Contents

1 Overview

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 to confirm before proceeding with a single package rather than the accompanied package approach.

2 Setting up a package to use a Hub

2.1 New Hub package

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:

2.1.1 Notify Bioconductor team member

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 does not suffice), you may use the Bioconductor Microsoft Azure Genomic Data Lake. Adding the data to the Data Lake 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 on “Storage of Data Files”.

2.1.2 Building the package

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.

2.1.2.1 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.

2.1.2.2 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.

2.1.2.3 vignettes/

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

2.1.2.4 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).

2.1.2.5 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 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.

2.1.2.6 DESCRIPTION / NAMESPACE

  • 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.

2.1.3 Data objects

Data are not formally part of the software package and are stored separately in a publicly accessible hosted site or by Bioconductor on Microsoft Genomic Data Lakes. The author should read the following section on “Storage of Data Files”.

2.1.4 Confirm Valid Metadata

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.

2.1.5 Package review

Once the data are in Genomic Data Lakes or public site and 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.

2.2 Additional resources to existing Hub 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 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.

  • Make data available: either on publicly accessible site or see section on “Uploading Data to Microsoft Azure Genomic Data Lake”.

  • 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.

  • 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 or ExperimentHub until the metadata are added to the database.

Contact or with any questions.

2.3 Converting a non AnnotationHub annotation package or non ExperimentHub

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

  3. Upload the data to data lake 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 or ExperimentHub for retrieving data.

  5. Push all changes with a version bump back to Bioconductor git.bioconductor.org location

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 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.

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 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 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 chanes 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.

4 Remove resources

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 or 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 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 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.

6 Visibility

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

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 Microsoft Azure Genomic Data Lake or hosting the data elsewhere on a publicly accessible site. See information below and choose the option 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. Github is not an acceptable hosting platform for data.

7.2 Uploading Data to Microsoft Azure Genomic Data Lake

Instead of providing the data files via dropbox, ftp, github, etc. we will grant temporary access to temporary data lakes directory where you can upload your data. Please email to obtain a SAS token for identification.

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 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.

There are a few different options users have for connecting to Microsoft Azure Genomic Data Lake to upload data. All require obtaining either a SAS token or SAS URL from the Bioconductor Core Team by emailing . In the examples below if the token is used, please insert provided sas token for ; similarly if the sas url is used, please insert provided sas url for .

7.2.1 R Interface with R package AzureStor

There is a way to upload data through the R package AzureStor and avoid having to download anything directly on your computer. Most of the documentation here is an adaption of the provided README and documentation provided through the AzureStor package and AzureStor Github.

Open R and load the AzureStor package provided through CRAN:

if (!requireNamespace("AzureStor", quietly = TRUE))
    install.packages("AzureStor")

library("AzureStor")

You will need to connect to the temporary storage location with provided sas credentials:

sas <- <sas token>
url <- "https://bioconductorhubs.blob.core.windows.net"
ep <- storage_endpoint(url, sas = sas)
container <- storage_container(ep, "staginghub")

Now the command to upload will depend on if your data is currently stored locally or in a remote location.

For locally available data use storage_multiupload. If your data files are in a local path /home/user/mypackage/data and assuming the name of your package is mypackage then you would use something like the following call:

files <- dir("/home/user/mypackage/data", recursive=TRUE)
src <- dir("/home/user/mypackage/data", recursive=TRUE, full.names=TRUE)
dest <- paste0("mypackage/", files)
storage_multiupload(container, src=src, dest=dest)

Please make sure the dest value starts with the name of your package.

For data that is currently being stored remotely (github, dropbox, ftp, etc), use copy_url_to_storage or multicopy_url_to_storage. As an example, say the data is store on a public github repository at MyGithub/MyPackage, in a package like directory strucutre where the data is in a data directory.

library(httr)

# get the list of files for the repository
response <- GET("https://api.github.com/repos/MyGithub/MyPackage/git/trees/master?recursive=1")

# get the blob urls and file names
src <- sapply(content(response)$tree, function(elt) elt$url)
names <- sapply(content(response)$tree, function(elt) elt$path)

# filter for the files in the data directory
# if you are uploaded subdirectories filter out the github blob for the
# directory name, subdirectories will be created automatically
keep <- grepl("^data/", names)
src <- src[keep]
names <- names[keep]

# we want the data in a directory with the package name
# the data should only have relevant subdirectories
dest = paste0("MyPackage/", gsub("data/","",names))

# upload to azure
multicopy_url_to_storage(container, src=src, dest=dest)

Keep in mind that github has rate limiting factors. If you have reached a max rate, you might get an error rate limit exceeded. You would have to check your upload to see what uploaded correctly and what is missing. You can also use the argument max_concurrent_transfers to lower the transfer rate.

If you are using AzureStor version > 3.5.2.9000, you have the option of passing an authentication header into the multicopy_url_to_storage function. For github, you would pass a generated Personal Access Token (PAT) with repo level access.

token = <github PAT>
auth_header = paste("token", token)
multicopy_url_to_storage(container, src=src, dest=dest, auth_header = auth_header)

This allows for secure access and will increase the maximum rate github allows.

7.2.2 Command Line via azcopy

The command line interface for upload is through azcopy. Download Microsoft azcopy and unzip/untar. You can choose to add the location of the azcopy executable file on your computer system PATH so that it can be found anywhere otherwise the following examples of utilizing azcopy should include the full path location where the file was unzip/untar. If the directory of data on your system is called MyPackageData, the following command would upload the directory:

azcopy copy --recursive MyPackageData <sas url>

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.)

7.2.3 GUI Interface with Azure Storage Explorer

For a GUI like experience for uploading data, download the Microsoft Azure Storage Explorer.

Once Installed, open the storage explorer and follow the following steps.

  1. The Select Resource window should automatically appear : select Blob Container. If the windows does not automatically appear, see Troubleshooting GUI at the bottom of this section for instructions on how to make this window appear, how to navigate if already logged in with a valid, non-expired sas token, and what to do if your sas token has expired or seeing an older login displaying without access.

  2. In the Select Connection Method window, select Shared access signature URL (SAS) and click Next in the bottom right corner.

  3. In the Enter Connection Info window, Type stainghub into the Display name. And insert the give into the Blob container SAS URL field. Then click Next in the bottom right corner.

  4. On the Summary window, verify and click Connect in the bottom right corner.

You should now see a GUI version of the storage container. staginghub is the temporary location to upload data. This is a shared location and there may be other users data folders located here that are visible to you. Your SAS token allows for list and create options so no user will be able to delete another users data. Please do not put your data into someone else’s folder. 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.)

  1. If your data is already is a directory with your package name, Use the upload folder option. Uploading a Folder will automatically upload any subdirectories if utilized.

    • Choose Upload in the top left and select Upload Folder

    • Navigate to the appropriate folder on your local file system in the Selected folder field.

    • Select Block blob as the Blob type.

    • Leave the Destination directory as /

    • Choose Upload in the bottom right

  2. If your data is not in a directory with your package name:

    • Choose Upload in the top left and select Upload Files

    • Navigate to and select the appropriate files on your local file system. This option will not allow you to select at subdirectories or folders, only files.

    • Select Block blob as the Blob type.

    • Change the Destination directory to your package name.

    • Choose Upload in the bottom right

Troubleshooting GUI

If the connection window did not appear automatically on opening there are a few common issues that might be the cause.

If you know you are not logged into a session, you can click on what looks like a outlet plug to launch the resource connection windows (see beginning of GUI section)

If you have already logged in and are still connected with a valid, non-expired SAS, you can naviage directly to the storage container using the left navigation pane.

  1. Click on Local & Attached to expand.

  2. Click on Storage Accounts to expand.

  3. Click on Attached Containers to expand.

  4. Click on Blob Containers to expand.

  5. You should see the attached staginghub. If you click on it should be accessible. If you get an error about connection authentication or at the bottom left in the properties for Shared Access Signature it says expired, you will have to detach the session and login with a valid SAS URL. To detach, right click on the staginghub in the explorer section and select detach. In the pop up for verification click “Yes”. Relogin with a valid SAS URL by clicking on the picture that looks like an outlet plug in the far.

7.2.4 Utilizing the Bioconductor Docker container

coming soon!

8 Validating

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.

9 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 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:

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 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 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 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 SQLiteConnection SQLiteFile myExperimentPackage/mydatabase.sqlite