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Ruffus - a lightweight python module to run computational pipelines
This is perfect for computational biology work. Good documentation too.
I write all my code into this framework now.
ruffus.org.uk
You're welcome!
I write all my code into this framework now.
ruffus.org.uk
You're welcome!
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Ruffus is nice, but I never could figure out how to parameterize the generated jobs (for different objects etc...).
I have my own pipeline now based on function-wrapping job objects that I probably should pour into the net one of these days if anyone was interested. It supports multiprocessing, code change detection (on the level of the wrapped functions), resume (d'oh) and of course the usually file time checking, as well as partial fault tolerance (a broken job will only affect itself and the jobs depended on it, no the other jobs). -
Hi. Just found this by Google.
I am the primary developer of Ruffus.
Ruffus is designed to be light-weight, flexible and easy to use but the fundamental approach is different enough to normal scripting that some of my users have to work through the whole of the tutorial before "getting it".
We are always looking for new ways to present the documentation or even adjust the semantics to make things clearer / easier. Feedback so far has been tremendously useful and driven most of the development of Ruffus.
I am here to help people figure things out / hand-hold if necessary. So if anyone wants to send me (on- or off-line) a quick conceptual sketch of what you want to do / what the pipeline is supposed to do, I am happy to write some quick code or point you in the right direction.
Of course, if you have rolled your own pipeline code already, you probably have quite different ideas about how things should be done!
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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 -
With the launch of new single-cell sequencing platforms in 2026, the field stands at an exciting inflection point. This article surveys the most impactful advances in the field and discusses how they’re reshaping research in cancer, immunology, and beyond.
Introduction
Single-cell sequencing technologies have undergone remarkable advances over the past decade, transitioning from low-throughput experimental approaches to highly scalable platforms capable of...05-22-2026, 06:42 AM
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