nf-core/epitopeprediction

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Introduction

nf-core/epitopeprediction is a bioinformatics best-practice analysis pipeline for epitope prediction and annotation. The pipeline performs epitope predictions for a given set of variants or peptides directly using state of the art prediction tools. Additionally, resulting prediction results can be annotated with metadata.

Supported prediction tools:

• syfpeithi
• mhcflurry
• mhcnuggets-class-1
• mhcnuggets-class-2
• netmhcpan-4.0
• netmhcpan-4.1
• netmhc-4.0
• netmhciipan-4.1

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 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.

Pipeline summary

1. Read variants, proteins, or peptides and HLA alleles
2. Generate peptides from variants or proteins or use peptides directly
3. Predict HLA-binding peptides for the given set of HLA alleles

First, prepare a samplesheet with your input data that looks as follows:

samplesheet.csv:

sample,alleles,mhc_class,filename
GBM_1,A*01:01;A*02:01;B*07:02;B*24:02;C*03:01;C*04:01,I,gbm_1_variants.vcf
GBM_1,A*02:01;A*24:01;B*07:02;B*08:01;C*04:01;C*07:01,I,gbm_1_peptides.vcf

Each row represents a sample with associated HLA alleles and input data (variants/peptides/proteins).

Now, you can run the pipeline using:

nextflow run nf-core/epitopeprediction \
   -profile <docker/singularity/.../institute> \
   --input samplesheet.csv \
   --outdir <OUTDIR>

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Pipeline output

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

Credits

nf-core/epitopeprediction was originally written by Christopher Mohr from Boehringer Ingelheim and Alexander Peltzer from Boehringer Ingelheim. Further contributions were made by Sabrina Krakau from Quantitative Biology Center and Leon Kuchenbecker from the Kohlbacher Lab.

The pipeline was converted to Nextflow DSL2 by Christopher Mohr, Marissa Dubbelaar from Clinical Collaboration Unit Translational Immunology and Quantitative Biology Center, Gisela Gabernet from Quantitative Biology Center, and Jonas Scheid from Quantitative Biology Center

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

Citations

If you use nf-core/epitopeprediction for your analysis, please cite it using the following doi: 10.5281/zenodo.3564666

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.