Do people prefer to use Galaxy?

Hi all, I would like to learn how to use command lines in Python and Linux for Bioinformatics and it seems hard especially if you don’t have that background. Is this the reason many people like to use Galaxy. Are there any other advantages to it?

Best wishes

Hi @Martyn,

simplified usage is certainly an aspect relevant , in particular, for beginners, but beginner-friendliness is certainly not Galaxy’s only strength.
Some others,in no particular order, are:

  • access to compute infrastructure
    many modern bioinformatics analysis tools come with non-trivial hardware and/or compute time requirements: It may be impractical for you to leave your own machine running for 20 hours until a certain analysis is done or you might simply not have those 128 GB of RAM that a certain tool requires, but you can always use your institute’s (if it has one) Galaxy instance or use a public server like
    Of course, your institute could also give you direct command line access to their compute, but Galaxy gives you controlled access, which server admins always appreciate, plus working on remote servers from just your terminal is really not very user-friendly
  • data management: everything you’re doing in Galaxy gets recorded in a database, gets organised for you into histories, etc.
    once you have done a good number of your own analyses on the command line, you will appreciate how much cognitive burden Galaxy takes away from you by simply freeing you from seemingly simple data management and documentation tasks
  • reproducibility: Galaxy has lots of features that increase your and other people’s chances of rerunning a given analysis of yours at some later time point and obtaining the same result, like tool versioning, containerised tool environments, possibility to share exact analyses capturing every aspect of the above plus exact tool configuration with others
  • workflows: Galaxy has a powerful graphical workflow editor to combine tools into analysis pipelines with built-in mechanisms for parallelisation; there are command line-based alternatives to Galaxy if you’re only considering this single feature, but please don’t build bioinformatic pipelines just on the shell and some Python scripts.

All of these features are highlighted and demoed in the various tutorials and slides that you can find in the “Introduction” section of the Galaxy training material. Please take some time to work through some of that material to judge whether Galaxy is for you!