Contents

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 New resources

2.1 Family of resources

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. Please read the section “Uploading Data to S3”.

  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.

    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.

    library(AnnotationHub)
    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.

  1. Data objects: Data are not formally part of the software package and are stored separately in AWS S3 buckets. The author should follow instructions in the section “Uploading Data to S3”

  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 Individual resources

Individual objects of a standard class can be added to the hub by providing only the data and metadata files or by creating a package as described in the Family of Resources section.

OrgDb, TxDb and BSgenome objects are well defined Bioconductor classes and methods to download and process these objects already exist in AnnotationHub. When adding only one or two objects the overhead of creating a package may be unnecessary. The goal of the package is to provide structure for metadata generation and makes sense when there are plans to update versions or add new organisms in the future.

Make sure the OrgDb, TxDb or BSgenome object you want to add does not already exist in the Biocondcutor annotation repository

Providing just data and metadata files involves the following steps:

  1. Notify Bioconductor team member: Adding the data to AWS S3 and the metadata to the production database involves assistance from a Bioconductor team member. Please read the section “Uploading Data to S3”.

  2. Prepare the data: In the case of an OrgDb object, only the sqlite file is stored in S3. See makeOrgPackageFromNCBI() and makeOrgPackage() in the AnnotationForge package for help creating the sqlite file. BSgenome objects should be made according to the steps outline in the BSgenome vignette. TxDb objects will be made on-the-fly from a GRanges with GenomicFeatures::makeTxDbFromGRanges() when the resource is downloaded from AnnotationHub. Data should be provided as a GRanges object. See GenomicRanges::makeGRangesFromDataFrame() or rtracklayer::import() for help creating the GRanges.

  3. Generate metadata: Prepare a .R file that generates metadata for the resource(s) by calling the AnnotationHubData::AnnotationHubMetadata() constructor. Argument details are found on the ?AnnotationHubMetadata man page.

As an example, this piece of code generates the metadata for the Vitis vinifera TxDb Timothée Flutre contributed to AnnotationHub:

metadata <- AnnotationHubMetadata(
    Description="Gene Annotation for Vitis vinifera",
    Genome="IGGP12Xv0",
    Species="Vitis vinifera",
    SourceUrl="http://genomes.cribi.unipd.it/DATA/V2/V2.1/V2.1.gff3",
    SourceLastModifiedDate=as.POSIXct("2014-04-17"),
    SourceVersion="2.1",
    RDataPath="community/tflutre/",
    TaxonomyId=29760L,
    Title="Vvinifera_CRIBI_IGGP12Xv0_V2.1.gff3.Rdata",
    BiocVersion=package_version("3.3"),
    Coordinate_1_based=TRUE,
    DataProvider="CRIBI",
    Maintainer="Timothée Flutre <timothee.flutre@supagro.inra.fr",
    RDataClass="GRanges",
    DispatchClass="GRanges",
    SourceType="GFF",
    RDataDateAdded=as.POSIXct(Sys.time()),
    Recipe=NA_character_,
    PreparerClass="None",
    Tags=c("GFF", "CRIBI", "Gene", "Transcript", "Annotation"),
    Notes="chrUn renamed to chrUkn"
)
  1. Add data to S3 and metadata to the database: This last step is done by the Biocondcutor team member.

3 Additional resources

Metadata for new versions of the data can be added to the same package as they become available.

Contact Lori.Shepherd@roswellpark.org or maintainer@bioconductor.org with any questions.

4 Bug fixes

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

4.1 Update the resource

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

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

4.2 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 Lori.Shepherd@roswellpark.org 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

5 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.

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 Lori.Shepherd@roswellpark.org or maintainer@bioconductor.org.

6 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.

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.

7 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.

8 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 Lori.Shepherd@roswellpark.org 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

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, …)

Once the upload is complete, email Lori.Shepherd@roswellpark.org to continue the process