https://github.com/nf-core/epitopeprediction
A bioinformatics best-practice analysis pipeline for epitope prediction and annotation
https://github.com/nf-core/epitopeprediction
epitope epitope-prediction mhc-binding-prediction nextflow nf-core pipeline workflow
Last synced: 4 months ago
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A bioinformatics best-practice analysis pipeline for epitope prediction and annotation
- Host: GitHub
- URL: https://github.com/nf-core/epitopeprediction
- Owner: nf-core
- License: mit
- Created: 2018-11-28T15:37:41.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2026-02-25T18:26:27.000Z (4 months ago)
- Last Synced: 2026-02-25T21:19:08.652Z (4 months ago)
- Topics: epitope, epitope-prediction, mhc-binding-prediction, nextflow, nf-core, pipeline, workflow
- Language: Nextflow
- Homepage: https://nf-co.re/epitopeprediction
- Size: 5.13 MB
- Stars: 51
- Watchers: 146
- Forks: 31
- Open Issues: 19
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATIONS.md
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
- awesome-vdj - **epitopeprediction** - practice analysis pipeline for epitope prediction and annotation<br>⭐ [49](https://github.com/nf-core/epitopeprediction/stargazers) · [Homepage](https://nf-co.re/epitopeprediction) · `Nextflow` (🔬 VDJ Analysis / Epitope Prediction)
README
[](https://github.com/codespaces/new/nf-core/epitopeprediction)
[](https://github.com/nf-core/epitopeprediction/actions/workflows/nf-test.yml)
[](https://github.com/nf-core/epitopeprediction/actions/workflows/linting.yml)[](https://nf-co.re/epitopeprediction/results)[](https://doi.org/10.5281/zenodo.3564666)
[](https://www.nf-test.com)
[](https://www.nextflow.io/)
[](https://github.com/nf-core/tools/releases/tag/3.4.1)
[](https://docs.conda.io/en/latest/)
[](https://www.docker.com/)
[](https://sylabs.io/docs/)
[](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-core/epitopeprediction)
[](https://nfcore.slack.com/channels/epitopeprediction)[](https://bsky.app/profile/nf-co.re)[](https://mstdn.science/@nf_core)[](https://www.youtube.com/c/nf-core)
## 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, proteins, or peptides directly using state of the art prediction tools. The pipeline can be used to generate putative neo-epitopes with variant input, scan one or more proteins for binding hotspots or darkspots analysis, and perform binding predictions on immunopeptidomics data with peptide input.
Supported prediction tools:
- `mhcflurry`
- `mhcnuggets`
- `mhcnuggetsii`
- `netmhcpan`
- `netmhciipan`
The pipeline is built using [Nextflow](https://www.nextflow.io), 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](https://www.nextflow.io/docs/latest/dsl2.html) 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](https://github.com/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](https://nf-co.re/epitopeprediction/results).
## 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
> [!NOTE]
> If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/usage/installation) on how to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline) with `-profile test` before running the workflow on actual data.
First, prepare a samplesheet with your input data that looks as follows:
`samplesheet.csv`:
```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_2,A*01:01;A*24:02;B*07:02;B*68:01;C*07:02;C*15:01,I,gbm_1_proteins.fasta
GBM_3,A*02:01;A*24:01;B*07:02;B*08:01;C*04:01;C*07:01,I,gbm_3_peptides.tsv
```
Each row represents a sample with associated HLA alleles and input data (variants/peptides/proteins). Alleles do not necessarily need to be in this format. We rely on [MHCgnomes](https://github.com/pirl-unc/mhcgnomes) to parse variations of nomenclatures into a uniform format.
Now, you can run the pipeline using:
```bash
nextflow run nf-core/epitopeprediction \
-profile \
--input samplesheet.csv \
--outdir
```
> [!WARNING]
> This version of the pipeline does not support conda environments, due to issues with upstream dependencies.
> This means you cannot use the `conda` and `mamba` profiles. Please use `docker` or `singularity` instead.
> We hope to add support for conda environments in the future.
> [!WARNING]
> Please provide pipeline parameters via the CLI or Nextflow `-params-file` option. Custom config files including those provided by the `-c` Nextflow option can be used to provide any configuration _**except for parameters**_; see [docs](https://nf-co.re/docs/usage/getting_started/configuration#custom-configuration-files).
For more details and further functionality, please refer to the [usage documentation](https://nf-co.re/epitopeprediction/usage) and the [parameter documentation](https://nf-co.re/epitopeprediction/parameters).
## Pipeline output
To see the results of an example test run with a full size dataset refer to the [results](https://nf-co.re/epitopeprediction/results) tab on the nf-core website pipeline page.
For more details about the output files and reports, please refer to the
[output documentation](https://nf-co.re/epitopeprediction/output).
## Credits
nf-core/epitopeprediction was originally written by [Christopher Mohr](https://github.com/christopher-mohr) and [Alexander Peltzer](https://github.com/apeltzer). Further contributions were made by [Sabrina Krakau](https://github.com/skrakau) and [Leon Kuchenbecker](https://github.com/lkuchenb).
The pipeline was converted to Nextflow DSL2 by [Christopher Mohr](https://github.com/christopher-mohr), [Marissa Dubbelaar](https://github.com/marissaDubbelaar), [Gisela Gabernet](https://github.com/ggabernet), and [Jonas Scheid](https://github.com/jonasscheid) and further modularized by [Jonas Scheid](https://github.com/jonasscheid) and [Alina Bauer](https://github.com/alina-bauer).
## Contributions and Support
If you would like to contribute to this pipeline, please see the [contributing guidelines](.github/CONTRIBUTING.md).
For further information or help, don't hesitate to get in touch on the [Slack `#epitopeprediction` channel](https://nfcore.slack.com/channels/epitopeprediction) (you can join with [this invite](https://nf-co.re/join/slack)).
## Citations
If you use nf-core/epitopeprediction for your analysis, please cite it using the following doi: [10.5281/zenodo.3564666](https://doi.org/10.5281/zenodo.3564666)
An extensive list of references for the tools used by the pipeline can be found in the [`CITATIONS.md`](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](https://dx.doi.org/10.1038/s41587-020-0439-x).