You could always look at the data you are currently using and infer from that what is expected, no?
> hg19IdeogramCyto
GRanges object with 862 ranges and 2 metadata columns:
seqnames ranges strand | name gieStain
<Rle> <IRanges> <Rle> | <factor> <factor>
[1] chr1 [ 0, 2300000] * | p36.33 gneg
[2] chr1 [2300000, 5400000] * | p36.32 gpos25
[3] chr1 [5400000, 7200000] * | p36.31 gneg
[4] chr1 [7200000, 9200000] * | p36.23 gpos25
[5] chr1 [9200000, 12700000] * | p36.22 gneg
... ... ... ... ... ... ...
[858] chrY [15100000, 19800000] * | q11.221 gpos50
[859] chrY [19800000, 22100000] * | q11.222 gneg
[860] chrY [22100000, 26200000] * | q11.223 gpos50
[861] chrY [26200000, 28800000] * | q11.23 gneg
[862] chrY [28800000, 59373566] * | q12 gvar
-------
seqinfo: 24 sequences from an unspecified genome; no seqlengths
So you need a GRanges with this extra metadata. Let's look at AnnotationHub.
> library(AnnotationHub)
> hub <- AnnotationHub()
> cyto <- query(hub, c("cytoband"))
> cyto
AnnotationHub with 7 records
# snapshotDate(): 2015-08-26
# $dataprovider: UCSC
# $species: Homo sapiens, Drosophila melanogaster, Mus musculus, Rattus norv...
# $rdataclass: GRanges
# additional mcols(): taxonomyid, genome, description, tags, sourceurl,
# sourcetype
# retrieve records with, e.g., 'object[["AH5012"]]'
title
AH5012 | Chromosome Band
AH5129 | Chromosome Band
AH5292 | Chromosome Band
AH5416 | Chromosome Band
AH6158 | Chromosome Band
AH6379 | Chromosome Band
AH6810 | Chromosome Band
> cyto$species
[1] "Homo sapiens" "Homo sapiens"
[3] "Homo sapiens" "Homo sapiens"
[5] "Mus musculus" "Rattus norvegicus"
[7] "Drosophila melanogaster"
> cyto[[1]]
require(\u201cGenomicRanges\u201d)
retrieving 1 resources
|======================================================================| 100%
UCSC track 'cytoBand'
UCSCData object with 862 ranges and 1 metadata column:
seqnames ranges strand | name
<Rle> <IRanges> <Rle> | <character>
[1] chr1 [ 1, 2300000] * | p36.33
[2] chr1 [2300001, 5400000] * | p36.32
[3] chr1 [5400001, 7200000] * | p36.31
[4] chr1 [7200001, 9200000] * | p36.23
[5] chr1 [9200001, 12700000] * | p36.22
... ... ... ... ... ...
[858] chr22 [37600001, 41000000] * | q13.1
[859] chr22 [41000001, 44200000] * | q13.2
[860] chr22 [44200001, 48400000] * | q13.31
[861] chr22 [48400001, 49400000] * | q13.32
[862] chr22 [49400001, 51304566] * | q13.33
-------
seqinfo: 93 sequences from hg19 genome
There were 33 warnings (use warnings() to see them)
I don' t know what you mean by 'my own species'. That's presumably human, but maybe I am being too literal ;-D. Anyway if you care about human, mouse, rat, or fly, you are like 75% of the way there. All you need is the staining information, which is presumably somewhere that a google search can go.
By 'my own species' I meant my species of research (Arabidopsis thaliana). I'm afraid I still don't see how I can import a dataset, or what format that has to come in. I have looked through the class structure, but it's not one I have seen before. I am looking at the way to construct it from https://www.bioconductor.org/help/workflows/annotation/AnnotatingRanges, but how can it be applied to an organism not in the database?
That is probably not what you want. Instead look at the primer vignette for GenomicFeatures. The basic idea is to have a compact representation (chr, start, end) of the position for something on a genome, as well as metadata (gene location, staining band, whatever) that corresponds to that location.
So to make a GRanges object for making a karyotype for Arabidopsis, you will need to get the (chr, start, end) for each staining region, and then you can create a GRanges object with those data and then use ggbio to make the karyogram. Since this is your species of research, I presume you already have the information you require, and just need to stuff it into a GRanges object.
As an example, we can make a small version of the karyotype data that comes with biovizBase:
> meta <- data.frame(name = c("p36.33","p36.32","p36.31"), gieStain = c("gneg","gpos25","gneg")) > bands <- data.frame(chr = rep("chr1", 3), start = c(0,2300000,5400000), end = c(2300000,5400000,7200000)) > example <- GRanges(bands$chr, IRanges(bands$start, bands$end), name = meta$name, gieStain = meta$gieStain) > example GRanges object with 3 ranges and 2 metadata columns: seqnames ranges strand | name gieStain <Rle> <IRanges> <Rle> | <factor> <factor> [1] chr1 [ 0, 2300000] * | p36.33 gneg [2] chr1 [2300000, 5400000] * | p36.32 gpos25 [3] chr1 [5400000, 7200000] * | p36.31 gneg ------- seqinfo: 1 sequence from an unspecified genome; no seqlengthsPresumably you would have all these data, and could just read in and emulate what I have done here.