optimizeReads()
,
readBam()
,
readWig()