Hi @GaryAitken
For the short guide, yes the link should be adjusted (I’ll do that!).
The current resource links as a reference can be found at:
Notes:
Installing and running a modern version of a Local Galaxy from Github requires administration beyond simply running run.sh. And if the first execution is not run inside of a standard .venv environment, you might get Galaxy to start up, but will usually have problems with tool installations, or tool dependency management pretty quickly! The tools you want to use from that tutorial will have many dependencies. The best way to correct this is to start over. You can save back your database, but it doesn’t sound like you will need that.
Your other option is the Docker Galaxy deployment. This involves some pre-requisite set up with a Docker environment first, but it will be much simpler to install and maintain, and you might find it nicer to work with. That will allow you to use Ephemeris scripts like this one to set your server → Hands-on: End-to-End Tissue Microarray Image Analysis with Galaxy-ME / End-to-End Tissue Microarray Image Analysis with Galaxy-ME / Imaging (admin-missing-tools) using this procedure → Hands-on: Galaxy Tool Management with Ephemeris / Galaxy Tool Management with Ephemeris / Galaxy Server administration. Be sure to see the Docker Galaxy README since some of this is pre-configured for you, as this version of Galaxy was designed to be used by scientists doing exactly what you are.
Back to the start, if you still want to try the Local Galaxy version instead!
Confirm your version of Python on your computer, and update if needed. Which version? You will be checking against the release notes for this instruction, and for the (very general) installation instructions. The current release is here. → 25.0 Galaxy Release (June 2025) — Galaxy Project 25.0.2.dev0 documentation
The instructions for setting up the .venv look pretty good to me. This is a bit “outside of Galaxy” since the operating system can have slightly different methods. But maybe this helps to understand how this works? → Create virtual environments for python with conda
You will follow up until Step 4. After that, Step 5 is what the run.sh command starting up Galaxy will be doing, and what tool installations will be doing.
The idea is to create then activate a directory. Then you install Galaxy from Github into that directory. Finally, start up Galaxy for the first time, then install tools (Ephemeris, or directly). You can install those in the tutorial, or you can use a workflow as the “baseline” and install based on the content.
In short, I don’t think these tools will work correctly without significant custom configuration with what you have now, and then you’ll be doing the same for other tool installations (or your own development). A clean environment, a clean install, then managed dependencies is probably the best path, and the Docker version will be easier. 