1 Overview

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 filtered or curated like those in ExperimentHub. Each resource has associated metadata that can be searched through the AnnotationHub client interface.

2 Setting up a package to use AnnotationHub

2.1 New AnnotationHub package

Multiple, related resources are added to AnnotationHub by creating a software package similar to the existing annotation packages. The package itself does not contain data but serves as a light weight wrapper around scripts that generate metadata for the resources added to AnnotationHub.

At a minimum the package should contain a man page describing the resources. Vignettes and additional R code for manipulating the objects are optional.

Creating the package involves the following steps:

  1. Notify Bioconductor team member.
    Man page and vignette examples in the software package will not work until the data are available in AnnotationHub. Adding the data to AWS S3 and the metadata to the production database involves assistance from a Bioconductor team member. The metadata.csv file will have to be created before the data can officially be added to the hub (See inst/extdata section below). Please read the section of “Storage of Data Files”.

  2. Building the software package: 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 AnnotationHub database. 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 AnnotationHubData::makeAnnotationHubMetadata. See ?AnnotationHubData::makeAnnotationHubMetadata for details. Ensure that the above function runs without ERROR.

    If necessary, metadata can be broken up into multiple csv files instead having of all records in a single “metadata.csv”.

  • inst/scripts/

    • make-data.R: A script describing the steps involved in making the data object(s). This includes 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 S3. If data are to be hosted on a personal web site instead of S3, 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 manipultaion this file is not needed.

    • make-metadata.R: A script to make the metadata.csv file located in inst/extdata of the package. See ?AnnotationHubData::makeAnnotationHubMetadata for a description of the metadata.csv file, expected fields and data types. The AnnotationHubData::makeAnnotationHubMetadata() function can be used to validate the metadata.csv file before submitting the package.

  • vignettes/

    OPTIONAL vignette(s) describing analysis workflows.

  • R/

    OPTIONAL functions to enhance data exploration.

  • man/

    • package man page: OPTIONAL. 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.

    • resource man pages: OPTIONAL. Man page(s) should describe the resource (raw data source, processing, QC steps) and demonstrate how the data can be loaded through the AnnotationHub interface. For example, replace "SEARCHTERM*" below with one or more search terms that uniquely identify resources in your package.

    hub <- AnnotationHub()
    myfiles <- query(hub, "SEARCHTERM1", "SEARCHTERM2")
    myfiles[[1]]  ## load the first resource in the list
  • DESCRIPTION / NAMESPACE The scripts used to generate the metadata will likely use functions from AnnotationHub or AnnotationHubData which should be listed in Depends/Imports as necessary. The biocViews should contain terms from AnnotationData and should also contain the term AnnotationHub.

  1. Data objects: Data are not formally part of the software package and are stored separately in a publicly accessible hosted site or by Bioconductor in an AWS S3 buckets. The author should read the following section on “Storage of Data Files”.

  2. Confirm valid metadata: Confirm the data in inst/exdata/metadata.csv are valid by running AnnotationHubData:::makeAnnotationHubMetadata() on your package. Please address and warnings or errors.

  3. Package review: Submit the package to the tracker for review. The primary purpose of the package review is to validate the metadata in the csv file(s). It is ok if the package fails R CMD build and check because the data and metadata are not yet in place. Once the metadata.csv is approved, records are added to the production database. At that point the package man pages and vignette can be finalized and the package should pass R CMD build and check.

2.2 Additional resources to existing AnnotationHub package

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 AnnotationHub. Good practice would be to include the version and / or genome build in the title. If the title is not unique, the AnnotationHub object will list multiple files with the same title. The user will need to use ‘rdatadateadded’ to determine which is the most current.

  • Make data available: either on publicly accessible site or see section on “Uploading Data to S3”

  • 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 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.

  • Bump package version and commit to git

  • Notify 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 until the metadata are added to the database.

Contact or with any questions.

2.3 Converting a non AnnotationHub annotation package

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() on your package.

  2. Add biocViews term “AnnotationHub” to DESCRIPTION

  3. Upload the data to S3 or place on 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 for retrieving data.

3 Bug fixes

A bug fix may involve a change to the metadata, data resource or both.

3.1 Update the resource

  • The replacement resource must have the same name as the original and be at the same location (path).

  • Notify that you want to replace the data and make the files available: see section “Uploading Data to S3”.

3.2 Update the metadata

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.

  • Notify that you want to change the metadata

  • Update make-metadata.R and regenerate the metadata.csv file

  • Bump the package version and commit to git

4 Remove resources

When a resource is removed from 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 AnnotationHub() constructor will not list the resource among the available ids. An attempt to extract the resource with ‘[[’ and the 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 or .

5 Versioning

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 S3 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 AnnotationHub object. The user will can use the ‘rdatadateadded’ metadata field to determine which file is the most current.

6 Visibility

Several metadata fields control which resources are visible when a user invokes AnnotationHub(). Records are filtered based on these criteria:

Once a record is added to AnnotationHub it is visable 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 >= May1, 2017 and in all Bioconductor versions >= 3.6.

A special filter for OrgDb is utilized. 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

7 Storage of Data Files

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. There are two options for storing data: Bioconductor AWS S3 buckets or hosting the data elsewhere on a publicly accessible site. See information below and choose the options that fits best for your situation.

7.1 Hosting Data on a Publicly Accessible Site

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.

7.2 Uploading Data to S3

Instead of providing the data files via dropbox, ftp, etc. we will grant temporary access to an S3 bucket where you can upload your data. Please email for access.

You will be given access to the ‘AnnotationContributor’ user. Ensure that the AWS CLI is installed on your machine. See instructions for installing AWS CLI here. Once you have requested access you will be emailed a set of keys. There are two options to set the profile up for AnnotationContributor

  1. Update your .aws/config file to include the following updating the keys accordingly:
[profile AnnotationContributor]
output = text
region = us-east-1
aws_access_key_id = ****
aws_secret_access_key = ****
  1. If you can’t find the .aws/config file, Run the following command entering appropriate information from above
aws configure --profile AnnotationContributor

After the configuration is set you should be able to upload resources using

# To upload a full directory use recursive:

aws --profile AnnotationContributor s3 cp test_dir s3://annotation-contributor/teset_dir --recursive --acl public-read

# To upload a single file

aws --profile AnnotationContributor s3 cp test_file.txt s3://annotation-contributor/test_file.txt --acl public-read

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, …). Please also do not forget to use the flag --acl public-read; This allows read access to the data file.

Once the upload is complete, email 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.

8 Example metadata.csv file and more information

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 on the package with any/all metadata files, and address any ERRORs that occur. Each data object uploaded to S3 should have an entry in the metadata file. Briefly, a description of the metadata columns required:

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 AWS S3 bucket 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 S3. 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. Let’s say I have a package myAnnotations and I upload two annotation files for dog and cow with information extracted from ensembl to S3. 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 release-95 Canis lupus 9612 true ensembl Bioconductor Maintainer character FilePath myAnnotations/canis_lupus_dingo.ASM325472v1.95.gtf.gz
Cow Annotation Gene Annotation for Bos taurus from ensemble 3.9 Bos taurus GTF release-74 Bos taurus 9913 true ensembl Bioconductor Maintainer character FilePath myAnnotations/Bos_taurus.UMD3.1.74.gtf.gz