nf-core/bench is a best-practice analysis pipeline for benchmarking variant call files. By default the pipeline will benchmark query variant call files against the GIAB truth sets for short and structural variants. However, defaults can be overwritten by specifying your own files in the sample sheet. Please note that by default the pipeline will only accept query variant call files that were mapped to the genomes matching the GIAB truth sets (GRCh37 or GRCh38 for short variants and GRCh37 for structural variants).

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website.

1. Prepare files for benchmarking
   • Prepare query .vcf.gz file
   • Prepare truth .vcf.gz file
   • Prepare genome .fa file
   • Prepare high confidence regions .bed file
2. Benchmark short variant (SNPs and INDELs) using hap.py
3. Benchmark structural variant (DELs and INSs) using truvari

Please note that some tools have not yet been containerised and the workflow will only work if you are using conda

1. Install Nextflow (>=21.04.0)

2. Install any of Docker, Singularity, Podman, Shifter or Charliecloud for full pipeline reproducibility (please only use Conda as a last resort; see docs)

3. Download the pipeline and test it on a minimal dataset with a single command:

   nextflow run nf-core/bench -profile test,<docker/singularity/podman/shifter/charliecloud/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.
   • If you are using singularity then the pipeline will auto-detect this and attempt to download the Singularity images directly as opposed to performing a conversion from Docker images. If you are persistently observing issues downloading Singularity images directly due to timeout or network issues then please use the --singularity_pull_docker_container parameter to pull and convert the Docker image instead. Alternatively, it is highly recommended to use the nf-core download command to pre-download all of the required containers before running the pipeline and to set the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options to be able to store and re-use the images from a central location for future pipeline runs.
   • If you are using conda, it is highly recommended to use the NXF_CONDA_CACHEDIR or conda.cacheDir settings to store the environments in a central location for future pipeline runs.

4. Start running your own analysis!

   nextflow run nf-core/bench -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input samplesheet.csv --genome GRCh37

The nf-core/bench pipeline comes with documentation about the pipeline usage, parameters and output.

nf-core/bench was originally written by Christopher Hakkaart from the Institute of Medical Genetics and Applied Genomics, Nicholas Smith and Christian Mertes from the Technical University of Munich, and Leon Brandhoff from the University of Cologne for use by the German Human Genome-Phenome Archive.

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 the Slack #bench channel (you can join with this invite).

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.