Create Bioconda packages¶

Making packages or scripts available via Bioconda makes them easily available to people using the Conda packaging system. A Conda package is created from a definition called a ‘recipe’, composed primarily of a meta.yaml file to define metadata associated with a package, and a script, build.sh, which installs the software. See https://bioconda.github.io/contributing.html for detailed info.

If a package you’re interested in using is not currently available via Bioconda, you can write a recipe very easily and submit to Bioconda as a PR. Once accepted, the package will be available via the usual ‘conda install’ mechanism. For example we wanted to use the DropletUtils Bioconductor package, and submitted a PR to add it. Likewise, packages of wrapper scripts can be provided through the same mechanism, as described in the following example.

Example: r-seurat-scripts¶

Create and Clone a fork of bioconda-recipes, and from within that clone create a new branch named for your new package (e.g. r-seurat-scripts):

git checkout -b <package name>

You will eventually submit a request for this branch to be merged, once your development is complete.

Then create a directory for a new recipe:

mkdir -p recipes/<package name>

Within that new directory create your meta.yaml and build.sh. For r-seurat-scripts meta.yaml looks like:

{% set version = "0.0.3" %}

package:
  name: r-seurat-scripts
  version: {{ version }}

source:
  url: https://github.com/ebi-gene-expression-group/r-seurat-scripts/archive/v{{ version }}.tar.gz
  sha256: f2dce203cdbec2d1c3e13209925c40318346e9aaa79379901a1017472b841c00

build:
  number: 0
  skip: true  # [win32]
  noarch: generic

requirements:
    host:
        - r-base 3.4.1
    run:
        - r-base 3.4.1
        - r-seurat 2.3.1
        - r-optparse
        - r-workflowscriptscommon

test:
    commands:
        - seurat-read-10x.R --help
        - seurat-create-seurat-object.R --help
        - seurat-normalise-data.R --help
        - seurat-filter-cells.R --help
        - seurat-find-variable-genes.R --help
        - seurat-run-pca.R --help
        - seurat-scale-data.R --help
        - seurat-dim-plot.R --help
        - seurat-find-clusters.R --help
        - seurat-run-tsne.R --help
        - seurat-find-markers.R --help
        - which seurat-get-random-genes.R
        - which r-seurat-scripts-post-install-tests.sh

about:
  home: https://github.com/ebi-gene-expression-group/r-seurat-scripts
  dev_url: https://github.com/ebi-gene-expression-group/r-seurat-scripts
  license: GPL-3
  summary: A set of wrappers for individual components of the Seurat package.
      Functions R packages are hard to call when building workflows outside of R,
      so this package adds a set of simple wrappers with robust argument parsing.
      Intermediate steps are currently mainly serialized R objects, but the
      ultimate objective is to have language-agnostic intermediate formats allowing
      composite workflows using a variety of software packages.
  license_family: GPL
extra:
  recipe-maintainers:
    - MathiasHaudgaard
    - FrodePedersen
    - ArneKr
    - johanneskoester
    - bgruening
    - daler
    - jdblischak

Note that:

The download package is specified under ‘source’. It links to a static version with a checksum (see the bioconda recipe guidelines). There will need to be a tagged release on the source repository to make this possible.
Requirements define the dependencies you need. You’ll need to specify the package you’re wrapping here, as well as dependencies such as r-workflowscriptscommon and r-optparse if you’re writing R wrappers.
The build section as used here (with ‘generic’) identifies the code as non-platform-specific.
There are dependencies ‘pinned’ to specific R versions. This is often a sensible approach to make sure your package continues to work after the default versions of your dependencies are updated.
The test sections runs commands to check the installation has worked. Using –help makes sure that e.g. optparse has loaded correctly, as well as the scripts being installed. DO NOT run any further testing here- Bioconda is only interested in whether the install works, downstream testing will be done by the user post-install.

r-seurat-script’s build.sh script is very simple:

#!/usr/bin/env bash

mkdir -p $PREFIX/bin
cp *.R $PREFIX/bin
cp *.sh $PREFIX/bin
cp *.bats $PREFIX/bin

This simply copies the scripts to conda’s build directory.

With these two files in place you can do a test local install of your Bioconda recipe. Make sure you’re in the directory for your recipe and then:

conda build .
conda install --force --use-local r-seurat-scripts

If you’ve done things correctly this will clone your package repository and install the scripts.

Submitting to Bioconda¶

Before submitting to Bioconda you will need to test the recipe in as close a manner as possible to how Bioconda does, in order to prevent wasting their continuous integration resources with buggy recipes. To do so, follow the bioconda contribution guidelines. Ideally, the CircleCI or mulled-build methods should be used, and will use containers to run the tests. This isn’t always easy to get working, however, so at a minimum use the non-docker Conda method cited in the documentation:

./bootstrap.py --no-docker /tmp/miniconda
source ~/.config/bioconda/activate
bioconda-utils build recipes config.yml --git-range master

Assuming the tests complete successfully, you can follow the instructions to submit a pull request, request review etc. With that process complete, your recipe will become a pakage availabe for installation via Conda.