I typically get this info from Homo.sapiens. The result is parasitic on
the TxDb that is in there. I don't know how easy it is to swap alternate
TxDb in to get a different build. I think it would make sense to regard
the OrganismDb instances as foundational for this sort of structural data.
On Wed, Jun 3, 2015 at 3:12 PM, Kasper Daniel Hansen <
kasperdanielhansen at gmail.com> wrote:
Let me rephrase this slightly. From one POV the purpose of GenomeInfoDb
is
clean up the seqinfo slot. Currently it does most of the cleaning, but it
does not add seqlengths.
It is clear that seqlengths depends on the version of the genome, but I
will argue so does the seqnames. Of course, for human, chr22 will not
change. But what about the names of all the random contigs? Or for other
organisms, what about going from a draft genome with 10k contigs to a more
completely genome assembled into fewer, larger chromosomes.
I acknowledge that this information is present in the BSgenome packages,
but it seems (to me) to be very appropriate to have them around for
cleaning up the seqinfo slot. For some situations it is not great to
depend on 1 GB> download for something that is a few bytes.
Best,
Kasper
On Wed, Jun 3, 2015 at 3:00 PM, Tim Triche, Jr. <tim.triche at gmail.com>
wrote:
It would be nice (for a number of reasons) to have chromosome lengths
readily available in a foundational package like GenomeInfoDb, so that,
say,
data(seqinfo.hg19)
seqinfo(myResults) <- seqinfo.hg19[ seqlevels(myResults) ]
would work without issues. Is there any particular reason this couldn't
happen for the supported/available BSgenomes? It would seem like a
matter to do
R> library(BSgenome.Hsapiens.UCSC.hg19)
R> seqinfo.hg19 <- seqinfo(Hsapiens)
R> save(seqinfo.hg19,
file="~/bioc-devel/GenomeInfoDb/data/seqinfo.hg19.rda")
and be done with it until (say) the next release or next released
BSgenome. I considered looping through the following BSgenomes
and if it isn't strongly opposed by (everyone) I may still do exactly
that. Seems useful, no?
e.g. for the following 42 builds,
grep("(UCSC|NCBI)", unique(gsub(".masked", "", available.genomes())),
value=TRUE)
[1] "BSgenome.Amellifera.UCSC.apiMel2"
"BSgenome.Btaurus.UCSC.bosTau3"
[3] "BSgenome.Btaurus.UCSC.bosTau4"
"BSgenome.Btaurus.UCSC.bosTau6"
[5] "BSgenome.Btaurus.UCSC.bosTau8" "BSgenome.Celegans.UCSC.ce10"
[7] "BSgenome.Celegans.UCSC.ce2" "BSgenome.Celegans.UCSC.ce6"
[9] "BSgenome.Cfamiliaris.UCSC.canFam2"
"BSgenome.Cfamiliaris.UCSC.canFam3"
[11] "BSgenome.Dmelanogaster.UCSC.dm2"
"BSgenome.Dmelanogaster.UCSC.dm3"
[13] "BSgenome.Dmelanogaster.UCSC.dm6" "BSgenome.Drerio.UCSC.danRer5"
[15] "BSgenome.Drerio.UCSC.danRer6" "BSgenome.Drerio.UCSC.danRer7"
[17] "BSgenome.Ecoli.NCBI.20080805"
"BSgenome.Gaculeatus.UCSC.gasAcu1"
[19] "BSgenome.Ggallus.UCSC.galGal3"
"BSgenome.Ggallus.UCSC.galGal4"
[21] "BSgenome.Hsapiens.NCBI.GRCh38" "BSgenome.Hsapiens.UCSC.hg17"
[23] "BSgenome.Hsapiens.UCSC.hg18" "BSgenome.Hsapiens.UCSC.hg19"
[25] "BSgenome.Hsapiens.UCSC.hg38"
"BSgenome.Mfascicularis.NCBI.5.0"
[27] "BSgenome.Mfuro.UCSC.musFur1"
"BSgenome.Mmulatta.UCSC.rheMac2"
[29] "BSgenome.Mmulatta.UCSC.rheMac3" "BSgenome.Mmusculus.UCSC.mm10"
[31] "BSgenome.Mmusculus.UCSC.mm8" "BSgenome.Mmusculus.UCSC.mm9"
[33] "BSgenome.Ptroglodytes.UCSC.panTro2"
"BSgenome.Ptroglodytes.UCSC.panTro3"
[35] "BSgenome.Rnorvegicus.UCSC.rn4"
"BSgenome.Rnorvegicus.UCSC.rn5"
[37] "BSgenome.Rnorvegicus.UCSC.rn6"
"BSgenome.Scerevisiae.UCSC.sacCer1"
[39] "BSgenome.Scerevisiae.UCSC.sacCer2"
"BSgenome.Scerevisiae.UCSC.sacCer3"
[41] "BSgenome.Sscrofa.UCSC.susScr3"
"BSgenome.Tguttata.UCSC.taeGut1"
Am I insane for suggesting this? It would make things a little easier
rtracklayer, most SummarizedExperiment and SE-derived objects, blah,
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