Bioinformatics Training and Teaching with Galaxy

2012 Galaxy Community Conference (GCC2012), Chicago, Illinois, July 25-27, 2012

A GCC22012Breakout Session

# Participants
  • Hans-Rudolf Hotz
  • Alban Lermine
  • Nikhil Joshi
  • Björn Grüning
  • Geir Kjetil Sandve
  • Xiao Dong
  • Morris Chukhman
  • Warren Kaplan
  • Madhavan Ganesh
  • Graeme Grimes
  • Ketaki Bhide
  • Patra Volarath
  • Erik Garrison
  • Dave Clements
  • Rama Sompallae
  • ...


These notes are a rough first grouping of Dave Clements' notes from the breakout. Please feel free to update them.


  • There is not a "teaching" instance of Galaxy anywhere. Options for training include using your own instance, using main, or using a cloud instance.
  • Weeklong course at UC Davis with everyone doing their own bioinformatics on the cloud. This is very inexpensive.
  • having 40 people on same instance can be a problem

What Participants Have Been Doing

  • Weeklong course on bioinformatics.
  • Two back to back courses. Only used UCSC encode and Galaxy. RNA-Seq analysis.
  • Perl for bioinformatics.
  • Use Galaxy for training, bioinformatics school
  • Don't use Galaxy internally at the bioinformatics core, but do use it for training.
    • Started teaching with command line and that was a disaster.
  • Use Galaxy to teach students, some of whom have not used a computer before. Galaxy might be too complicated for that group.

Goals for Training

  • Want to be able to teach biologists enough so that they can do the analysis, but also to have them learn how to do in depth analysis.
  • To learn programming
    • Galaxy as a visual programming language.
    • Don't have to teach R or Perl, but the ideas: transform the ides
  • Make bioinformatics training a part of medical education.
  • Teach people to do their own analysis.


  • Not enough time for training. Just do intro to bioinformatics using Galaxy
  • Problem with teaching is that participants forget it after a month, if they don't use it frequently after the training.
  • Teaching R is an order of magnitude harder to teach than perl
  • Do you teach the details or high level?
    • Researchers who have specific needs.
    • You have students who you want to teach basic understanding too
    • We also need to train the biologists and chemists to use the tools, but too understand the outputs.
    • Some past bioinformatics training focused on statistics to the point of alienating biologists
  • Trainers can't know every detail of every tool
  • How do you communicate the complexity of analysis and tools?
    • Can ask researchers "what do you want to assume?" That's a lot work.
    • Asking "which statistical test do you want to use" is not a question that many researchers can answer meaningfully.
  • Biologists are really good at following protocols, as long as they are at the bench. Following recipes on computers is not the same thing.
  • Researchers that just want the data analyzed without understanding analysis. Some would prefer to have an analysis blackbox, with a red button that says "analyze."
  • The interface for files in Galaxy is confusing for users.
  • How do you have a meaningful example that runs fast.
  • Number of tools is daunting
  • Bioinformatics courses are not as good as many other courses
    • Textbooks are not as good
    • Field is developing so fast; no chance to standardize
    • End up with Bioinformatics for dummies courses


  • Jeremy's parameter walking can make it clear that they can get different results.
    • Flip side is this can be used to get the results they want.
    • Demonstrate how sensitive things are.
  • Improve interface for history / files in Galaxy
    • tie together files that are produced as a chunk.
    • Change it to be more hierarchical. Have folders. Too many files, e.g. cufflinks get 11 files.
    • Get Multiple views.
    • Does Galaxy have access to Human Computer Interaction folks?
  • Struggling with tools can get the point across that this is inherently difficult
  • Galaxy is about the making the simple things easy to do, so you can get to the science.
  • Include a "Hey this is important!" notice on complex tools.
  • What students really like: Show them and RNA-Seq un-replicated, and then we contrive a grand challenge and do some analysis to gain insight into that. Split people up into groups and have them tackle it. They like that a lot.
  • hands-on workshops may be overrated. Maybe speak for an hour, and then have participants go off for a few days.
  • Take a high-profile paper and reproduce the results from a paper. That's going to be motivating.
  • Set up a catalog of teaching material
    • Want to be able to share material
    • slide sharing would be helpful
  • Teach good Galaxy practices
    • Always rename your output files

Plan of Action

We did not discuss a plan of action at the breakout, but we will here.

More to come ...