Earlier quoted context omitted.
As someone who is considering a switch from generic software engineering towards bioinformatics, what would you say the pain points are? If this is not the way to remove workflow friction, what is?
Would like to second this question. I'm very interested in getting into this world, but it feels like there isn't a clear path (especially for someone self-taught like me). Bioinformatics feels pretty inaccessible without a computer science or biology degree, even with substantial R and Python experience.
1) The fellows writing papers - usually these guys have PhDs. Usually a science-focused PhD. 2) Analysts - often have a background in mathematics, biology, or big-data. Success here can lead to an onramp to camp 1. Much of your time here is spent in interactive programming environments, like Jupyter notebooks. 3) Programmers - writing novel or faster bioinformatic tools, often in low-level languages like C++ or Rust. Sometimes you can get a paper out of these, especially if you have a CS background. There's increasingly room for higher-level tools though here too, so it starts to overlap with 2. 4) Pipeline programmers - people gluing analysis workflows together out of the tools written in low-level languages, often with a liberal helping of Unix command-fu. Often sort of an ad-hoc role, containing people from diverse backgrounds, from biology to sysadmin. (This is my current role). 5) Biology/wetlab - people running experiments in the lab, and want to analyze their own work, especially for QC purposes. Wild-west ad-hoc development practices.