V-pipe is designed with hierarchically organised data in mind:
samples ├── patient1 │ ├── 20100113 │ └── 20110202 └── patient2 └── 20081130
Here, we have two samples from patient 1 and one sample from patient 2. All sample names should be unique such later mixups of different timepoints can be avoided.
V-pipe’s parameters for the number of cores to use and the maximum memory is specified in the config file
vpipe.config, for instance:
[ngshmmalign] number_cores = 24 leave_tmp = true
This instructs the
ngshmmalign step to use 24 cores and leave the MSA temp files, which might be useful for debugging certain genomic regions.
To invoke V-pipe on the current sample set, first perform a verbose dry-run:
snakemake -n -p -s vpipe.snake
and after confirming that all targets are as you would expect them, perform the real run:
snakemake -s vpipe.snake
You can find more ressources about using V-pipe on the project’s wiki.
See Getting started for instructions regarding initial setup.
V-pipe: user configurable options contains a list of options that can be set in V-pipe’s config file
V-pipe as a benchmark tool
V-pipe also provides an unified benchmarking platform, by incorporating two additional modules: a read simulator and a module to evaluate the accuracy of the results.
V-Pipe uses Snakemake, a robust workflow management system, and it is possible for users to easily customize the workflow by adding or excluding rules according to their specific requirements.