On the ground however, they learn both. Or I should say if you find someone who is exclusive to one or the other language (which happens), they aren't much of a programmer to begin with. There is a whole food chain essentially in life sciences in academia with computer knowledge. There are those who write the tooling, who are perhaps so abstracted they don't know the underlying biology and vet their tooling based on simulated data and a comparison with existing tooling, toiling in their own castles on tooling that might not ever see a real dataset. There are those who use the tooling to create novel pipelines to analyze data and draw conclusions based on their own or their collaborators literature research or life science perspectives, they might not care if the finding is truly novel or if its merely proving an existing gumption with a tool that hasn't yet been applied to an existing dataset. Then there are also those who run the pipelines created by others within their research group, sometimes others who have long left that given research group, with brittle hardcoded paths and other "DO NOT TOUCH" segments in a massive single 2500 line file that gurgitates some plots from a standard sort of csv file as input.
