Introduction

Conda is a package and environment manager. As a package manager it enables you to install a wide range of software and tools using one simple command: conda install. As an environment manager it allows you to create and manage multiple different environments, each with their own set of packages. What are the benefits of using an environment manager? Some examples include the ability to easily run different versions of the same package, have different cross-package dependencies that are otherwise incompatible with each other and, last but not least, easy installation of all the software needed for an analysis.

Environments are of particular relevance when making bioinformatics projects reproducible. Full reproducibility requires the ability to recreate the system that was originally used to generate the results. This can, to a large extent, be accomplished by using Conda to make a project environment with specific versions of the packages that are needed in the project. You can read more about Conda here.

A Conda package is a compressed tarball (system-level libraries, Python or other modules, executable programs or other components). Conda keeps track of the dependencies between packages and platforms - this means that when installing a given package, all necessary dependencies will also be installed.

Conda packages are typically hosted and downloaded from remote so-called channels. Some widely used channels for general-purpose and bioinformatics packages are conda-forge and Bioconda, respectively. Both of these are community-driven projects, so if you're missing some package you can contribute to the channel by adding the package to it. When installing a Conda package you specify the package name, version (optional) and channel to download from.

A Conda environment is essentially a directory that is added to your PATH and that contains a specific collection of packages that you have installed. Packages are symlinked between environments to avoid unnecessary duplication.

This tutorial depends on files from the course GitHub repo. Take a look at the setup for instructions on how to set it up if, you haven't done so already. Then open up a terminal and go to workshop-reproducible-research/conda. Instructions below assume that you are standing in conda/ unless otherwise specified (e.g. if it says "create a file", it means save it in conda/).

Environment basics

Let's assume that you are just about to start a new exciting research project called Project A.

• Let's make our first Conda environment:

conda create -n project_a -c bioconda fastqc

This will create an environment called project_a, containing FastQC from the Bioconda channel. Conda will list the packages that will be installed and ask for your confirmation.

• Once it is done, you can activate the environment:

conda activate project_a

By default, Conda will add information to your prompt telling you which environment that is active.

• To see all your environments you can run:

conda info --envs

The active environment will be marked with an asterisk.

• To see the installed packages in the active environment, run:

conda list

• Now, deactivate the environment by running conda deactivate.
• List all environments again. Which environment is now marked as active?
• Try to run FastQC:

fastqc --version

• Did it work? Activate your project_a environment and run the fastqc --version command again. Does it work now?

Hopefully the FastQC software was not found in your base environment (unless you had installed it previously), but worked once your environment was activated.

• Now, let's add another package (SRA-Tools) to our environment using conda install. Make sure that project_a is the active environment first.

conda install -c bioconda sra-tools

• If we don't specify the package version, the latest available version will be installed. What version of SRA-Tools got installed?
• Run the following to see what versions are available:

conda search -c bioconda sra-tools

• Now try to install a different version of SRA-Tools, e.g.:

conda install -c bioconda sra-tools=2.7.0

Read the information that Conda displays in the terminal. It probably asks if you want to downgrade the initial SRA-Tools installation to the one specified here (2.7.0 in the example). You can only have one version of a given package in a given environment.

Let's assume that you will have sequencing data in your Project A, and want to use the latest Bowtie2 software to align your reads.

• Find out what versions of Bowtie2 are available in the Bioconda channel using conda search -c bioconda.
• Now install the latest available version of Bowtie2 in your project_a environment.

Let's further assume that you have an old project (called Project Old) where you know you used Bowtie2 v2.2.5. You just got back reviewer comments and they want you to include some alignment statistics. Unfortunately, you haven't saved that information so you will have to rerun the alignment. Now, it is essential that you use the same version of Bowtie that your results are based on, otherwise the alignment statistics will be misleading. Using Conda environments this becomes simple. You can just have a separate environment for your old project where you have an old version of Bowtie2 without interfering with your new Project A where you want the latest version.

• Make a new environment for your old project:

conda create -n project_old -c bioconda bowtie2=2.2.5

• List your environments (do you remember the command?).
• Activate project_old and check the Bowtie2 version (bowtie2 --version).

• Activate project_a again and check the Bowtie2 version.

• Now let's try to remove an installed package from the active environment:

conda remove sra-tools

• Run conda deactivate to exit your active environment.
• Now, let's remove an environment:

conda env remove -n project_old

After making a few different environments and installing a bunch of packages, Conda can take up some disk space. You can remove unnecessary files with the command:

conda clean -a

This will remove package tar-balls that are left from package installations, unused packages (i.e. those not present in any environments), and cached data.

Environments in projects

We have up until now specified which Conda packages to install directly on the command line using the conda create and conda install commands. For working in projects this is not the recommended way. Instead, for increased control and reproducibility, it is better to use an environment file (in yml format) that specifies the packages, versions and channels needed to create the environment for a project.

Throughout these tutorials we will use a case study where we analyze an RNA-seq experiment with the multiresistant bacteria MRSA (see intro). You will now start to make a Conda yml file for this MRSA project. The file will contain a list of the software and versions needed to execute the analysis code.

In this Conda tutorial, all code for the analysis is available in the script code/run_qc.sh. This code will download the raw FASTQ-files and subsequently run quality control on these using the FastQC software.

We will start by making a Conda yml-file that contains the required packages to perform these two steps. Later in the course, you will update the Conda yml-file with more packages, as the analysis workflow is expanded.

• Let's get going! Make a yml file called environment.yml looking like this, and save it in the current directory (which should be workshop-reproducible-research/conda):

channels:
- conda-forge
- bioconda
dependencies:
- fastqc=0.11.9
- sra-tools=2.10.1

• Now, make a new Conda environment from the yml file (note that here the command is conda env create as opposed to conda create that we used above):

conda env create -n project_mrsa -f environment.yml

• Activate the environment!
• Now we can run the code for the MRSA project found in code/run_qc.sh, either by running bash code/run_qc.sh or by opening the run_qc.sh file and executing each line in the terminal one by one. Do this!

This should download the project FASTQ files and run FastQC on them (as mentioned above).

• Check your directory contents (ls -Rlh, or in your file browser). It should now have the following structure:

   conda/
    |
    |- code/
    |   |- run_qc.sh
    |
    |- data/
    |   |- raw_internal/
    |       |- SRR935090.fastq.gz
    |       |- SRR935091.fastq.gz
    |       |- SRR935092.fastq.gz
    |
    |- intermediate/
    |   |- fastqc/
    |       |- SRR935090_fastqc.zip
    |       |- SRR935091_fastqc.zip
    |       |- SRR935092_fastqc.zip
    |
    |- results/
    |   |- fastqc/
    |       |- SRR935090_fastqc.html
    |       |- SRR935091_fastqc.html
    |       |- SRR935092_fastqc.html
    |
    |- environment.yml

Note that all that was needed to carry out the analysis and generate these files and results was environment.yml (that we used to create a Conda environment with the required packages) and the analysis code in code/run_qc.sh.

Projects can often be quite large and require lots of dependencies; it can feel daunting to try to capture all of that in a single Conda environment, especially when you consider potential incompatibilities that may arise. It can therefore be a good idea to start new projects with an environment file with each package you know that you will need to use, but without specifying exact versions (except for those packages where you know you need a specific version). Conda will then try to get the latest compatible versions of all the specified software, making the start-up and installation part of new projects easier. You can then add the versions that were installed to your environment file afterwards, ensuring future reproducibility.

Extra material

The following extra material contains some more advanced things you can do with Conda and the command line in general, which is not part of the main course materials. All the essential skills of Conda are covered by the previous section: the material here should be considered tips and tricks from people who use Conda as part of their daily work. You thus don't need to use these things unless you want to, and you can even skip this part of the lesson if you like!

Managing Python versions

With Conda it's possible to keep several different versions of Python on your computer at the same time, and switching between these versions is very easy. However, a single Conda environment can only contain one version of Python.

Your current Python installation

The Conda base environment has its own version of Python installed. When you open a terminal (after having installed Conda on your system) this base environment is activated by default (as evidenced by (base) prepended to your prompt). You can check what Python version is installed in this environment by running python --version. To see the exact path to the Python executable type which python.

In addition to this your computer may already have Python installed in a separate (system-wide) location outside of the Conda installation. To see if that is the case type conda deactivate until your prompt is not prepended with a Conda environment name. Then type which python. If a path was printed to the terminal (e.g. /usr/bin/python) that means some Python version is already installed in that location. Check what version it is by typing python --version.

Now activate the base Conda environment again by typing conda activate (or the equivalent conda activate base) then check the Python installation path and version using which and python --version as above. See the difference? When you activate a Conda environment your $PATH variable is updated so that when you call python (or any other program) the system first searches the directory of the currently active environment.

Different Python versions

When you create a new Conda environment you can choose to install a specific version of Python in that environment as well. As an example, create an environment containing Python version 3.5 by running:

conda create -n py35 python=3.5

Here we name the environment py35 but you can choose whatever name you want.

To activate the environment run:

conda activate py35

You now have a completely separate environment with its own Python version.

Let's say you instead want an environment with Python version 2.7 installed. You may for instance want to run scripts or packages that were written for Python 2.x and are thus incompatible with Python 3.x. Simply create the new Conda environment with:

conda create -n py27 python=2.7

Activate this environment with:

conda activate py27

Now, switching between Python versions is as easy as typing conda activate py35 / conda activate py27.

Configuring Conda

The behaviour of your Conda installation can be changed using an optional configuration file .condarc. On a fresh Conda install no such file is included but it's created in your home directory as ~/.condarc the first time you run conda config.

You can edit the .condarc file either using a text editor or by way of the conda config command. To list all config parameters and their settings run:

conda config --show

Similar to Conda environment files, the configuration file is in yml syntax. This means that the config file is structured in the form of key:value pairs where the key is the name of the config parameter (e.g. auto_update_conda) and the value is the parameter setting (e.g. True).

Adding the name of a config parameter to conda config --show will show only that parameter, e.g. conda config --show channels.

You can change parameters with the --set, --add, --append and --remove flags to conda config.

If you for example want to enable the 'Always yes' behaviour which makes Conda automatically choose the yes option, such as when installing, you can run:

conda config --set always_yes True

To see details about a config parameter you can run conda config --describe <parameter>. Try running it on the channels parameter:

conda config --describe channels

In the beginning of this tutorial we added Conda channels to the .condarc file using conda config --add channels. To remove one of the channels from the configuration file you can run:

conda config --remove channels conda-forge

Check your .condarc file to see the change. To add the conda-forge channel back to the top of the channels simply run:

conda config --add channels conda-forge

To completely remove a parameter and all its values run:

conda config --remove-key <parameter>

For a list of Conda configuration parameters see the Conda configuration page.

Decorating your prompt

By default, Conda adds the name of the currently activated environment to the end of your command line prompt. This is a good thing, as it makes it easier to keep track of what environment and packages you have access to. The way this is done in the default implementation becomes an issue when using absolute paths for environments (specifying conda env create -p <path/to/environment>, though, as the entire path will be added to the prompt. This can take up a lot of unnecessary space, but can be solved in a number of ways.

The most straightforward way to solve this is to change the Conda configuration file, specifically the settings of the env_prompt configuration value which determines how Conda modifies your command line prompt. For more information about this setting you can run conda config --describe env_prompt and to see your current setting you can run conda config --show env_prompt.

By default env_prompt is set to ({default_env}) which modifies your prompt with the active environment name if it was installed using the -n flag or if the environment folder has a parent folder named envs/. Otherwise the full environment path (i.e. the 'prefix') is displayed.

If you instead set env_prompt to ({name}) Conda will modify your prompt with the folder name of the active environment. You can change the setting by running the following:

conda config --set env_prompt '({name}) '

If you wish to keep the ({default_env}) behaviour, or just don't want to change your Conda config, an alternative is to keep Conda environment folders within a parent folder called envs/. This will make Conda only add the folder name of the Conda environment to your prompt when you activate it.

As an example, say you have a project called project_a with the project path ~/myprojects/project_a. You could then install the environment for project_a into a folder ~/myprojects/project_a/envs/project_a_environment. Activating the environment by pointing Conda to it (e.g. conda activate ~/myprojects/project_a/envs/project_a_environment) will only cause your prompt to be modified with project_a_environment.

Bash aliases for conda

Some programmers like to have aliases (i.e. shortcuts) for common commands. Here are two aliases for conda that might prove useful for you.

# Activate an environment
alias coac='conda activate'

# Deactivate an environment
alias code='conda deactivate'

Don't forget to add them to your ~/.bash_profile if you want to use them!

Optimising for speed

One of the greatest strengths of Conda is, unfortunately, also its greatest weakness in its current implementation: the availability of a frankly enormous number of packages and versions. This means that the search space for the dependency hierarchy of any given Conda environment can become equally enormous, leading to a (at times) ridiculous execution time for the dependency solver. It is not uncommon to find yourself waiting for minutes for Conda to solve a dependency hierarchy, sometimes even into the double digits. How can this be circumvented?

Firstly, it is useful to specify as many of the major.minor.patch version numbers as possible when defining your environment: this drastically reduces the search space that Conda needs to go through. This is not always possible, though. For example, we mentioned in the end of the Environments in projects section that you might want to start out new projects without version specifications for most packages, which means that the search space is going to be large. Here is where another software comes into play: Mamba.

The Mamba package manager is built on-top of Conda with some changes and additions that greatly speed up the execution time. First of all, core parts of Mamba are written in C++ instead of Python, like the original Conda. Secondly, it uses a different dependency solver algorithm which is much faster than the one Conda uses. Lastly, it allows for parallel downloading of repository data and package files with multi-threading. All in all, these changes mean that Mamba is (currently) simply a better version of Conda. Hopefully these changes will be incorporated into the Conda core at some point in the future!

So, how do you get Mamba? Funnily enough, the easiest way to install it is (of course) using Conda! Just run conda install -n base -c conda-forge mamba, which will install Mamba in your base Conda environment. Mamba works almost exactly the same as Conda, meaning that all you need to do is to stop using conda <command> and instead use mamba <command> - simple! There are only a few exceptions to this, the two major ones beings activating and deactivating environments: you still have to use conda activate and conda deactivate. So transitioning into using Mamba is actually quite easy - enjoy your shorter execution times!