Hello,
Thank you for your helpful answer Lori.
I will create an experimentHub package that will contain one fastq file.
I also needed to access to gtf and transcriptome cdna files from ensembl.
In your website I read that publicly available data like gtf and
transcriptome.fa files should not be added to the experimentHub because it is
possible to access to them through the annotationHub.
I easily accessed to the path of one transcriptome file I cas interested to
using these lines of code :
ah = AnnotationHub()
# query the annotation hub
transcriptome_datasets <- query(ah, c("FaFile","Ensembl", "Caenorhabditis
elegans", "Caenorhabditis_elegans.WBcel235.cdna.all.fa"))
# access to local path of the transcriptome dataset
user@transcriptome_path <- transcriptome_datasets[["AH49057"]]$path
I tried to do the same for the annotation GTF file but I can not retrieve the
local path of the file once it is downloaded.
I directly access to the content of the file
ah = AnnotationHub()
# query the annotation hub
annotation_datasets <- query(ah, c("GTF","Ensembl", "Caenorhabditis elegans",
"Caenorhabditis_elegans.WBcel235.84"))
# retrieve dataset locally and keep path to local file
user@annotation_path <- annotation_datasets[["AH50789"]]$path
I have two questions :
- How is it possible to access to the path of each file downloaded from the
annotationHub using AnnotationHub ID?
- Is it normal that I did not find transcriptome of C. elegans more recent than
version 81 of ensembl?
Cheers,
Julien
Le 22.01.19 à 15:13, Shepherd, Lori a écrit :
You could see if there is any existing data already in Bioconductor for use
with your package. That would be preferable.
http://bioconductor.org/packages/release/BiocViews.html#___Software
searching for fastq - you could see what data ShortRead, seqTools, and
FastqCleaner
similarly you could also search for rna-seq packages to see if any of their
data is appropriate.
There are also a number of experiment data packages that may provide the data
format you are in need of.
http://bioconductor.org/packages/release/BiocViews.html#___ExperimentData
You could search here as well.
Lastly, Bioconductor has an experimentHub for storing large data files. You
can search interactively in R or the web API interface here:
https://experimenthub.bioconductor.org/
If none of those location provide data currently in Bioconductor that is
suitable for your package, You can submit your own data to the ExperimentHub.
http://bioconductor.org/packages/devel/bioc/vignettes/ExperimentHub/inst/doc/CreateAnExperimentHubPackage.html
You could download directly but this could be time consuming depending on
internet connections and download speeds. The Bioconductor hubs provide a
caching mechanism so it is only downloaded once and then it remembers where the
file is on the system for later use.
Cheers,
Lori Shepherd
Bioconductor Core Team
Roswell Park Cancer Institute
Department of Biostatistics & Bioinformatics
Elm & Carlton Streets
Buffalo, New York 14263
________________________________
From: Bioc-devel
<bioc-devel-boun...@r-project.org><mailto:bioc-devel-boun...@r-project.org> on
behalf of Julien Wollbrett
<julien.wollbr...@unil.ch><mailto:julien.wollbr...@unil.ch>
Sent: Tuesday, January 22, 2019 8:57:23 AM
To: bioc-devel@r-project.org<mailto:bioc-devel@r-project.org>
Subject: [Bioc-devel] Best practices to load data for vignette/tests
Hi everyone,
I am currently working on a R package called BgeeCall allowing to
automatically generate present/absent expression calls from any RNA-Seq
fastq files as long as the species is present in Bgee (https://bgee.org/)
.
The package is almost ready and I am currently writing the vignette and
some tests.
This package can be seen as a workflow taking as input one transcriptome
and at least one fastq file.
My question is how can I import these 2 files to run the vignette/tests?
They are too big to be part of my package.
Can I directly download them from SRA and ensembl (or from my own
server)? Do I need to create a dataset that will be loaded by my package
for this kind of raw and publicly available data?
Do you know if I could reuse some already existing dataset? I am
interested to any best practices infomation.
Thank you for your answers.
Best Regards,
Julien
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