Guidelines for bioconda recipes

bioconda recipe checklist

  • Source URL is stable (details)

  • sha256 or md5 hash included for source download (details)

  • Appropriate build number (details)

  • .bat file for Windows removed (details)

  • Remove unnecessary comments (details)

  • Adequate tests included (details)

  • Files created by the recipe follow the FSH (details)

  • License allows redistribution and license is indicated in meta.yaml

  • Package does not already exist in the defaults or conda-forge channels with some exceptions (details)

  • Package is appropriate for bioconda (details)

  • If the recipe installs custom wrapper scripts, usage notes should be added to extra -> notes in the meta.yaml.

  • Update 21 Jan 2019: Recipes that contain pure Python packages should be marked as a “noarch” (details).

  • Update 7 Mar 2018: When patching a recipe, please provide details on how you tried to address the problem upstream (details)

Stable urls

While supported by conda, git_url and git_rev are not as stable as a git tarball. Ideally, a github repo should have tagged releases that are accessible as tarballs from the “releases” section of the github repo. Correspondingly, a bitbucket repo should have have tagged versions that are accessible as tarballs from the “Downloads” -> “tags” section of the bitbucket repo.

TODO: additional info on the various R and bioconductor URLs


We support either sha256 or md5 checksums to verify the integrity of the source package. If a checksum is provided alongside the source package, then use that. Otherwise we prefer sha256 over md5.

Use shasum -a 256 ... (or sha256sum or openssl sha256 etc) on a file to compute its sha256 hash, and copy this into the recipe, for example:

wget -O- $URL | shasum -a 256

Likewise use md5sum (Linux) or md5 (OSX) on a file to compute its md5 hash, and copy this into the recipe.

Build numbers

The build number (see conda docs) can be used to trigger a new build for a package whose version has not changed. This is useful for fixing errors in recipes. The first recipe for a new version should always have a build number of 0.

.bat files

When creating a recipe using one of the conda skeleton tools, a .bat file for Windows will be created. Since bioconda does not support Windows and to reduce clutter, please remove these files

Comments in recipes

When creating a recipe using one of the conda skeleton tools, often many comments are included, for example, to point out sections that can be uncommented and used. Please delete all auto-generated comments in meta.yaml and But please add any comments that you feel could help future maintainers of the recipe, especially if there’s something non-standard.

Filesystem Hierarchy Standard

Recipes should conform to the Filesystem Hierarchy Standard (FSH). This is most important for libraries and Java packages; for these cases use one of the recipes below as a guideline.

Existing package exceptions

If a package already exists in one of the dependent channels but is broken or cannot be used as-is, please first consider fixing the package in that channel. If this is not possible, please indicate this in the PR and notify @bioconda/core in the PR.

Packages appropriate for bioconda

Bioconda is a bioinformatics channel, so we prefer to host packages specific to this domain. If a bioinformatics recipe has more general dependencies, please consider opening a pull request with conda-forge which hosts general packages.

All CRAN packages that do not depend on a package in bioconda should be added to conda-forge instead. This is still the case if the CRAN package is directly related to bioinformatics.

If uploading of an unreleased version is necessary, please follow the versioning scheme of conda for pre- and post-releases (e.g. using a, b, rc, and dev suffixes, see here).

“Noarch” packages

A “Noarch” packages package must be created for pure python packages. To do so, add noarch: python to the build section of the meta.yaml file.

For other generic packages (like a data package), add noarch: generic to

the build section.


There is currently no mechanism to define, in the meta.yaml file, that a particular dependency should come from a particular channel. This means that a recipe must have its dependencies in one of the following:

  • as-yet-unbuilt recipes in the repo but that will be included in the PR

  • conda-forge channel

  • bioconda channel

  • default Anaconda channel

Otherwise, you will have to write the recipes for those dependencies and include them in the PR. One shortcut is to use anaconda search -t conda <dependency name> to look for other packages built by others. Inspecting those recipes can give some clues into building a version of the dependency for bioconda.

Test Dependencies

During a Bioconda package build, the stringent mulled-build test runs the tests listed in the test section of the recipe’s meta.yaml file in an environment which does not have the dependencies listed in the test: requires: section. Because of this, all tests in that section must only rely on runtime dependencies or the build will fail.

If you do want to run tests that rely on dependencies listed in test: requires:, those tests should not be written directly in the meta.yaml file but instead put in a file in the same directory as the meta.yaml file. This file is picked up and run by the standard conda build test that occurs before the mulled-build, but is not passed on to and run by the mulled-build.


Some recipes require small patches to get the tests to pass, for example, fixing hard-coded shebang lines (as described at /usr/bin/perl or /usr/bin/python not found). Other patches are more extensive. When patching a recipe, please first make an effort to fix the issue upstream and document that effort in your pull request by either linking to the relevant upstream PR or indicating that you have contacted the author. The goal is not to block merging your PR until upstream is fixed, but rather to make sure upstream authors know there’s an issue that other users (including non-bioconda users) might be having. Ideally, upstream would fix the issue quickly and the PR could be modified, but it’s fine to merge with the patches and if/when upstream fixes, a separate bioconda PR could be opened that pulls in those upstream changes.


If a Python package is available on PyPI, use conda skeleton pypi <packagename> , or the more recent alternative grayskull pypi <packagename> to create a recipe, then remove the bld.bat and any extra comments in meta.yaml and The test that is automatically added is probably sufficient. The exception is when the package also installs a command-line tool, in which case that should be tested as well.


conda skeleton pypi and grayskull pypi only work with packages that have a source distribution produced with python sdist. Packages on PyPI which only have a wheel will not work. Packages containing only “built source” distributions produced with python bdist on UNIX will likewise not work.


Make sure you have a conda-build version 3.x when running conda skeleton pypi <packagename>. If you are still using conda-build 2.x, either update your conda-build package, or follow the migration guidelines in cb3-main.

  • typical import check: pysam

  • import and command-line tests: chanjo

By default, Python recipes (those that have python listed as a dependency) must be successfully built and tested on all supported Python versions in order to pass. However, many Python packages are not fully compatible across all Python versions. Use the preprocessing selectors in the meta.yaml file along with the build/skip entry to indicate that a recipe should be skipped.

For example, a recipe that only runs on Python 2.7 should include the following:

  -   python <3

Or a package that only runs on Python 3.6 and 3.7:

  - python >=3.6

Alternatively, for straightforward compatibility fixes you can apply a patch in the meta.yaml.



Most R packages on CRAN should be submitted at Conda-Forge. Specifically, if the CRAN package has a Bioconductor package dependency, it belongs in Bioconda. If the CRAN package does not have a Bioconductor package dependency, it belongs in Conda-Forge.


Using the conda skeleton cran method results in a recipe intended to be built for Windows as well, with lines like:

{% set posix = 'm2-' if win else '' %}
{% set native = 'm2w64-' if win else '' %}


       - $R -e "library('RNeXML')"  # [not win]
       - "\"%R%\" -e \"library('RNeXML')\""  # [win]

.. code-block:: yaml

        - {{ posix }}zip               # [win]

The bioconda channel does not build for Windows. To keep recipes streamlined, please remove the “set posix” and “set native” lines described above, remove the whole build entry under requirements, and convert the test:commands: block to only:

    - $R -e "library('RNeXML')"

Use conda skeleton cran <packagename> where packagename is a package available on CRAN and is case-sensitive. Either run that command in the recipes dir or move the recipe it creates to recipes. The recipe name will have an r- prefix and will be converted to lowercase. Typically can be used without modification, though dependencies may also need recipes. For further details on skeleton entries, you can also refer to the cran skeleton template.

Please remove any unnecessary comments and delete the bld.bat file which is used only on Windows.

If the recipe was created using conda skeleton cran or the scripts/ script, the default test is probably sufficient. Otherwise see the examples below to see how tests are performed for R packages.

  • recipe for R package not on CRAN, also with patch: spp

R (Bioconductor)

Use the bioconda-utils bioconductor-skeleton tool to build a Bioconductor skeleton. After using the bootstrap method to set up a testing environment and activating that environment (which will ensure the correct versions of bioconda-utils and conda-build), from the top level of the bioconda-recipes repository run:

bioconda-utils bioconductor-skeleton recipes config.yml DESeq2

Note that the provided package name is a case-sensitive package available on Bioconductor. The output recipe name will have a bioconductor- prefix and will be converted to lowercase. Data packages will be detected automatically, and a post-link script (see for details). Typically the resulting recipe can be used without modification, though dependencies may also need recipes. Recipes for dependencies with an r- prefix should be created at Conda-Forge unless the CRAN package has a Bioconductor dependency; see R (CRAN) above.

R (other sources)

If a package is only provided in a public repository (e.g. at github or bitbucket) or via some other website, first check with the authors of the package, if they are planning to publish it on CRAN or Bioconductor. This is always preferable, as it will ensure quality control and permanent availability at a stable URL, and can warrant waiting for such a publication. If this is not planned, you should check if a tagged version is available in a public repo (see infos on stable URLs above) or if the authors are willing to generate one. Only if none of this succeeds, the risk of the source repository or website disappearing should be taken.

Once you have obtained a stable URL to the package, follow the guidelines for R packages on CRAN above and adjust the URL and checksum accordingly.


Add a wrapper script if the software is typically called via java -jar .... Sometimes the software already comes with one; for example, fastqc already had a wrapper script, but peptide-shaker did not.

New recipes should use the openjdk package from conda-forge (recipe feedstock), the java-jdk package from bioconda is deprecated.

JAR files should go in $PREFIX/share/$PKG_NAME-$PKG_VERSION-$PKG_BUILDNUM. A wrapper script should be placed here as well, and symlinked to $PREFIX/bin.


Use conda skeleton cpan <packagename> to build a recipe for Perl and place the recipe in the recipes dir. The recipe will have the perl- prefix.


Historically, before conda-forge hosted Perl packages, many non-bioinformatics-related Perl packages were hosted on Bioconda. These are slowly being migrated to conda-forge.

An example of such a package is perl-module-build.

Alternatively, you can additionally ensure the build requirements for the recipe include perl-app-cpanminus, and then the script can be simplified. An example of this simplification is perl-time-hires.

If the recipe was created with conda skeleton cpan, the tests are likely sufficient. Otherwise, test the import of modules (see the imports section of the meta.yaml files in above examples).

Additionally, if the recipe was created with conda skeleton cpan, several modifications are necessary to satisfy bioconda policies:

  • remove the bld.bat script

  • remove the source/fn entry in meta.yaml

  • the requirements/build keyword in meta.yaml should be changed to requirements/host


Build tools (e.g., autoconf) and compilers (e.g., gcc) should be specified in the build requirements. Compilers are handled via a special macro. E.g., {{ compiler('c')}} ensures that the correct version of gcc is used. For the C++ variant g++, you need to use {{ compiler('cxx') }}. These rules apply for both Linux and macOS.

Conda distinguishes between dependencies needed for building (the build section), and dependencies needed during build time (the host section). For example, the following

    - {{ compiler('c') }}
    - zlib
    - zlib

specifies that a recipe needs the C compiler to build, and zlib present during building and running.

For two examples see:

If the package uses zlib, then please see the troubleshooting section on zlib.

If your package links dynamically against a particular library, it is often necessary to pin the version against which it was compiled, in order to avoid ABI incompatibilities. Instead of hardcoding a particular version in the recipe, we rely on conda doing this automatically. We use globally defined configurations, namely this for dependencies from conda-forge and this for dependencies in bioconda. If you need to pin another library, please notify @bioconda/core, and we will extend these lists.

It’s not uncommon to have difficulty compiling package into a portable conda package. Since there is no single solution, here are some examples of how bioconda contributors have solved compiling issues to give you some ideas on what to try:

  • ococo edits the source in to accommodate the C++ compiler on OSX

  • muscle patches the makefile on OSX so it doesn’t use static libs

  • metavelvet, eautils, preseq have several patches to their makefiles to fix LIBS and INCLUDES, INCLUDEARGS, and CFLAGS

  • mapsplice includes an older version of samtools; the included samtools’ makefile is patched to work in conda envs.

  • mosaik has platform-specific patches – one removes -static on linux, and the other sets BLD_PLATFORM correctly on OSX

  • mothur and soapdenovo have many fixes to makefiles


Bioconda has a small number of haskell tools. Most often they are built with stack (which is available on conda-forge). NGLess provides an example of how to call stack. Here are a few notes:

  • LD_LIBRARY_PATH/LIBRARY_PATH are set to include both ${PREFIX}/lib and the system paths (otherwise, stack setup will fail).

  • Create a directory (called fake-home in this example) and set it as $HOME, further setting $STACK_ROOT to use a subdirectory of this $HOME.

Mac OS X support is generally missing (any help is appreciated, see #6607).

General command-line tools

If a command-line tool is installed, it should be tested. If it has a shebang line, it should be patched to use /usr/bin/env for more general use. An example of this is fastq-screen.

For command-line tools, running the program with no arguments, checking the programs version (e.g. with -v) or checking the command-line help is sufficient if doing so returns an exit code 0. Often the output is piped to /dev/null to avoid output during recipe builds.


If a package depends on Python and has a custom build string, then py{{CONDA_PY}} must be contained in that build string. Otherwise Python will be automatically pinned to one minor version, resulting in dependency conflicts with other packages. See mapsplice for an example of this.


Metapackages tie together other packages. All they do is define dependencies. For example, the hubward-all metapackage specifies the various other conda packages needed to get full hubward installation running just by installing one package. Other metapackages might tie together conda packages with a theme. For example, all UCSC utilities related to bigBed files, or a set of packages useful for variant calling.

For packages that are not anchored to a particular package (as in the last example above), we recommended semantic versioning starting at 1.0.0 for metapackages.

Other examples of interest

Packaging is hard. Here are some examples, in no particular order, of how contributors have solved various problems:

  • graphviz has an OS-specific option to configure

  • crossmap removes libs that are shipped with the source distribution

  • hisat2 runs 2to3 to make it Python 3 compatible, and copies over individual scripts to the bin dir

  • krona has a script that gets called after installation to alert the user a manual step is required

  • htslib has a small test script that creates example data and runs multiple programs on it

  • spectacle runs 2to3 to make the wrapper script Python 3 compatible, patches the wrapper script to have a shebang line, deletes example data to avoid taking up space in the bioconda channel, and includes a script for downloading the example data separately.

  • gatk is a package for licensed software that cannot be redistributed. The package installs a placeholder script (in this case doubling as the jar wrapper) to alert the user if the program is not installed, along with a separate script (gatk-register) to copy in a user-supplied archive/binary to the conda environment

Name collisions (identical names)

In rare cases, a new recipe may have an identical name as an existing conda, conda-forge, bioconda, or Python package. This sort of naming collision should be avoided. For example, the weblogo recipe is for the standard command-line tool. There is also a Python package called weblogo on PyPI. In this case, to reduce ambiguity we prefixed the Python package with python- (see python-weblogo).

If in doubt about how to handle a naming collision, please submit an issue, or ask @bioconda/core in a PR.


An adequate test must be included in the recipe. An “adequate” test depends on the recipe, but must be able to detect a successful installation. While many packages may ship their own test suite (unit tests or otherwise), including these in the recipe is not recommended since it may timeout the build system on CircleCI. We especially want to avoid including any kind of test data in the repository.

Note that a test must return an exit code of 0. The test can be in the test field of meta.yaml, or can be a separate script (see the relevant conda docs for testing).

It is recommended to pipe unneeded stdout/stderr to /dev/null to avoid cluttering the output in the CircleCI build environment.

Link and unlink scripts (pre- and post- install hooks)

It is possible to include scripts that are executed before or after installing a package, or before uninstalling a package. These scripts can be helpful for alerting the user that manual actions are required after adding or removing a package. For example, a script may be used to alert the user that he or she will need to create a database or modify a settings file. Any package that requires a manual preparatory step before it can be used should consider alerting the user via an echo statement in a script. These scripts may be added at the same level as meta.yaml and

  • is executed prior to linking (installation). An error causes conda to stop.

  • is executed after linking (installation). When the post-link step fails, no package metadata is written, and the package is not considered installed.

  • is executed prior to unlinking (uninstallation). Errors are ignored. Used for cleanup.

These scripts have access to the following environment variables:

  • $PREFIX The install prefix

  • $PKG_NAME The name of the package

  • $PKG_VERSION The version of the package

  • $PKG_BUILDNUM The build number of the package


In general, recipes can be updated in-place. The older package[s] will continue to be hosted and available on while the recipe will reflect just the most recent package.

However, if an older version of a packages is required but has not yet had a package built, create a subdirectory of the recipe named after the old version and put the recipe there. Examples of this can be found in bowtie2, bx-python, and others.