Last updated: 2020-04-22

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This tutorial was adopted from here. The output shown is based on running the commands inside a container using the continuumio/miniconda Docker image. To follow this tutorial, make sure you have Docker installed and use docker pull to download the latest container.

docker pull continuumio/miniconda3

The objective of the workshop is to demonstrate how Conda can be used to simplify the installation of bioinformatic tools and to create reproducible (and separate) environments.

What is Conda?

From the Conda documentation:

Conda is an open source package management system and environment management system that runs on Windows, macOS and Linux. Conda quickly installs, runs and updates packages and their dependencies. Conda easily creates, saves, loads and switches between environments on your local computer. It was created for Python programs, but it can package and distribute software for any language.

If you have run into dependency problems before when trying to install bioinformatic tools, Conda helps deal with this. Furthermore, Conda makes it easier to install and work with incompatible tools.

What is Anaconda?

Anaconda is a distribution of Conda. It is a data science platform that comes with a lot of packages (too many in my opinion).

What is Miniconda?

Miniconda is a minimal installer for Conda. It is a small, bootstrap version of Anaconda that includes only Conda, Python, the packages they depend on, and a small number of other useful packages, including pip, zlib and a few others. I prefer using Miniconda and only installing tools that I need.

What is Bioconda?

Bioconda is a distribution of bioinformatics software realised as a channel for the versatile Conda package manager. Conda channels are simply the locations where packages are stored. Most widely used bioinformatic tools are available on the Bioconda channel, which hosts over 6,000 bioinformatics packages.

Conda basics

For this workshop we are using Conda inside a Docker container. Once you have pulled the latest image, run the following command.

# run new container
docker run -it --rm continuumio/miniconda bash

Your command prompt should look something like after running the command above:

(base) root@d470a3e9da91:/# 

Conda version

Like any good tool, if you type conda without any parameters you will get the usage and all the subcommands.

usage: conda [-h] [-V] command ...

conda is a tool for managing and deploying applications, environments and packages.


positional arguments:
    clean        Remove unused packages and caches.
    config       Modify configuration values in .condarc. This is modeled
                 after the git config command. Writes to the user .condarc
                 file (/root/.condarc) by default.
    create       Create a new conda environment from a list of specified
    help         Displays a list of available conda commands and their help
    info         Display information about current conda install.
    init         Initialize conda for shell interaction. [Experimental]
    install      Installs a list of packages into a specified conda
    list         List linked packages in a conda environment.
    package      Low-level conda package utility. (EXPERIMENTAL)
    remove       Remove a list of packages from a specified conda environment.
    uninstall    Alias for conda remove.
    run          Run an executable in a conda environment. [Experimental]
    search       Search for packages and display associated information. The
                 input is a MatchSpec, a query language for conda packages.
                 See examples below.
    update       Updates conda packages to the latest compatible version.
    upgrade      Alias for conda update.

optional arguments:
  -h, --help     Show this help message and exit.
  -V, --version  Show the conda version number and exit.

conda commands available from other packages:

We can find out what version of Conda we are using.

conda --version
conda 4.7.10

Conda Help and Manual

To see the full documentation for any command, type the command followed by --help. For example, to learn about the conda update command:

conda update --help

We will now make sure that Conda is up to date by using conda update. Conda will compare versions and let you know what is available to install. It will also tell you about other packages that will be automatically updated or changed with the update.

# when prompted enter "y"
conda update conda

conda --version
conda 4.7.12

You can also update all Conda packages to the latest compatible version.

# don't need to run this
conda update --all

Cleaning up

Conda will download and cache temporary files; remember to use conda clean periodically to clean up temp files.

conda clean -a

Installing bioinformatic tools

Installing bcftools is just a single command using the Bioconda channel.

conda install -c bioconda bcftools
cd /tmp

# get all SNPs, ignore the metadata, and view the first two lines
bcftools view -v snps aln_consensus.bcf | grep -v "^#" | head -2
1000000 336 .   A   G   221.999 .   DP=112;VDB=0.756462;SGB=-0.693147;MQ0F=0;AF1=1;AC1=2;DP4=0,0,102,0;MQ=60;FQ=-281.989    GT:PL   1/1:255,255,0
1000000 378 .   T   C   221.999 .   DP=101;VDB=0.704379;SGB=-0.693147;MQ0F=0;AF1=1;AC1=2;DP4=0,0,99,0;MQ=60;FQ=-281.989 GT:PL   1/1:255,255,0

Here’s how you would install bcftools without Conda.

Managing Environments

What is a Conda environment and why is it so useful?

Using Conda, you can create an isolated environment for your project. An environment is a set of packages that can be used in one or multiple projects. The default environment with Miniconda is the base environment. I don’t recommend installing all your packages/tools under the same environment.

There are two ways of creating a Conda environment.

  1. An environment file in YAML format (environment.yml).
  2. Manual specifications of packages.

Creating environment with an environment file.

An example of an environment file (environment.yml) I used for a specific project.

name: new_project
  - bioconda
  - anaconda
  - conda-forge
  - defaults
  - fastqc
  - multiqc
  - cutadapt
  - bwa
  - samtools
  - macs2
  - bedtools
  - deeptools
  - minimap2
  - star
  - parallel
  - idr

Now, let’s use this environment.yml environment file to install an older version of bwa in an isolated environment called bwa_old.

name: bwa_old
  - bioconda
  - bwa=0.7.15

Create the environment.


conda env create --file environment.yml

# check list of environments
conda env list
# conda environments:
base                  *  /opt/conda
bwa_old                  /opt/conda/envs/bwa_old

Activate the environment. The (bwa_old) in the beginning of the line indicates that we are curently using the bwa_old Conda environment.

conda activate bwa_old

# your prompt should change to
# (bwa_old) root@d470a3e9da91:/tmp#


Program: bwa (alignment via Burrows-Wheeler transformation)
Version: 0.7.15-r1140
Contact: Heng Li <>

Deactivate the environment.

conda deactivate

# your prompt will change back to
# (base) root@d470a3e9da91:/tmp#

Creating environment by manually specifying packages.

We can create Conda environments by specifying the name, channel, and list of packages within the terminal. In the example below, we are creating the test_env environment that uses python 2.7 and a list of libraries: numpy, matplotlib, pandas.

conda create -c conda-forge -n test_env python=2.7 numpy matplotlib pandas

Conda will solve any dependencies between the packages like before and create a new environment with those packages. I prefer creating environments using an environment file rather than on the command line.

Sharing Environments with others

To share an environment, you can export your Conda environment to an environment file. By doing this, the resulting environment file is very detailed with listings of specific versions of tools.

Exporting your environment to a file called test_env.yml:

conda activate bwa_old

conda env export -f test_env.yml

cat test_env.yml 
name: bwa_old
  - bioconda
  - defaults
  - _libgcc_mutex=0.1=main
  - bwa=0.7.15=1
  - libgcc=7.2.0=h69d50b8_2
  - libgcc-ng=9.1.0=hdf63c60_0
  - libstdcxx-ng=9.1.0=hdf63c60_0
  - zlib=1.2.11=h7b6447c_3
prefix: /opt/conda/envs/bwa_old

Each package follows the package=version=build convention. Most Conda packages use a system called semantic versioning to identify distinct versions of a software package unambiguously. Under semantic versioning, software is labeled with a three-part version identifier of the form MAJOR.MINOR.PATCH; the label components are non-negative integers separated by periods.

Note that this environment file may not work across platforms, since the builds and versions might be different for different operating systems (another reason to use Docker).

Deleting environments

Before deleting an environment make sure you are not currently using the environment or you will get an error.

conda env remove -n bwa_old

Managing Packages

Seeing what packages are available

The commands below will list all the packages in the bwa_old and base environments. The list will include versions of each package, the specific build, and the channel that the package was downloaded from. conda list is useful to ensure that you have installed the packages that you desire.

conda list -n bwa_old
# packages in environment at /opt/conda/envs/bwa_old:
# Name                    Version                   Build  Channel
_libgcc_mutex             0.1                        main  
bwa                       0.7.15                        1    bioconda
libgcc                    7.2.0                h69d50b8_2  
libgcc-ng                 9.1.0                hdf63c60_0  
libstdcxx-ng              9.1.0                hdf63c60_0  
zlib                      1.2.11               h7b6447c_3  

conda list -n base
# packages in environment at /opt/conda:
# Name                    Version                   Build  Channel
_libgcc_mutex             0.1                        main  
asn1crypto                1.2.0                    py27_0  
ca-certificates           2019.11.27                    0  
certifi                   2019.11.28               py27_0  
cffi                      1.13.2           py27h2e261b9_0  
chardet                   3.0.4                 py27_1003  
conda                     4.7.12                   py27_0  
conda-package-handling    1.6.0            py27h7b6447c_0  
cryptography              2.8              py27h1ba5d50_0  
enum34                    1.1.6                    py27_1  
futures                   3.3.0                    py27_0  
idna                      2.8                      py27_0  
ipaddress                 1.0.23                     py_0  
libedit                   3.1.20181209         hc058e9b_0  
libffi                    3.2.1                hd88cf55_4  
libgcc-ng                 9.1.0                hdf63c60_0  
libstdcxx-ng              9.1.0                hdf63c60_0  
ncurses                   6.1                  he6710b0_1  
openssl                   1.1.1d               h7b6447c_3  
pip                       19.3.1                   py27_0  
pycosat                   0.6.3            py27h14c3975_0  
pycparser                 2.19                     py27_0  
pyopenssl                 19.1.0                   py27_0  
pysocks                   1.7.1                    py27_0  
python                    2.7.16               h9bab390_0  
readline                  7.0                  h7b6447c_5  
requests                  2.22.0                   py27_0  
ruamel_yaml               0.15.46          py27h14c3975_0  
setuptools                42.0.2                   py27_0  
six                       1.13.0                   py27_0  
sqlite                    3.30.1               h7b6447c_0  
tk                        8.6.8                hbc83047_0  
tqdm                      4.40.0                     py_0  
urllib3                   1.24.2                   py27_0  
wheel                     0.33.6                   py27_0  
yaml                      0.1.7                had09818_2  
zlib                      1.2.11               h7b6447c_3  

Conda packages

Conda packages are files containing a bundle of resources: usually libraries and executables, but not always. In principle, Conda packages can include data, images, notebooks, or other assets.

It is important to be careful when downloading packages and use only trusted sources. Conda forge is a reliable source for many popular python packages. Anaconda Cloud is a package management service that makes it easy to find, access, store and share public and private notebooks, environments, and Conda and PyPI packages. Bioconda is a trusted channel for the conda package manager specialising in bioinformatics software.

Pre-configuring Channels

If you have a few trusted channels that you prefer to use, you can pre-configure these so that everytime you are creating an environment, you won’t need to explicitly declare the channel.

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

After adding channels, you can search for packages.

conda search bwa
Loading channels: done
# Name                       Version           Build  Channel             
bwa                            0.5.9               0  bioconda            
bwa                            0.5.9               1  bioconda            
bwa                            0.5.9      ha92aebf_2  bioconda            
bwa                            0.6.2               0  bioconda            
bwa                            0.6.2               1  bioconda            
bwa                            0.6.2      ha92aebf_2  bioconda            
bwa                           0.7.3a               0  bioconda            
bwa                           0.7.3a               1  bioconda            
bwa                           0.7.3a      h84994c4_3  bioconda            
bwa                           0.7.3a      h84994c4_4  bioconda            
bwa                           0.7.3a      ha92aebf_2  bioconda            
bwa                            0.7.4      h84994c4_1  bioconda            
bwa                            0.7.4      ha92aebf_0  bioconda            
bwa                            0.7.4      hed695b0_2  bioconda            
bwa                            0.7.4      hed695b0_3  bioconda            
bwa                            0.7.8               0  bioconda            
bwa                            0.7.8               1  bioconda            
bwa                            0.7.8      h84994c4_3  bioconda            
bwa                            0.7.8      ha92aebf_2  bioconda            
bwa                            0.7.8      hed695b0_4  bioconda            
bwa                            0.7.8      hed695b0_5  bioconda            
bwa                           0.7.12               0  bioconda            
bwa                           0.7.12               1  bioconda            
bwa                           0.7.13               0  bioconda            
bwa                           0.7.13               1  bioconda            
bwa                           0.7.15               0  bioconda            
bwa                           0.7.15               1  bioconda            
bwa                           0.7.16      pl5.22.0_0  bioconda            
bwa                           0.7.17      h84994c4_4  bioconda            
bwa                           0.7.17      h84994c4_5  bioconda            
bwa                           0.7.17      ha92aebf_3  bioconda            
bwa                           0.7.17      hed695b0_6  bioconda            
bwa                           0.7.17      pl5.22.0_0  bioconda            
bwa                           0.7.17      pl5.22.0_1  bioconda            
bwa                           0.7.17      pl5.22.0_2  bioconda            

Removing Conda Packages

Use conda remove to remove packages.

conda remove bcftools


  1. I’ve set up an environment for an analysis and I want to activate the environment in a script so that I can runs the tools from my script. For example you want to submit your script to Slurm. You may come across the error:
CommandNotFoundError: Your shell has not been properly configured to use 'conda activate'.

You can either use source instead of conda activate

source activate my_env

Or use source (change accordingly if you used Anaconda or installed it somewhere else). See here for more information.

source ~/miniconda3/etc/profile.d/
conda activate my_env
  1. Solving environment takes too long!

I don’t know if it is overkill but I try to separate some tools into their own environment. I also try to group tools based on the type of analysis I need to perform. For example I separate the Google Cloud SDK into its own environment.

conda create -c conda-forge -n gcloud google-cloud-sdk
  1. A tool that I want to use isn’t available on Conda.

Try to use Docker to install the tool. I have a Dockerfile that creates an image with commonly used tools and libraries for building and installing bioinformatics software.

R version 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.4

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.6.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.4      rprojroot_1.3-2 digest_0.6.25   later_1.0.0    
 [5] R6_2.4.1        backports_1.1.5 git2r_0.26.1    magrittr_1.5   
 [9] evaluate_0.14   stringi_1.4.6   rlang_0.4.5     fs_1.4.0       
[13] promises_1.1.0  whisker_0.4     rmarkdown_2.1   tools_3.6.2    
[17] stringr_1.4.0   glue_1.3.2      httpuv_1.5.2    xfun_0.12      
[21] yaml_2.2.1      compiler_3.6.2  htmltools_0.4.0 knitr_1.28