The bioinformatics workflow managers are designed around the quirkiness of bioinformatics, and they remove a lot of boilerplate. That makes them easier to grok for someone who doesn't have a strong programming background, at the cost of some flexibility.

Some features that bridge the gap:

1. Command-line tools are often used in steps of a bioinformatics pipeline. The workflow managers expect this and make them easier to use (e.g. https://github.com/snakemake/snakemake-wrappers).

2. Using file I/O to explicitly construct a DAG is built-in, which seems easier to understand for researchers than constructing DAGs from functions.

3. Built-in support for executing on a cluster through something like SLURM.

4. Running "hacky" shell or R scripts in steps of the pipeline is well-supported. As an aside, it's surprising how often a mis-implemented subprocess.run() or os.system() call causes issues.

5. There's a strong community building open-source bioinformatics pipelines for each workflow manager (e.g. nf-core, warp, snakemake workflows).

Airflow – and the other less domain-specific workflow managers – are arguably better for people who have a stronger software engineering basis. For someone who moved wet lab to dry lab and is learning to code on the side, I think the bioinformatics workflow managers lower the barrier to entry.