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A Hands On Introduction to Conda: March 4, 2021

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Saranya Canchi edited this page Mar 29, 2021 · 8 revisions

When: Thursday, March 4, from 10:00 am-12:00 pm PST

Instructors: Marisa Lim and Saranya Canchi

Moderator: Abhijna Parigi

Helpers: Jose Sanchez, Jeremy Walter

Description: This free 2 hour hands-on tutorial will introduce you to the package and environment manager, Conda. You'll learn how to set up conda environments and manage software installations. We'll discuss other applications, such as packaging software, running workflows, or creating binders all with conda!

While we wait to get started --

  • Please fill out our pre-workshop survey if you have not already done so! Link here: https://forms.gle/XuFwaGDemWyCLUYh7

  • Open a web browser - e.g., Firefox, Chrome, or Safari. Open the binder by clicking this button: Binder. It may take a few minutes to load.

Introductions

We are both postdocs at UC Davis and part of the training and engagement team for the NIH Common Fund Data Ecosystem, a project supported by the NIH to increase data reuse and cloud computing for biomedical research.

You can contact us at [email protected] and [email protected].

Why Conda ?

  • OS-agnostic tool for package and environment management
  • clean software installation along with dependencies
  • searches for compatible software versions
  • many packages available - python, R, etc.
  • user has admin permissions, good for use on HPC where you'd otherwise need to request software installations

What is an Environment ?

Think of environments like zoom breakout rooms and the main room as your laptop OS. You can have many rooms and each independent from one another yet all are connected to your main room. You can join a room, chat, share screen etc and those remain part of the room with no cross over to the main room or any other room.

The equivalent of zoom rooms for software are directories that contain the specific software/tools and their dependencies for a given environment. This is a great way to maintain different versions of the same software !

Why do we need isolated software environments ?

  • your research/project requires specific versions of software packages (versioning)
  • you want to experiment with new packages or features of existing packages without compromising your current workflow (reproducibility)
  • you want to replicate results from a publication (repeatability)
  • your collaborators require you to use certain software/tools that are incompatibile with your current system (compatibility)

There are many actions you can perform on conda environments. We will cover a few today:

  • create
  • list
  • remove
  • update
  • revert
  • export

Goals for today:

  • learn about conda and how to use it
  • learn the basics of software installation, software dependencies, and isolation environments
  • learn about managing virtual environments

Let's get started!

If not done already:

  • 💻 Open a web browser - e.g., Firefox, Chrome, or Safari.
  • Open the binder by clicking this button: Binder

✔️ Did the binder load? Put up a ✋ on Zoom if you see Rstudio in your web browser.

Initialize conda

⌨️ Copy/paste commands into the terminal OR run the commands from the workshop_commands.sh file in the binder.

Setup the conda installer and initialize the settings:

source $(conda info --base)/etc/profile.d/conda.sh
conda init
conda config --set auto_activate_base false

If you have a WindowsOS, the copy/paste function may not work in the Terminal panel.

Instead, change the code language in the Source panel to Shell. Then, copy/paste code and run line by line from Source.

We will shorten command prompt to $:

echo "PS1='\w $ '" >> .bashrc

Re-start terminal for the changes to take effect (type exit and then open a new terminal). Activate the base conda environment:

conda activate base

✔️ Put up a ✋ on Zoom when you've got a new terminal window and see this at the prompt: (base) ~ $

Conda channels: Searching for software

The channels are places that conda looks for packages. The default channels after conda installation is set to Anaconda Inc's channels (Conda's Developer).

conda config --show channels
conda info # get channel URLs

Channels exist in a hierarchial order. By default:

Channel priority > package version > package build number

Image credit: Gergely Szerovay

Some key points:

  • conda-forge and bioconda are channels that contain commuity contributed software
  • Bioconda specializes in bioinformatics software (supports only 64-bit Linux and Mac OS)
  • conda-forge contains many dependency packages
  • In absence of other channels, conda searches the default repository which consists of ten official repositories.
  • You can even install R packages with conda!
  • See Resources for links.

We will update the channels to include bioconda since our demo includes downloading some bioinformatic tools. The order of the channels matters !

First add bioconda channel which defaults to top of the list:

conda config --prepend channels bioconda
conda config --get channels

Then re-add the conda-forge channel to move it to top of the list to follow the Bioconda recommended channel order:

conda config --prepend channels https://conda.anaconda.org/conda-forge
conda config --get channels

Install Software

We will install FastQC which is a software tool that provides a simple way to run quality control checks on raw sequencing data.

Search for software (fastqc):

conda search fastqc

Create conda environment and install fastqc:

conda create -y --name fqc fastqc

Activate environment:

conda activate fqc

Check fastqc version:

fastqc --version

✔️ Put up a ✋ on Zoom if your command prompt shows the name of the environment in parentheses: (fqc) ~ $

To go back to (base) ~ $ environment:

conda deactivate

Our QC steps for the pipeline involve fastqc and trimmomatic, which is useful for read trimming (i.e., adapters). There are multiple ways we could create the conda environment that contains both software programs.

Method 1: install software in existing environment

We could add trimmomatic to the fqc environment:

conda install -y trimmomatic=0.36
conda list # check installed software

We can specify the exact software version 👆 The default is to install the most current version, but sometimes your workflow may depend on a different version.

Method 2: install both software during environment creation

When you switch conda environments, conda changes the PATH (and other environment variables) so it searches for software packages in different places.

Let's check the PATH for method 1:

echo $PATH

You should see that the first element in the PATH changes each time you switch environments!

conda deactivate
conda create -y --name fqc_trim fastqc trimmomatic=0.36
conda activate fqc_trim 
conda list # check installed software
echo $PATH # path for method 2

The following methods use an external file to specify the packages to install.

Method 3: specify software to install with a YAML file

Often, it's easier to create environments and install software using a YAML file (YAML is a file format) that specifies all the software to be installed. For our example, we create a file called test.yml. Let's start back in the (base) environment.

conda deactivate

The test.yml file contains the following in YAML format:

name: qc_yaml #this specifies environment name
channels:
    - conda-forge
    - bioconda
    - defaults
dependencies:
    - fastqc
    - trimmomatic=0.36

Create the environment - note the difference in conda syntax:

conda env create -f test.yml #since environment name specified in yml file, we do not need to use -n flag here
conda activate qc_yaml
conda list  # check installed software

✍️ Try Method 4 (below) on your own!

For this approach, we export a list of the exact software package versions installed in a given environment and use it to set up new environments. This set up method won't install the latest version of a given program, for example, but it will replicate the exact environment set up you exported from.

Method 4: Install exact environment

conda activate fqc
conda list --export > packages.txt
conda deactivate

Two options -

  1. install the exact package list into an existing environment:
conda install --file=packages.txt
  1. set up a new environment with the exact package list:
conda env create --name qc_file --file packages.txt

Managing Environments

At this point, we have several conda environments! To see a list, there are 2 commands (they do the same thing!):

conda env list

or

conda info --envs

Generally, you want to avoid installing too many software packages in one environment. It takes longer for conda to resolve compatible software versions for an environment the more software you install.

For this reason, in practice, people often manage software for their workflows with multiple conda environments.

FastQC demo

If not already done, activate one of the environments we created, e.g.,:

conda activate fqc

Let's make sure the software was installed correctly:

fastqc --help

Output should look like:

FastQC - A high throughput sequence QC analysis tool

SYNOPSIS

        fastqc seqfile1 seqfile2 .. seqfileN

    fastqc [-o output dir] [--(no)extract] [-f fastq|bam|sam]
           [-c contaminant file] seqfile1 .. seqfileN
...

Download data

curl -L https://osf.io/5daup/download -o ERR458493.fastq.gz

Check out the data:

gunzip -c ERR458493.fastq.gz | wc -l

✔️ How many lines are in this fastq file? (add number to Zoom chat)

What does the fastq file look like?

gunzip -c ERR458493.fastq.gz | head

Run FastQC!

fastqc ERR458493.fastq.gz

✔️ Put up a ✋ on Zoom if you see an html file in the directory.

Exercise 1 ✍️

First, install the blast software with version 2.9.0 using conda.

Hint!

💡 You can use one of these approaches to install blast:

  • install the software in an existing env using conda install -y <name of the software>
  • create a new env using conda create -y --name <name of env> <software to install>

Second, try this example BLAST analysis in the conda environment. In this example, we are comparing the sequence similarity of a mouse protein sequence to a reference zebra fish sequence - as you might imagine, they are not that similar! But for today, this exercise will demonstrate running a quick analysis in a conda environment and bonus points if you find out how similar/dissimilar they are! (More details on BLAST and what each step is for here). Run each line of code below in the terminal:

  • Make a directory for the exercise files:

    mkdir exercise1
cd exercise1

  • Download with curl command and unzip data files:

    curl -o mouse.1.protein.faa.gz -L https://osf.io/v6j9x/download
curl -o zebrafish.1.protein.faa.gz -L https://osf.io/68mgf/download
gunzip *.faa.gz

  • Subset the data for a test run:

    head -n 11 mouse.1.protein.faa > mm-first.faa

  • Format zebra fish sequence as the blast database to search against:

    makeblastdb -in zebrafish.1.protein.faa -dbtype prot

  • Run a protein blast search with blastp!

    blastp -query mm-first.faa -db zebrafish.1.protein.faa -out mm-first.x.zebrafish.txt -outfmt 6

What does the output look like?

Note that if you conda deactivate, you can still access the input/intermediate/output files from the BLAST analysis. They are not 'stuck' inside the conda environment!

Exercise 2 ✍️

Conda allows you to revert to a previous version of your software using the --revision flag:

Usage:

# list all revisions
conda list --revisions

# revert to previous state
conda install --revision <number>

# for example:
conda install --revision 1

Earlier, we installed an older version of trimmomatic (0.36). Try updating it to the most recent version and then revert back to the old version.

Hint!

💡 You can do this exercise in any of the conda environments we created earlier with trimmomatic. You can update software with conda update <software name>

Tidying up

To remove old conda environments:

conda env remove --name <conda env name>

To remove software:

conda remove <software name>

Be sure to save any work/notes you took in the binder to your computer. Any new files/changes are not available when the binder session is closed!

For example, select a file, click "More", click "Export":

What else can we use Conda for?

  • Reproducing analyses

    • Why: removes the guesswork on what version of software to use or how to install it!

    For example, it's a lot easier to replicate a software environment from a publication or share software versions used in your own analysis using conda (particularly method 3 & 4 mentioned above ways to create conda environments from YAML or exact package list files).

  • Packaging software

    • Why: to share your cool software with the world!

    The gist is that you write a recipe that has all the specs about your software that is submitted to a channel, e.g., conda-forge or bioconda. Once correctly formatted and tested (with continuous integration automation) so it works on different operating systems, it's added to the channel and the world can use and install your software with conda!

  • Running parts of analysis workflows in their own environment

    • Why: avoid software version conflicts, easier to keep track of software/versions used for a specific analysis, easier for others to reproduce

    This is a very helpful feature for workflows. For example, in Snakemake workflows, you can specify that each step (called a "rule") is executed in an isolated conda environment by adding a conda: directive:

    rule fastqc_raw:
    input: "rnaseq/raw_data/{sample}.fq.gz"
    output: "rnaseq/raw_data/fastqc/{sample}_fastqc.html"
    params:
         outdir="rnaseq/raw_data/fastqc"
    conda: "rnaseq-env.yml"
    shell:
          """
          fastqc {input} --outdir {params.outdir}
          """

  • Creating binders

    • Why: teaching tool for software or analysis demos, share reproducible analysis

    Examples:

Q&A

📝 Please fill out our post-workshop survey! We use the feedback to help improve our training materials and future workshops. Link here: https://forms.gle/qKZj61wwZYx8fkQ27

Questions?

Resources

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