Introduction to Conda

What is Conda?

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. Why would you want to do that? For instance, you can easily run different versions of the same package or have different cross-package dependencies that are otherwise incompatible with each other.

Environments are of particular relevance when making bioinformatics projects reproducible. Full reproducibility requires the possibility 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.

Conda packages

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 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 by adding it to either channel. When installing a Conda package you specify the package name, version (optional), and channel to download from.

Conda environments

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

Set up

This tutorial depends on files from the course GitHub repo. Take a look at the intro 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/).

Install Conda

• Go ahead and install Conda as described below. Be sure to download the correct file for your OS.

macOS

# *** Install Miniconda3 for 64-bit macOS ***
curl https://repo.continuum.io/miniconda/Miniconda3-4.7.12.1-MacOSX-x86_64.sh -O
bash Miniconda3-4.7.12.1-MacOSX-x86_64.sh
rm Miniconda3-4.7.12.1-MacOSX-x86_64.sh

Linux

# *** Install Miniconda3 for 64-bit Linux ***
curl https://repo.continuum.io/miniconda/Miniconda3-4.7.12.1-Linux-x86_64.sh -O
bash Miniconda3-4.7.12.1-Linux-x86_64.sh
rm Miniconda3-4.7.12.1-Linux-x86_64.sh

• The installer will ask you questions during the installation:
  • Do you accept the license terms? (Yes)
  • Do you accept the installation path or do you want to chose a different one? (Probably yes)
  • Do you want to run conda init to setup conda on your system? (Yes)
• Restart your shell so the settings in ~/.bashrc/~/.bash_profile can take effect.
• You can verify that the installation worked by running:

conda --version

• Next, we will setup the the default channels (from where packages will be searched for and downloaded if no channel is specified).

conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge

Practical exercise

Get going with environments

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.
• 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.

How to use in a reproducible project setting

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 a file (in yaml format) specifying 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
  • run quality control on these using FastQC
• 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.
• So let's get going! Make a yaml file called environment.yml looking like this, and save it in the current directory (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 yaml 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 version (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

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 pythonversion 2.7 of 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 YAML 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.

Tips and tricks

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!

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 promp 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 deactive'

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