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nf-core/clinvap

Clinical Variant Annotation Pipeline.

Build Status GitHub Actions CI Status GitHub Actions Linting Status Nextflow

install with bioconda Docker

Introduction

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.

Quick Start

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 either docker or singularity 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> --reads '*_R{1,2}.fastq.gz' --genome GRCh37

See usage docs for all of the available options when running the pipeline.

Documentation

The nf-core/clinvap pipeline comes with documentation about the pipeline, found in the docs/ directory:

  1. Installation
  2. Pipeline configuration
  3. Running the pipeline
  4. Output and how to interpret the results
  5. Troubleshooting

Credits

nf-core/clinvap was originally written by Bilge Sürün.

Contributions and Support

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).

Citation

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.