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VDJ Analysis"],"sub_categories":["Epitope Prediction"],"readme":"\u003ch1\u003e\n  \u003cpicture\u003e\n    \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"docs/images/nf-core-epitopeprediction_logo_dark.png\"\u003e\n    \u003cimg alt=\"nf-core/epitopeprediction\" src=\"docs/images/nf-core-epitopeprediction_logo_light.png\"\u003e\n  \u003c/picture\u003e\n\u003c/h1\u003e\n\n[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new/nf-core/epitopeprediction)\n[![GitHub Actions CI Status](https://github.com/nf-core/epitopeprediction/actions/workflows/nf-test.yml/badge.svg)](https://github.com/nf-core/epitopeprediction/actions/workflows/nf-test.yml)\n[![GitHub Actions Linting Status](https://github.com/nf-core/epitopeprediction/actions/workflows/linting.yml/badge.svg)](https://github.com/nf-core/epitopeprediction/actions/workflows/linting.yml)[![AWS CI](https://img.shields.io/badge/CI%20tests-full%20size-FF9900?labelColor=000000\u0026logo=Amazon%20AWS)](https://nf-co.re/epitopeprediction/results)[![Cite with Zenodo](http://img.shields.io/badge/DOI-10.5281/zenodo.3564666-1073c8?labelColor=000000)](https://doi.org/10.5281/zenodo.3564666)\n[![nf-test](https://img.shields.io/badge/unit_tests-nf--test-337ab7.svg)](https://www.nf-test.com)\n\n[![Nextflow](https://img.shields.io/badge/version-%E2%89%A525.04.0-green?style=flat\u0026logo=nextflow\u0026logoColor=white\u0026color=%230DC09D\u0026link=https%3A%2F%2Fnextflow.io)](https://www.nextflow.io/)\n[![nf-core template version](https://img.shields.io/badge/nf--core_template-3.4.1-green?style=flat\u0026logo=nfcore\u0026logoColor=white\u0026color=%2324B064\u0026link=https%3A%2F%2Fnf-co.re)](https://github.com/nf-core/tools/releases/tag/3.4.1)\n[![run with conda](http://img.shields.io/badge/run%20with-conda-3EB049?labelColor=000000\u0026logo=anaconda)](https://docs.conda.io/en/latest/)\n[![run with docker](https://img.shields.io/badge/run%20with-docker-0db7ed?labelColor=000000\u0026logo=docker)](https://www.docker.com/)\n[![run with singularity](https://img.shields.io/badge/run%20with-singularity-1d355c.svg?labelColor=000000)](https://sylabs.io/docs/)\n[![Launch on Seqera Platform](https://img.shields.io/badge/Launch%20%F0%9F%9A%80-Seqera%20Platform-%234256e7)](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-core/epitopeprediction)\n\n[![Get help on Slack](http://img.shields.io/badge/slack-nf--core%20%23epitopeprediction-4A154B?labelColor=000000\u0026logo=slack)](https://nfcore.slack.com/channels/epitopeprediction)[![Follow on Bluesky](https://img.shields.io/badge/bluesky-%40nf__core-1185fe?labelColor=000000\u0026logo=bluesky)](https://bsky.app/profile/nf-co.re)[![Follow on Mastodon](https://img.shields.io/badge/mastodon-nf__core-6364ff?labelColor=FFFFFF\u0026logo=mastodon)](https://mstdn.science/@nf_core)[![Watch on YouTube](http://img.shields.io/badge/youtube-nf--core-FF0000?labelColor=000000\u0026logo=youtube)](https://www.youtube.com/c/nf-core)\n\n## Introduction\n\n**nf-core/epitopeprediction** is a bioinformatics best-practice analysis pipeline for epitope prediction and annotation.\nThe 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.\n\nSupported prediction tools:\n\n- `mhcflurry`\n- `mhcnuggets`\n- `mhcnuggetsii`\n- `netmhcpan`\n- `netmhciipan`\n\nThe 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!\n\nOn 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).\n\n## Pipeline summary\n\n1. Read variants, proteins, or peptides and HLA alleles\n2. Generate peptides from variants or proteins or use peptides directly\n3. Predict HLA-binding peptides for the given set of HLA alleles\n\n\u003e [!NOTE]\n\u003e 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.\n\nFirst, prepare a samplesheet with your input data that looks as follows:\n\n`samplesheet.csv`:\n\n```csv\nsample,alleles,mhc_class,filename\nGBM_1,A*01:01;A*02:01;B*07:02;B*24:02;C*03:01;C*04:01,I,gbm_1_variants.vcf\nGBM_2,A*01:01;A*24:02;B*07:02;B*68:01;C*07:02;C*15:01,I,gbm_1_proteins.fasta\nGBM_3,A*02:01;A*24:01;B*07:02;B*08:01;C*04:01;C*07:01,I,gbm_3_peptides.tsv\n```\n\nEach 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.\n\nNow, you can run the pipeline using:\n\n```bash\nnextflow run nf-core/epitopeprediction \\\n   -profile \u003cdocker/singularity/.../institute\u003e \\\n   --input samplesheet.csv \\\n   --outdir \u003cOUTDIR\u003e\n```\n\n\u003e [!WARNING]\n\u003e This version of the pipeline does not support conda environments, due to issues with upstream dependencies.\n\u003e This means you cannot use the `conda` and `mamba` profiles. Please use `docker` or `singularity` instead.\n\u003e We hope to add support for conda environments in the future.\n\n\u003e [!WARNING]\n\u003e 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).\n\nFor 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).\n\n## Pipeline output\n\nTo 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.\nFor more details about the output files and reports, please refer to the\n[output documentation](https://nf-co.re/epitopeprediction/output).\n\n## Credits\n\nnf-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).\n\nThe 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).\n\n## Contributions and Support\n\nIf you would like to contribute to this pipeline, please see the [contributing guidelines](.github/CONTRIBUTING.md).\n\nFor 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)).\n\n## Citations\n\nIf 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)\n\nAn extensive list of references for the tools used by the pipeline can be found in the [`CITATIONS.md`](CITATIONS.md) file.\n\nYou can cite the `nf-core` publication as follows:\n\n\u003e **The nf-core framework for community-curated bioinformatics pipelines.**\n\u003e\n\u003e Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso \u0026 Sven Nahnsen.\n\u003e\n\u003e _Nat Biotechnol._ 2020 Feb 13. doi: [10.1038/s41587-020-0439-x](https://dx.doi.org/10.1038/s41587-020-0439-x).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnf-core%2Fepitopeprediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnf-core%2Fepitopeprediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnf-core%2Fepitopeprediction/lists"}