Learning objectives:
- learn what bioconda is
- understand basic
conda
commands - learn how to list installed software packages
- learn how to manage multiple installation environments
See the bioconda paper and the bioconda web site.
Bioconda is a community-enabled repository of 3,000+ bioinformatics packages, installable via the conda
package
manager. It consists of a set of recipes, like this one, for sourmash, that are maintained by the community.
It just works, and it's effin' magic!!
Conda tracks installed packages and their versions.
Conda makes sure that different installed packages don't have conflicting dependencies (we'll explain this below).
We have already installed conda on this instance, but please see the installation HackMD after the class if you'd like to do this yourself.
To be able to use that installation, we need to let the instance know what path to find bioconda in:
echo export PATH=$PATH:/opt/miniconda3/bin >> ~/.bashrc
Then, run the following command (or start a new terminal session) in order to activate the conda environment:
source ~/.bashrc
Add channels
conda config --add channels defaults
conda config --add channels conda-forge
conda config --add channels bioconda
Try installing something:
conda install sourmash
and running it --
sourmash
will produce some output. (We'll tell you more about sourmash later.)
yay!
Conda is a "package manager" or software installer. See the full list of commands.
conda install
to install a package.
conda list
to list installed packages.
conda search
to search packages. Note that you'll see one package for every version of the software and for every version of Python (e.g. conda search sourmash
).
bioconda is a channel for conda, which just means that you
can "add" it to conda as a source of packages. That's what the conda config
above does.
Note, Bioconda supports only 64-bit Linux and Mac OSX.
You can check out the bioconda site.
You can use conda search
, or you can use google, or you can go visit the list of recipes.
This will save the list of conda-installed software you have in a particular
environment to the file packages.txt
:
conda list --export packages.txt
(it will not record the software versions for software not installed by conda.)
conda install --file=packages.txt
will install those packages in your local environment.
A feature that we do not use much here, but that can be very handy in some circumstances, is different environments.
"Environments" are multiple different collections of installed software. There are two reasons you might want to do this:
- first, you might want to try to exactly replicate a specific software install, so that you can replicate a paper or an old condition.
- second, you might be working with incompatible software, e.g. sometimes different software pipelines need different version of the same software. An example of this is older bioinformatics software that needs python2, while other software needs python3.
To create a new environment named pony
, type:
conda create -n pony
Then to activate (switch to) that environment, type:
source activate pony
And now when you run conda install
, it will install packages into this new environment, e.g.
conda install -y checkm-genome
(note here that checkm-genome requires python 2).
To list environments, type:
conda env list
and you will see that you have two environments, base
and
pony
, and pony
has a *
next to it because that's your
current environment.
And finally, to switch back to your base environment, do:
source activate base
and you'll be back in the original environment.
If you want to impress reviewers and also keep track of what your software versions are, you can:
- manage all your software inside of conda
- use
conda list --export software.txt
to create a list of all your software and put it in your supplementary material.
This is also something that you can record for yourself, so that if you are trying to exactly reproduce
conda works on Windows, Mac, and Linux.
bioconda works on Mac and Linux.
It does not require admin privileges to install, so you can install it on your own local cluster quite easily.
Connect to RStudio by setting your password (note, password will not be visible on the screen):
sudo passwd $USER
figuring out your username:
echo My username is $USER
and finding YOUR RStudio server interface Web address:
echo http://$(hostname):8787/
Now go to that Web address in your Web browser, and log in with the username and password from above.