Clinical Variant Annotation Pipeline.
The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible.
i. Install nextflow
ii. Install one of docker
, singularity
or conda
iii. Download the pipeline and test it on a minimal dataset with a single command
nextflow run nf-core/clinvap -profile test,<docker/singularity/conda/institute>
Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use
-profile institute
in your command. This will enable eitherdocker
orsingularity
and set the appropriate execution settings for your local compute environment.
iv. Start running your own analysis!
nextflow run nf-core/clinvap -profile <docker/singularity/conda/institute> --annotated_vcf <input> --skip_vep true
See usage docs for all of the available options when running the pipeline.
The nf-core/clinvap pipeline comes with documentation about the pipeline, found in the docs/
directory:
- Installation
- Pipeline configuration
- Running the pipeline
- Output and how to interpret the results
- Troubleshooting
nf-core/clinvap was originally written by Bilge Sürün.
If you would like to contribute to this pipeline, please see the contributing guidelines.
For further information or help, don't hesitate to get in touch on Slack (you can join with this invite).
If you use nf-core/clinvap for your analysis, please cite the following article:
Sürün, B., Schärfe, C.P., Divine, M.R., Heinrich, J., Toussaint, N.C., Zimmermann, L., Beha, J. and Kohlbacher, O., 2020. ClinVAP: a reporting strategy from variants to therapeutic options. Bioinformatics, 36(7), pp.2316-2317. https://doi.org/10.1093/bioinformatics/btz924
You can cite the nf-core
pre-print as follows:
Ewels PA, Peltzer A, Fillinger S, Alneberg JA, Patel H, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. nf-core: Community curated bioinformatics pipelines. bioRxiv. 2019. p. 610741. doi: 10.1101/610741.