#CONDA INSTALL PACKAGE FROM GITHUB INSTALL#
#CONDA INSTALL PACKAGE FROM GITHUB DOWNLOAD#

This is because even though prodigal may be in the bioconda channel, some of its dependencies may be found elsewhere or in multiple places, and having those 3 channels set in the proper priority helps to ensure the correct versions of everything are being used. Some installs work fine regardless (as is the case with prodigal here), but others will fail, and even worse, some will succeed but have more insidious issues. see this in the bioconda documentation here.

#CONDA INSTALL PACKAGE FROM GITHUB INSTALL#

Despite what the install examples show on the anaconda pages like the one above, for many packages from bioconda to install properly, more channels need to be set in a specific hierarchy (e.g. There is more on this below, but because itвЂ™s important, weвЂ™re going to note it here too.

#CONDA INSTALL PACKAGE FROM GITHUB DOWNLOAD#

Choosing for our appropriate system here (working in the binder linked above), we are going to want a Linux version, so this is one way to download it to our system (from copying the link under вЂњMiniconda3 Linux 64-bitвЂќ: Most likely, we will want Miniconda3, and for a 64-bit system. Both can still build environments in the otherвЂ™s python version, but more than likely we should just take python 3. Those labeled Miniconda 2 are using python 2 as the base, while those labeled Miniconda 3 are using python 3. The download page for Miniconda is here, and we should pick the one appropriate for our operating system. For this reason, weвЂ™re gonna move forward with Miniconda. Anaconda is large with lots of stuff, Miniconda is more lightweight and we install just what we want. After the screen loads, we can click the вЂњTerminalвЂќ icon under вЂњOtherвЂќ to launch our command-line environment:Д¬onda comes in two broad forms: Anaconda and Miniconda. WeвЂ™ll most likely want to be doing this on our own system eventually, but if we just want a temporary system to run through this tutorial, we can open a Binder by clicking this badge вЂ“. If thatвЂ™s not the case yet, then consider running through the Unix crash course first рџ™‚ NOTE: This page assumes already having some familiarity with working at the command line. The benefits go further, like really helping with reproducibility too, but letвЂ™s get into it! Conda lets us easily create and manage separate environments to avoid these types of version conflicts, and automatically checks for us when we try to install something new (so we find out now, before we break something somewhere under the hood and have no idea what happened). But then, Program C will depend on a different version of Program B, and this causes problems. Sometimes Program A will depend on a specific version of Program B.

Going hand-in-hand with making things easier to install is condaвЂ™s other value, that it handles different environments very nicely for us.

Being вЂњconda-installableвЂќ requires that someone (could be the developer, could be others) has gone through the trouble of making it that way, so not everything is available, but almost everything weвЂ™re likely to want to use is. Thank you, Conda team рџ™‚Д¬onda is a package and environment manager that is by far the easiest way to handle installing most of the tools we want to use in bioinformatics. This is a page about Conda in the style of this site, but there is also excellent documentation available from the Conda developers and community here.

Creating an environment and installing packages in one command.

An example of something we might want to install in our base environment.

Installing a specific version of a package.

Making an environment with a specific python version.

Conda install package from github