As someone who cut his teeth on bioinformatics before eventually just completing a full computer science degree, I was a bit worried that this article wouldn’t

As someone who cut his teeth on bioinformatics before eventually just completing a full computer science degree, I was a bit worried that this article wouldn’t address the applied scientific audience well. Pleasantly surprised, though, at how well this article evangelizes NumPy to that exact community. Many labs are gaining access to or creating physical tools that create data analysis over experimental design problems. Biologists transitioning from running gels to detect the existence of a gene to running sequencing or flow cytometry to segment populations, for example. I remember seeing I think an RNA-seq experiment that tracked the full lineage of hematopoietic (blood) stem cells by a professor - my jaw was on the floor at the level of insight. One remaining step is transitioning many bioinformatics courses from applied tooling to general program design and open-sourcing. I’ve noticed a few labs have done that really well for years, but it is not often found as a field-level skillset.