Dear all, I would like to represent 2 (chromosomal) interactions as Granges data structure: let's say : chr1 1000-1010 site interacting with chr1 5000-5010 chr1 5000-5010 site interacting with chr1 8000-8010 in order to apply the function georm_arch in ggbio for visualization ... I would appreciate any suggestions in this respect. thanks very much, Bogdan [[alternative HTML version deleted]]

Probably the smartest way to do this seems like the 'r3Cseq' package, which comes with an example. But it uses RangedData for now. R> biocLite(c('r3Cseq','BSgenome.Mmusculus.UCSC.mm9')) ## time passes R> require(r3Cseq) R> library(BSgenome.Mmusculus.UCSC.mm9) R> load(system.file("data","example.data.rda",package="r3Cseq")) R> calculateRPM(my.data) R> getInteractions(my.data) R> plotOverviewInteractions(my.data) Another, less smart, idea: add valuesinteractions.GR)$interactsWith as a SimpleList and walk through the list as needed? R> int.GR <- as(foo, 'GenomicRanges') R> showint.GR) ## GRanges with 6 ranges and 1 elementMetadata col: ## seqnames ranges strand | interactsWith ## <rle> <iranges> <rle> | <list> ## A1BG chr19 [ 63549984, 63565932] - | ######## ## NAT2 chr8 [ 18293035, 18303003] + | ######## ## ADA chr20 [ 42681577, 42713790] - | ######## ## CDH2 chr18 [ 23784933, 24011189] - | ######## ## AKT3 chr1 [241718158, 242073207] - | ######## ## LOC100009676 chr3 [102877980, 102880471] + | ######## ## ... R> valuesint.GR)$interactsWith <- SimpleList( c(2,3), c(1,4), c(), c(1), c(6), c(5) ) R> invisible(lapply( valuesint.GR)$interactsWith, function(x) cat(namesint.GR)[x], "\n" ))) ## NAT2 ADA ## A1BG CDH2 ## ## A1BG ## LOC100009676 ## AKT3 Per above, this would be the disrecommended approach, as far as I know. Hope this helps. --t On Tue, Aug 7, 2012 at 10:14 AM, Bogdan Tanasa <tanasa at="" gmail.com=""> wrote: > > Dear all, > > I would like to represent 2 (chromosomal) interactions as Granges data > structure: > > let's say : > chr1 1000-1010 site interacting with chr1 5000-5010 > chr1 5000-5010 site interacting with chr1 8000-8010 > > in order to apply the function georm_arch in ggbio for visualization ... > > I would appreciate any suggestions in this respect. thanks very much, > > Bogdan > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- A model is a lie that helps you see the truth. Howard Skipper