Putting it all together
Assuming you've been able to:
- Install
snpQT
- Pick and maybe configure a profile
- Set up your own parameter file
You're now ready to run snpQT
:
nextflow run main.nf -params-file <path_to_params_file> -profile standard,singularity -w <path_to_work_dir> -resume
<path_to_params_file>
is the filepath of a parameter file that you have configured e.g.$HOME/test_run.yaml
<path_to_work_dir>
is the filepath to a directory that will store intermediate files generated by the pipeline. This directory is used to make checkpoints and allows-resume
to continue work if the pipeline stops. The directory can get quite big.
Once the pipeline finishes, a summary analysis and your output data will be
published to the --results
directory you specified in the parameter file.
A simple full example using the provided toy dataset (stored in data/
directory) including a Sample and Variant QC:
$ nextflow run main.nf -params-file $HOME/parameters.yaml -profile standard,conda -w $HOME/work
Tip
- Using a parameter file is a good practise, if you prefer using arguments on the command-line, the equivalent
command would be:
nextflow run main.nf -profile standard,conda -params-file $HOME/parameters.yaml -w $HOME/work --qc --db db/ --bed data/toy.bed --bim data/toy.bim --fam data/toy.fam --results results/
.
Feel free to inspect the reports generated in $HOME/results
. The
reports will look a bit odd because the input data is synthetic. For a more detailed exploration of all the implemented
workflows on the toy dataset, as well as a real-life ALS- Control Cohort visit our Tutorial page.
There are nine individual workflows in snpQT
. We provide a detailed
explanation of each workflow in the User guide
section.