First, one must decide if an ExperimentHub or AnnotationHub package is appropriate.
The AnnotationHubData package provides tools to acquire, annotate, convert
and store data for use in Bioconductor’s AnnotationHub. BED files from the
Encode project, gtf files from Ensembl, or annotation tracks from UCSC, are
examples of data that can be downloaded, described with metadata, transformed
to standard Bioconductor data types, and stored so that they may be
conveniently served up on demand to users via the AnnotationHub client. While
data are often manipulated into a more R-friendly form, the data themselves
retain their raw content and are not normally filtered or curated like those in
ExperimentHub.
Each resource has associated metadata that can be searched through the
AnnotationHub client interface.
ExperimentHubData provides tools to add or modify resources in
Bioconductor’s ExperimentHub. This ‘hub’ houses curated data from courses,
publications, or experiments. It is often convenient to store data to be used in
package examples, testings, or vignettes in the ExperimentHub. The resources can
be files of raw data or more often are R / Bioconductor objects such as
GRanges, SummarizedExperiment, data.frame etc. Each resource has associated
metadata that can be searched through the ExperimentHub client interface.
It is advisable to create a separate package for annotations or experiment data rather than an all encompassing package of data and code. However, it is sometimes understandable to have a Software package that also serves as the package front end for the hubs. Although this is generally not recommended; if you think you have a use case please reach out to hubs@bioconductor.org to confirm before proceeding with a single package rather than the accompanied package approach.
Related resources are added to AnnotationHub or ExperimentHub by
creating a package. The package should minimally contain the resource metadata,
man pages describing the resources, and a vignette. It may also contain supporting
R functions the author wants to provide. This is a similar design to the
existing Bioconductor experimental data packages or annotation packages except
the data is stored in Microsoft Azure Genomic Data Lake or other publicly accessibly
sites (like Amazon S3 buckets or institutional servers) instead of
the data/ or inst/extdata/ directory of the package. This keeps the package
light weight and allows users to download only necessary data files.
Below are the steps required for creating the package and adding new resources:
Bioconductor team memberThe man page and vignette examples in the package will not work until
the data are available in AnnotationHub or ExperimentHub. If you are not
hosting the data on a stable web server (github and dropbox does not suffice), you should
look into a stable option. We highly recommend zenodo; other options can
include cloudflare, S3 buckets, mircorsoft azure data lake, or an institutional
level server. If you do not have access to a secure location, you can reach out
to Bioconductor team member to discuss at hubs@bioconductor.org. Adding data
to the live location will also require reaching out to hubs@bioconductor.org. To
have the data live in the appropriate hub, the metadata.csv file will have to be
created (See inst/extdata section below) and the description file of the package
will need to be accurate.
When a resource is downloaded from one of the hubs the associated package is loaded in the workspace making the man pages and vignettes readily available. Because documentation plays an important role in understanding these resources please take the time to develop clear man pages and a detailed vignette. These documents provide essential background to the user and guide appropriate use the of resources.
Below is an outline of package organization. The files listed are required unless otherwise stated.
inst/extdata/metadata.csv:
This file contains the metadata in the format of one row per resource
to be added to the Hub database (each row corresponds to one data file
uploaded to publically hosted data server). The file should be generated
from the code in inst/scripts/make-metadata.R where the final data are
written out with write.csv(..., row.names=FALSE). The required column
names and data types are specified in
ExperimentHubData::makeExperimentHubMetadata or
AnnotationHubData::makeAnnotationHubMetadata. See
?ExperimentHubData::makeExperimentHubMetadata or
?AnnotationHubData::makeAnnotationHubMetadata for details.
Ensuring that the above function runs without ERROR is also a validation step
for the metadata file.
An example data experiment package metadata.csv file can be found here
If necessary, metadata can be broken up into multiple csv files instead having of all records in a single “metadata.csv”. The requirement is the necessary required columns and using csv format.
inst/scripts/make-data.R:
A script describing the steps involved in making the data object(s). It can be
code, pseudo-code, or text but should include where the original data were
downloaded from, pre-processing, and how the final R object was made. Include
a description of any steps performed outside of R with third party
software. Output of the script should be files on disk ready to be pushed to
data server. If data are to be hosted on a personal web site instead of
Microsoft Azure Genomic Data Lake, this file
should explain any manipulation of the data prior to hosting on the web
site. For data hosted on a public web site with no prior manipulation this
file is not needed. For experimental data objects, it is encouraged to
serialize Data objects with save() with the .rda extension on the filename
but not strictly necessary. If the data is provided in another format an
appropriate loading method may need to be implemented. Please advise when
reaching out for “Uploading Data to Microsoft Azure Genomic Data Lake”.
make-metadata.R:
A script to make the metadata.csv file located in inst/extdata of the
package. See ?ExperimentHubData::makeExperimentHubMetadata or
?AnnotationHubData::makeAnnotationHubMetadata for a description of expected
fields and data types. The ExperimentHubData::makeExperimentHubMetadata() or
AnnotationHubData::makeAnnotationHubMetadata() can be used to validate the
metadata.csv file before submitting the package.
vignettes/R/R/*.R: Optional. Functions to enhance data exploration.For ExperimentHub resources only:
- zzz.R: Optional. You can include a .onLoad() function in a zzz.R file that
exports each resource name (i.e., metadata.csv field title) into a function. This allows the data
to be loaded by name, e.g., resource123().
``` r
.onLoad <- function(libname, pkgname) {
fl <- system.file("extdata", "metadata.csv", package=pkgname)
titles <- read.csv(fl, stringsAsFactors=FALSE)$Title
createHubAccessors(pkgname, titles)
}
```
`ExperimentHub::createHubAccessors()` and
`ExperimentHub:::.hubAccessorFactory()` provide internal
detail. The resource-named function has a single 'metadata'
argument. When metadata=TRUE, the metadata are loaded (equivalent
to single-bracket method on an ExperimentHub object) and when
FALSE the full resource is loaded (equivalent to double-bracket
method).man/package man page: The package man page serves as a landing point and should briefly describe all resources associated with the package. There should be an entry for each resource title either on the package man page or individual man pages. While this is optional, it is strongly recommended.
resource man pages: Resources can be documented on the same page, grouped by common type or have their own dedicated man pages. Man page(s) should describe the resource (raw data source, processing, QC steps) and demonstrate how the data can be loaded through the standard hub interface.
Data can be accessed via the standard ExperimentHub or AnnotationHub interface with single and double-bracket methods. Queries are often useful for finding resources. For example you could replace packagename with the name of this package being developed, e.g.,
library(ExperimentHub)
eh <- ExperimentHub()
myfiles <- query(eh, "PACKAGENAME")
myfiles[[1]] ## load the first resource in the list
myfiles[["EH123"]] ## load by EH idNOTE: As a developer, resources should be accessed within your package using the Hub id, e.g., `myfiles[[“EH123”]].
You can use multiple search queries to further filter resources. For example, replace "SEARCHTERM*" below with one or more search terms that uniquely identify resources in your package.
library(AnnotationHub)
hub <- AnnotationHub()
myfiles <- query(hub, "SEARCHTERM1", "SEARCHTERM2")
myfiles[[1]] ## load the first resource in the listExperimentHub packages only If a .onLoad() function is used to export each resource as a function
also document that method of loading, e.g.,
resourceA(metadata = FALSE) ## data are loaded
resourceA(metadata = TRUE) ## metadata are displayedPackage authors are encouraged to use the ExperimentHub::listResources() and
ExperimentHub::loadResource() functions in their man pages and vignette.
These helpers are designed to facilitate data discovery within a specific
package vs within all of ExperimentHub.
DESCRIPTION / NAMESPACEThe package should depend on and fully import AnnotationHub or ExperimentHub. If using the
suggested .onLoad() function for ExperimentHub, import the utils package in the DESCRIPTION
file and selectively importFrom(utils, read.csv) in the NAMESPACE.
If making an Experiment Data Hub package, the biocViews should contain terms
from
ExperimentData
and should also contain the term ExperimentHub.
If making an Annotation Hub package, the biocViews should contain terms from
AnnotationData
and should also contain the term AnnotationHub.
If the case where a software package was appropriate rather than a separate
annotation or experiment data package, the biocViews term should include only
Software
terms but must include either AnnotationHubSoftware or
ExperimentHubSoftware.
Large data are not formally part of the software package and are stored separately in a publicly accessible hosted site.
When you are satisfied with the representation of your resources in
your metadata.csv (or other aptly named csv file) the Bioconductor team
member will add the metadata to the production database. Confirm the metadata
csv files in inst/extdata/ are valid by by running either
ExperimentHubData::makeExperimentHubMetadata() or
AnnotationHubData::makeAnnotationHubData() on your package. Please address any
warnings or errors.
Once the metadata have been added to the production database the man pages and vignette can be finalized. When the package passes R CMD build and check it can be submitted to the package tracker for review. The package should be submitted without any of the data that is now located remotely. This keeps the package light weight and minimal size while still providing access to key large data files now stored remotely. If the data files were added to the github repository please see removing large data files and clean git tree to remove the large files and reduce package size.
Many times these data package are created as a supplement to a software package. There is a process for submitting multiple package under the same issue.
Metadata for new versions of the data can be added to the same package as they become available.
The titles for the new versions should be unique and not match the title of
any resource currently in the Hub. Good practice would be to
include the version and / or genome build in the title. If the title is
not unique, the AnnotationHub or ExperimentHub object will list multiple
files with the same title. The user will need to use ‘rdatadateadded’ to
determine which is the most current or infer from the id numbers which could
lead to confusion. Let the core team member know if any previously available
version should be “removed” by adding a ‘rdatadateremoved’.
Make data available on a publicly accessible site.
Update make-metadata.R with the new metadata information
Generate a new metadata.csv file. The package should contain metadata for all versions of the data in ExperimentHub or AnnotationHub so the old file should remain. When adding a new version it might be helpful to write a new csv file named by version, e.g., metadata_v84.csv, metadata_85.csv etc. If using a single metadata.csv file, please add new or updated entries to the end of the file.
Bump package version and commit to git
Notify hubs@bioconductor.org that an update is ready and a team member will add the new metadata to the production database; new resources will not be visible in AnnotationHub or ExperimentHub until the metadata are added to the database.
Contact hubs@bioconductor.org or maintainer@bioconductor.org with any questions.
experiment data package to utilizing the Hub.
The concepts and directory structure of the package would stay the same. The main steps involved would be
Restructure the inst/extdata and inst/scripts to include metadata.csv and
make-data.R as described in the section above for creating new packages. Ensure the
metadata.csv file is formatted correctly by running
AnnotationHubData::makeAnnotationHubMetadata() or
ExperimentHubData::makeExperimentHubMetadata() on your package.
Add biocViews term “AnnotationHub” or “ExperimentHub” to DESCRIPTION (or “AnnotationHubSoftware”, “ExperimentHubSoftware” if appropriate).
Upload the data to a publicly accessible site and remove the data from the package. See the section on “Storage of Data Files” below.
Once the data is officially added to the hub, update any code to utilize AnnotationHub or ExperimentHub for retrieving data.
Push all changes with a version bump back to Bioconductor git.bioconductor.org location
A bug fix may involve a change to the metadata, data resource or both.
The replacement resource must have the same name as the original and be at the same location (path).
Notify hubs@bioconductor.org that you want to replace the data and make the files available: see section “Uploading Data to Microsoft Azure Genomic Data Lake”.
If a file is replaced on the data lake directly, the old file will no longer be accessible. This could affect reproducibility of end users’ research if the old file has already been utilized. This approach should be done with caution.
New metadata records can be added for new resources but modifying existing records is discouraged. Record modification will only be done in the case of bug fixes and has to be done manually on the database by a core team member.
Update make-metadata.R and regenerate the metadata.csv file if necessary
Bump the package version and commit to git
Notify hubs@bioconductor.org that you want to change the metadata for resources. The core team member will likely need the current AH/EH ids for the resources that need updating and a summary of what fields in the metadata file changed. NOTE: Large changes to the metadata may require the core team member to remove the resources entirely from the database and re-add resulting in new AH/EH ids.
Removing resources should be done with caution. The intent is that resources in the Hubs be ‘reproducible’ research by providing a stable snapshot of the data. Data made available in Bioconductor version x.y.z should be available for all versions greater than x.y.z. Unfortunately this is not always possible. If you find it necessary to remove data from AnnotationHub/ExperimentHub please contact hubs@bioconductor.org or maintainer@bioconductor.org for assistance.
When a resource is removed from ExperimentHub or AnnotationHub two things happen:
the ‘rdatadateremoved’ field is populated with a date and the ‘status’
field is populated with a reason why the resource is no longer available. Once
these changes are made, the ExperimentHub() or AnnotationHub() constructor
will not list the resource among the available ids. An attempt to extract the resource with
‘[[’ and the EH/AH id will return an error along with the status message. The
function getInfoOnIds() will display metadata information for any resource
including resources still in the database but no longer available.
In general, resources are only removed when they are no longer available (e.g., moved from web location, no longer provided etc.).
To remove a resource from AnnotationHub contact hubs@bioconductor.org
or maintainer@bioconductor.org.
Versioning of resources is handled by the maintainer. If you plan to provide incremental updates to a file for the same organism / genome build, we recommend including a version in the title of the resource so it is easy to distinguish which is most current. We also would recommend when uploading the data to genomic data lake or your publicly accessible site to have a directory structure accounting for versioning.
If you do not include a version, or make the title unique in some way,
multiple files with the same title will be listed in the ExperimentHub or
AnnotationHub object. The user will have to use the ‘rdatadateadded’ metadata field
to determine which file is the most current or try an infer from ids which can
lead to confusion.
Several metadata fields control which resources are visible when a user invokes ExperimentHub()/AnnotationHub(). Records are filtered based on these criteria:
Once a record is added to ExperimentHub/AnnotationHub it is visible from that point forward until stamped with ‘rdatadateremoved’. For example, a record added on May 1, 2017 with ‘biocVersion’ 3.6 will be visible in all snapshots >= May 1, 2017 and in all Bioconductor versions >= 3.6.
A special filter for OrgDb is utilized in AnnotationHub. Only one OrgDb is available per
release/devel cycle. Therefore contributed OrgDb added to a devel cycle are
masked until the following release. There are options for debugging these masked
resources. See ?setAnnotationHubOption
The data should not be included in the package. This keeps the package light weight and quick for a user to install. This allows the user to investigate functions and documentation without downloading large data files and only proceeding with the download when necessary. When at all possible data should be hosted on a publicaly accessible site designated by the package maintainer. If this is not possible contact a core team member at hubs@bioconductor.org to request options for hosting.
Data can be accessed through the hubs from any publicly accessible site. The
metadata.csv file[s] created will need the column Location_Prefix to indicate
the hosted site. See more in the description of the metadata columns/fields
below but as a quick example if the link to the data file is
ftp://mylocalserver/singlecellExperiments/dataSet1.Rds an example breakdown of
the Location_Prefix and RDataPath for this entry in the metadata.csv file
would be ftp://mylocalserver/ for the Location_Prefix and
singlecellExperiments/dataSet1.Rds for the RDataPath. Github and Dropbox are not an
acceptable hosting platform for data. We highly recommend zenodo; other
possiblities include cloudflare, S3 buckets, microsoft data lakes, or possible a
server located at your home institution.
In some cases we may allow access to a Bioconductor Microsoft Azure Genomic
Data Lake. Instead of providing the data files via dropbox, ftp, github, etc. we will grant
temporary access to S3 directory where you can upload your data for
preprocessing.
If interesting in hosting on Bioconductor, please email hubs@bioconductor.org. You should provide the following information:
Description or link to package and description of hosted data. Include why existing data already provided in similar packages or hub is not appropriate and why you would like to host your own.
Why a location like zenodo is not appropriate and/or verification that you do not have access to an institutional level hosting location or something like S3 bucket or Azure is not available.
The number and size of files to be hosted and the total size that is to be uploaded.
Please upload the data with the appropriate directory structure, including subdirectories as necessary (i.e. top directory must be software package name, then if applicable, subdirectories of versions, …).
Once the upload is complete, email hubs@bioconductor.org to continue the process. To add the data officially the data will need to be uploaded and the metadata.csv file will need to be created in the github repository.
In some cases we may allow access to a Bioconductor Microsoft Azure Genomic
Data Lake. Please email hubs@bioconductor.org to obtain necessary
information. Examples below we assume the data on your system is in a directory
call YourLocalDataDir and will use the following that would be provided by core
team:
A helper R package has been created to assist with upload called BiocHubsIngestR; It is currently on github. A contributor can use the following commands in R to upload data:
## install package
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("Bioconductor/BiocHubsIngestR")
## set up authentication
BiocHubsIngestR::auth(username = "hubtest1", password = "102da5beeebe1339ef50dd9138589d8e46a354d1ad69a7b909f165d265f38a33")
## Upload data
BiocHubsIngestR::upload("/Local/Path/To/YourLocalDataDir", bucket="userdata")
In some cases we may allow access to a Bioconductor Microsoft Azure Genomic
Data Lake. Instead of providing the data files via dropbox, ftp, github, etc. we will grant
temporary access to S3 bucket where you can upload your
data for preprocessing. The command line interface for upload is through AWS S3 Command Line
Interface. You should install the AWS CLI on your
machine. Please email hubs@bioconductor.org to obtain necessary
information. Examples below we assume the data on your system is in a directory
call YourLocalDataDir and will use the following that would be provided by core
team:
To set up credentials on your system you would use the command
aws configure --profile <username>. It will take you through prompts for AWS
Access Key Id, AWS Secret Access Key, Default region name, and Default output
format. Using our example information it would be something like the following:
> aws configure --profile hubtest1
AWS Access Key ID: hubtest1
AWS Secret Access Key: 102da5beeebe1339ef50dd9138589d8e46a354d1ad69a7b909f165d265f38a33
Default region name: <leave blank>
Default output format: <leave blank>
You would then be able to access the userdata that was set up for you. You can
use s3 cp to upload data. Use with recursive to upload directories. The
general form will be
aws --profile <username>
--endpoint-url https://<username>.hubsingest.bioconductor.org/
s3 cp --recursive <path to yourlocal directory>
s3://<coreteam bucket name>/<local directory name>So using our example data:
aws --profile hubtest1
--endpoint-url https://hubtest1.hubsingest.bioconductor.org/
s3 cp --recursive /path/to/YourLocalDataDir
s3://userdata/YourLocalDataDir
You can check the upload with s3 ls. With our example data it would look
something like
aws --profile hubtest1
--endpoint-url https://hubtest1.hubsingest.bioconductor.org/
s3 ls --recursive s3://userdata/In general, all files should be in a folder that matches your package name. Only upload data files; subdirectories are optionally okay to include to distguish versions or characteristics of the data (i.e species, tissue types). Do not upload your entire package directory (i.e DESCRIPTION, NAMESPACE, R/, etc.)
Once the upload is complete, email hubs@bioconductor.org to continue the process. To add the data officially the data will need to be uploaded and the metadata.csv file will need to be created in the github repository.
Coming soon!
The best way to validate record metadata is to read inst/extdata/metadata.csv
(or aptly named csv file in inst/extdata) using the
AnnotationHubData::makeAnnotationHubMetadata() or
ExperimentHubData::makeExperimentHubMetadata(). If that is successful the
metadata should be valid and able to be entered into the database.
As described above the metadata.csv file (or multiple metadata.csv files) will
need to be created before the data can be added to the database. To ensure
proper formatting one should run AnnotationHubData::makeAnnotationHubMetadata
or ExperimentHubData::makeExperimentHubMetadata
on the package with any/all metadata files, and address any ERRORs that
occur. Each data object uploaded to data server should have an entry (row) in the metadata
file. Briefly, a description of the metadata columns required:
FilePath that
instead of trying to load the file into R, will only return the path to the
locally downloaded file.Any additional columns in the metadata.csv file will be ignored but could be included for internal reference.
More on Location_Prefix and RDataPath. These two fields make up the complete
file path url for downloading the data file. If using the Bioconductor Microsoft
Azure Genomic Data Lake the Location_Prefix should not be included in the metadata file[s] as this field
will be populated automatically. The RDataPath will be the directory structure
you uploaded to the Data Lake. If you uploaded a directory MyAnnotation/, and that
directory had a subdirectory v1/ that contained two files counts.rds and
coldata.rds, your metadata file will contain two rows and the RDataPaths would
be MyAnnotation/v1/counts.rds and MyAnnotation/v1/coldata.rds. If you
host your data on a publicly accessible site you must include a base url as the
Location_Prefix. If your data file was at
ftp://myinstiututeserver/biostats/project2/counts.rds, your metadata file will
have one row and the Location_Prefix would be ftp://myinstiututeserver/ and
the RDataPath would be biostats/project2/counts.rds.
This is a bad example because these annotations are already in the hubs but it should give you an idea of the format for AnnotationHub. Let’s say I have a package myAnnotations and I upload two annotation files for dog and cow with information extracted from ensembl to Bioconductor’s Data Lake location. You would want the following saved as a csv (comma seperated output) but for easier view we show in a table:
| Title | Description | BiocVersion | Genome | SourceType | SourceUrl | SourceVersion | Species | TaxonomyId | Coordinate_1_based | DataProvider | Maintainer | RDataClass | DispatchClass | RDataPath |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dog Annotation | Gene Annotation for Canis lupus from ensembl | 3.9 | Canis lupus | GTF | ftp://ftp.ensembl.org/pub/release-95/gtf/canis_lupus_dingo/Canis_lupus_dingo.ASM325472v1.95.gtf.gz | release-95 | Canis lupus | 9612 | true | ensembl | Bioconductor Maintainer maintainer@bioconductor.org | character | FilePath | myAnnotations/canis_lupus_dingo.ASM325472v1.95.gtf.gz |
| Cow Annotation | Gene Annotation for Bos taurus from ensemble | 3.9 | Bos taurus | GTF | ftp://ftp.ensembl.org/pub/release-74/gtf/bos_taurus/Bos_taurus.UMD3.1.74.gtf.gz | release-74 | Bos taurus | 9913 | true | ensembl | Bioconductor Maintainer maintainer@bioconductor.org | character | FilePath | myAnnotations/Bos_taurus.UMD3.1.74.gtf.gz |
This is a dummy example but hopefully it will give you an idea of the format for ExperimentHub. Let’s say I have a package myExperimentPackage and I upload two files one a SummarizedExperiments of expression data saved as a .rda and the other a sqlite database both considered simulated data. You would want the following saved as a csv (comma seperated output) but for easier view we show in a table:
| Title | Description | BiocVersion | Genome | SourceType | SourceUrl | SourceVersion | Species | TaxonomyId | Coordinate_1_based | DataProvider | Maintainer | RDataClass | DispatchClass | RDataPath |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Simulated Expression Data | Simulated Expression values for 12 samples and 12000 probles | 3.9 | NA | Simulated | http://mylabshomepage | v1 | NA | NA | NA | http://bioconductor.org/packages/myExperimentPackage | Bioconductor Maintainer maintainer@bioconductor.org | SummarizedExperiment | Rda | myExperimentPackage/SEobject.rda |
| Simulated Database | Simulated Database containing gene mappings | 3.9 | hg19 | Simulated | http://bioconductor.org/packages/myExperimentPackage | v2 | Home sapiens | 9606 | NA | http://bioconductor.org/packages/myExperimentPackage | Bioconductor Maintainer maintainer@bioconductor.org | SQLiteConnection | SQLiteFile | myExperimentPackage/mydatabase.sqlite |