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Nextflow: Data-Driven Computational Pipelines

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Re: Nextflow: Data-Driven Computational Pipelines

#41
post #29

Earlier quoted context omitted.

I for one have started to appreciate the fact that the shell/commandline interface means: - We have an interface that very strongly imposes composability, that is rarely seen in other parts of IT, and making people actually "follow the rules" :D - Data is (mostly) treated as immutable, except perhaps inside tools - Data is cached - The cli boundaries means that at least one can inspect inputs/outputs as a way to debu…

nextflow can't even get this right- base nextflow uses some combination of `--paramName` and `--param-name` and treats them as interchangeable, while nf-core encourages `--param_name` (but nextflow sees that as different). All trivial differences but just layers on the CLI frustration train.

[deleted]

Re: Nextflow: Data-Driven Computational Pipelines

#42
post #37

Earlier quoted context omitted.

From one proteomics person to another, what tools are you using? I can see needing snakemake for something like proteogenomics (our lab published a tool in that area) or DIA cause that pipeline can get a little complex. But for run of the mill stuff, as long as I have CLIs, I don’t really find myself needing anything beyond a basic batch file. Before you wonder why I don’t know that, I do top-down software developmen…

The DDA pipeline goes like this: ThermoRawFileParser -> Comet -> [a bunch of OpenMS tools] -> Percolator -> [custom quantification stuff]. The data is mostly derived from chemoproteomics experiments where you have isotopically labeled control and compound treated samples that are enriched with some probe. As a result, we work a lot with ratios and have to differentiate the scenario where your compound completely bloc…

I’d be happy to chat offline. Your current work and my current work I think share a lot of similarity. My GitHub is avcarr2.
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