Basically, I need to first fetch the metadata on all the samples, and then later group them by treatment based on that metadata. In other words, the structure of later parts of the DAG depends on the results of executing earlier parts of the DAG, so the full structure of the DAG is not known initially. The solution I used was to split the workflow in two: a "pre-workflow workflow" that fetches the sample metadata and then the main workflow which reads the metadata and builds the DAG based on it. See here: https://github.com/DarwinAwardWinner/CD4-csaw/blob/master/Sn...

This a common pattern that I see when putting together bioinformatics workflows: the full DAG of actions to execute cannot be known until part of the way through executing that DAG. Most workflow tools can't handle this gracefully. Another Python DAG-executor, called doit, can handle this case, by specifying that some rules should not be evaluated until after others have finished running. But it doesn't have some features that I wanted from Snakemake (e.g. compute cluster execution), so I ended up with the above solution instead.