A Hands On Introduction to Conda: March 18, 2021
When: Thursday, March 18, from 10:00 am-12:00 pm PST
Instructors: Marisa Lim and Abhijna Parigi
Moderator: Saranya Canchi
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 --
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✔️ Have you checked the pre-workshop resources page?
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📝 Please fill out our pre-workshop survey if you have not already done so! Link here: https://forms.gle/uyKyES7euDg7i1GZA
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💻 Open a web browser - e.g., Firefox, Chrome, or Safari. Open the binder by clicking this button:
. It may take a few minutes to load.
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 mcwlim@ucdavis.edu and aaparigi@ucdavis.edu.
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
- OS-agnostic tool for package and environment management (meaning it works for Windows, Mac, and Linux!)
- 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
Think of software environments like school classrooms, where the school is your laptop OS. Schools have many classrooms and while they are independent rooms they are still connected by hallways. Students can walk into any room and each room will have its own set of supplies (i.e., desks, chairs, whiteboard, decorations, etc.).
For conda, the equivalent of a classroom and its supplies is a conda environment on your computer and its set of specifically downloaded software/tools. Input/output files, like students, can easily move between environments and just like a school can have multiple classrooms, conda can manage multiple conda environments. This is what makes it a great tool for maintaining different versions of the same software all on 1 computer!
- versioning: your research/project requires specific versions of software packages
- reproducibility: you want to experiment with new packages or features of existing packages without compromising your current workflow
- repeatability: you want to replicate results from a publication
- compatibility: your collaborators require you to use certain software/tools that are incompatibile with your current system
❓ Questions?
If not done already:
- 💻 Open a web browser - e.g., Firefox, Chrome, or Safari.
- Open the binder by clicking this button:
✔️ Did the binder load? Put up a ✋ on Zoom if you see Rstudio in your web browser.
Set up Rstudio panels!
⌨️ Copy/paste commands into the terminal OR run the commands from the workshop_commands.sh file in the binder.
Installer sets up two things: Conda and the root environment. The root environment contains the selected python version and some basic packages.
Setup the conda installer and initialize the settings:
conda init
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).
✔️ Put up a ✋ on Zoom when you've got a new terminal window and see this at the prompt:
~ $
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 list # get list of packages in base environment
Channels exist in a hierarchial order. By default:
Channel priority > package version > package build number
See Resources for more info on channels!
We will update the channel list order and add bioconda since we are using bioinformatic tools today. The order of the channels matters!
First, add the defaults channel:
conda config --add channels defaults
conda config --get channels
Then add the bioconda channel:
conda config --add channels bioconda
conda config --get channels
Lastly, add the conda-forge channel to move it to top of the list, following Bioconda's recommended channel order:
conda config --add channels conda-forge
conda config --get channels
With this configuration, conda will search for packages in this order: 1) conda-forge, 2) bioconda, and 3) defaults
Another way to add channels is:
conda config --prepend channels bioconda
❓ Questions?
Instructor switch!
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
High-throughput sequencing data quality control steps often involve fastqc and trimmomatic. Trimmomatic is useful for read trimming (i.e., adapters). There are multiple ways we could create a conda environment that contains both software programs:
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.
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.
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 are using 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=0.36Create 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
See Resources for a 4th method of exporting exact environments.
Software can also be installed by specifying the channel with -c flag i.e.:
conda install -c conda-forge -c bioconda sourmash
At this point, we have several conda environments! To see a list, there are 2 commands (they do the same thing!):
conda env listor
conda info --envsGenerally, 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.
❓ Questions?
If not already done, activate one of the environments we created, e.g.,:
conda activate fqcLet's make sure the software was installed correctly:
fastqc --helpOutput 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 (a yeast sequence file):
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.
❓ Questions?
Instructor switch!
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First, install the
blastsoftware with version 2.9.0 using conda.
Hint!
:bulb: You can use one of these approaches to install blast:
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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
curlcommand 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!
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!
:bulb: 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":
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Reproducing analyses
- Why: removes the guesswork on what version of software to use or how to install it!
Details:
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).
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Packaging software
- Why: to share your cool software with the world!
Details:
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!
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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
Details:
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} """
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Creating binders
- Why: teaching tool for software or analysis demos, share reproducible analysis
Details:
Examples:
- the Rstudio binder we're using today was created with https://binder.pangeo.io/
- metENP binder: tool for metabolite enrichment analysis and their associated enriched pathways
There are many actions you can perform with conda environments. Today we have mentioned these!
- init
- config
- search
- create
- activate/deactivate
- list
- remove
- update
- revert
- export
📝 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/X238HaBbNRKATqDs8
❓ Questions?