We have been evaluating Nextflow before in my work at pharmb.io , but that was before DSL2 and the support for re-usable modules (which was one reason we needed to develop our own tools to support our challenges, as explained in the paper ). Thus, there’s definitely some stuff to get into.
Based on my years in bioinformatics and data science, I’ve seen that the number one skill that you need to develop is to be able to effectively troubleshoot things, because things will invariably fail in all kinds of ways.
Today marked the day when we ran the very first production workflow with SciPipe , the Go -based scientific workflow tool we’ve been working on over the last couple of years. Yay! :)
This is how it looked (no fancy GUI or such yet, sorry):
The first result we got in this very very first job was a list of counts of ligands (chemical compounds) in the ExcapeDB dataset (download here ) interacting with the 44 protein/gene targets identified by Bowes et al as a good baseline set for identifying hazardous side-effects effects in the body (that is, any chemical compounds binding these proteins, will never become an approved drug).