Tweet
Most of the students and postdocs that come to our group have no little or no bioinformatics experience so the first step is usually to teach them some basic command line operations. I just started to check out Galaxy as a tool to get them up and running and doing research more quickly. Given that Galaxy also has a significant learning curve, is it really going to save time and effort? I'm just curious what other people's experiences are.
-
-
It's not Galaxy that has a significant learning curve, it's Bioinformatics. The idea that using something like Galaxy will help skip months or years of practise is just silly.
By the time you know what tools to use and what parameters to choose, there is little left between using Galaxy and creating your own pipelines. -
I agree. Galaxy is an abstraction layer over what is essential practice - the art of passing data from one part of a pipeline to the next. They will be far more accomplished with command line basics at their disposal than being point and click experts in Galaxy.
I realise some might think this is something of an outmoded view, but if you hide the command line from people they'll never get to grips with the basic tools that make a bioinformaticians life much simpler. Awk is the perfect example of this.
Galaxy hides the discoveries of parallelism, the realisation that some things are IO bound, some things are CPU limiting. It stops you from thinking about the nature of what you are doing.
The paramaterisation - well that's just as essential in your own tools and in Galaxy. Blind default parameter acceptance is what you need to encourage the newbies out of. I dread to think how many RNA-Seq experiments are blindly pushed through Galaxy installations parameterised for mammalian data where the data is from from plants..
I take on intern (undergraduate) students each year, from a biology background with no previous bioinformatics experience. The first thing they get is a command line tutorial and a book on Perl (sorry, but the place is a historical Perl shop
).
Bash, Perl, Python, R - these are hard to explore in Galaxy. Yes I know there is the Tool Factory, but it just seems like an extra layer of cruft in the way of 'getting things done'.Comment
-
Comment
-
I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...06-18-2026, 07:11 AM -
Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.
The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
...06-02-2026, 10:05 AM
Comment