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Showing content from https://bioconductor.org/packages/devel/bioc/vignettes/HubPub/inst/doc/CreateAHubPackage.html below:

Creating A Hub Package: ExperimentHub or AnnotationHub

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:

The 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.

• 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.

• 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.

• One or more vignettes describing analysis workflows or use cases. It could minimally show how to access the resources from the hub.

• 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).

• 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 id

  NOTE: 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 list

• ExperimentHub 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 displayed

• Package 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.

• The 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.

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

1. 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.

2. Add biocViews term “AnnotationHub” or “ExperimentHub” to DESCRIPTION (or “AnnotationHubSoftware”, “ExperimentHubSoftware” if appropriate).

3. Upload the data to a publicly accessible site and remove the data from the package. See the section on “Storage of Data Files” below.

4. Once the data is officially added to the hub, update any code to utilize AnnotationHub or ExperimentHub for retrieving data.

5. 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.

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:

• ‘snapshotdate’ >= the date of the Bioconductor release being used
• ‘rdatadateadded’ >= today’s date
• ‘rdatadateremoved’ is NULL / NA
• ‘biocVersion’ is <= to the Bioconductor version being used

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:

1. 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.

2. 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.

3. 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:

1. username: hubtest1
2. key: 102da5beeebe1339ef50dd9138589d8e46a354d1ad69a7b909f165d265f38a33
3. core team bucket name: userdata

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:

1. username: hubtest1
2. key: 102da5beeebe1339ef50dd9138589d8e46a354d1ad69a7b909f165d265f38a33
3. core team bucket name: userdata

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.

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