epitopeprediction

A bioinformatics best-practice analysis pipeline for epitope prediction and annotation

https://github.com/nf-core/epitopeprediction

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
Found 10 DOI reference(s) in README
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Academic publication links
✓
Committers with academic emails
8 of 20 committers (40.0%) from academic institutions
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (10.1%) to scientific vocabulary

Keywords

epitope epitope-prediction mhc-binding-prediction nextflow nf-core pipeline workflow

Keywords from Contributors

metagenomics mhc openms peptides mass-spectrometry immunopeptidomics dda bioinformatics annotation rna

Last synced: 6 months ago · JSON representation ·

Repository

A bioinformatics best-practice analysis pipeline for epitope prediction and annotation

Basic Info

Host: GitHub
Owner: nf-core
License: mit
Language: Nextflow
Default Branch: master
Homepage: https://nf-co.re/epitopeprediction
Size: 4.94 MB

Statistics

Stars: 47
Watchers: 157
Forks: 29
Open Issues: 14
Releases: 9

Topics

epitope epitope-prediction mhc-binding-prediction nextflow nf-core pipeline workflow

Created about 7 years ago · Last pushed 7 months ago

Metadata Files

Readme Changelog Contributing License Code of conduct Citation Codeowners

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, 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

Read variants, proteins, or peptides and HLA alleles
Generate peptides from variants or proteins or use peptides directly
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 on how to set-up Nextflow. Make sure to test your setup 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 to parse variations of nomenclatures into a uniform format.

Now, you can run the pipeline using:

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

[!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.

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 and Alexander Peltzer. Further contributions were made by Sabrina Krakau and Leon Kuchenbecker.

The pipeline was converted to Nextflow DSL2 by Christopher Mohr, Marissa Dubbelaar, Gisela Gabernet, and Jonas Scheid and further modularized by Jonas Scheid and Alina Bauer.

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.

Owner

Name: nf-core
Login: nf-core
Kind: organization
Email: core@nf-co.re

Website: http://nf-co.re
Twitter: nf_core
Repositories: 84
Profile: https://github.com/nf-core

A community effort to collect a curated set of analysis pipelines built using Nextflow.

Citation (CITATIONS.md)

# nf-core/epitopeprediction: Citations

## [nf-core](https://pubmed.ncbi.nlm.nih.gov/32055031/)

> Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Mar;38(3):276-278. doi: 10.1038/s41587-020-0439-x. PubMed PMID: 32055031.

## [Nextflow](https://pubmed.ncbi.nlm.nih.gov/28398311/)

> Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017 Apr 11;35(4):316-319. doi: 10.1038/nbt.3820. PubMed PMID: 28398311.

## Pipeline tools

- [MultiQC](https://pubmed.ncbi.nlm.nih.gov/27312411/)

  > Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016 Oct 1;32(19):3047-8. doi: 10.1093/bioinformatics/btw354. Epub 2016 Jun 16. PubMed PMID: 27312411; PubMed Central PMCID: PMC5039924.

- [SnpSift](https://dx.doi.org/10.3389/fgene.2012.00035)

  > Pablo Cingolani, Viral M. Patel, Melissa Coon, Tung Nguyen, Susan J. Land, Douglas M. Ruden and Xiangyi Lu. Using Drosophila melanogaster as a model for genotoxic chemical mutational studies with a new program, SnpSift. _Frontiers in Genetics_ 3, 35 (2012). doi: 10.3389/fgene.2012.00035.

- [Epytope (FRED2)](https://dx.doi.org/10.1093/bioinformatics/btw113)

  > Benjamin Schubert, Mathias Walzer, Hans-Philipp Brachvogel, András Szolek, Christopher Mohr, Oliver Kohlbacher. FRED 2: an immunoinformatics framework for Pythonö Bioinformatics 32(13), 2044-2046 (2016). doi: 10.1093/bioinformatics/btw113.

- [MHCflurry](https://dx.doi.org/10.1016/j.cels.2018.05.014)

  > Timothy J. O’Donnell, Alex Rubinsteyn, Maria Bonsack, Angelika B. Riemer, Uri Laserson, Jeff Hammerbacher. MHC flurry: open-source class I MHC binding affinity prediction. Cell systems 7(1), 129-132 (2018). doi: 10.1016/j.cels.2018.05.014.

- [MHCnuggets](https://dx.doi.org/10.1158/2326-6066.CIR-19-0464)

  > Xiaoshan M. Shao, Rohit Bhattacharya, Justin Huang, I.K. Ashok Sivakumar, Collin Tokheim, Lily Zheng, Dylan Hirsch, Benjamin Kaminow, Ashton Omdahl, Maria Bonsack, Angelika B. Riemer, Victor E. Velculescu, Valsamo Anagnostou, Kymberleigh A. Pagel and Rachel Karchin. High-throughput prediction of MHC class i and ii neoantigens with MHCnuggets. Cancer Immunology Research 8(3), 396-408 (2020). doi: 10.1158/2326-6066.CIR-19-0464.

- [NetMHC-4.0](https://pubmed.ncbi.nlm.nih.gov/26515819/)

  > Andreatta M, Nielsen M. Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics. 2016 Feb 15;32(4):511-7. doi: 10.1093/bioinformatics/btv639. Epub 2015 Oct 29. PMID: 26515819; PMCID: PMC6402319.

- [NetMHCpan-4.0](https://pubmed.ncbi.nlm.nih.gov/28978689/)

  > Jurtz V, Paul S, Andreatta M, Marcatili P, Peters B, Nielsen M. NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data. J Immunol. 2017 Nov 1;199(9):3360-3368. doi: 10.4049/jimmunol.1700893. Epub 2017 Oct 4. PMID: 28978689; PMCID: PMC5679736.

- [NetMHCpan-4.1](https://pubmed.ncbi.nlm.nih.gov/32406916/)

  > Reynisson B, Alvarez B, Paul S, Peters B, Nielsen M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res. 2020 Jul 2;48(W1):W449-W454. doi: 10.1093/nar/gkaa379. PMID: 32406916; PMCID: PMC7319546.

- [NetMHCII-2.3](https://pubmed.ncbi.nlm.nih.gov/29315598/)

  > Jensen KK, Andreatta M, Marcatili P, Buus S, Greenbaum JA, Yan Z, Sette A, Peters B, Nielsen M. Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology. 2018 Jul;154(3):394-406. doi: 10.1111/imm.12889. Epub 2018 Feb 6. PMID: 29315598; PMCID: PMC6002223.

- [NetMHCII-2.3](https://pubmed.ncbi.nlm.nih.gov/29315598/)

  > Jensen KK, Andreatta M, Marcatili P, Buus S, Greenbaum JA, Yan Z, Sette A, Peters B, Nielsen M. Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology. 2018 Jul;154(3):394-406. doi: 10.1111/imm.12889. Epub 2018 Feb 6. PMID: 29315598; PMCID: PMC6002223.

- [NetMHCIIpan-4.0](https://pubmed.ncbi.nlm.nih.gov/32406916/)
  > Reynisson B, Alvarez B, Paul S, Peters B, Nielsen M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res. 2020 Jul 2;48(W1):W449-W454. doi: 10.1093/nar/gkaa379. PMID: 32406916; PMCID: PMC7319546.

## Software packaging/containerisation tools

- [Anaconda](https://anaconda.com)

  > Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Anaconda, Nov. 2016. Web.

- [Bioconda](https://pubmed.ncbi.nlm.nih.gov/29967506/)

  > Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018 Jul;15(7):475-476. doi: 10.1038/s41592-018-0046-7. PubMed PMID: 29967506.

- [BioContainers](https://pubmed.ncbi.nlm.nih.gov/28379341/)

  > da Veiga Leprevost F, Grüning B, Aflitos SA, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Alvarez RV, Griss J, Nesvizhskii AI, Perez-Riverol Y. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics. 2017 Aug 15;33(16):2580-2582. doi: 10.1093/bioinformatics/btx192. PubMed PMID: 28379341; PubMed Central PMCID: PMC5870671.

- [Docker](https://dl.acm.org/doi/10.5555/2600239.2600241)

  > Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239), 2. doi: 10.5555/2600239.2600241.

- [Singularity](https://pubmed.ncbi.nlm.nih.gov/28494014/)

  > Kurtzer GM, Sochat V, Bauer MW. Singularity: Scientific containers for mobility of compute. PLoS One. 2017 May 11;12(5):e0177459. doi: 10.1371/journal.pone.0177459. eCollection 2017. PubMed PMID: 28494014; PubMed Central PMCID: PMC5426675.

GitHub Events

Total

Create event: 7
Release event: 1
Issues event: 28
Watch event: 5
Delete event: 1
Issue comment event: 90
Push event: 54
Pull request review comment event: 112
Pull request review event: 99
Pull request event: 63
Fork event: 3

Last Year

Create event: 7
Release event: 1
Issues event: 28
Watch event: 5
Delete event: 1
Issue comment event: 90
Push event: 54
Pull request review comment event: 112
Pull request review event: 99
Pull request event: 63
Fork event: 3

Committers

Last synced: over 2 years ago

All Time

Total Commits: 519
Total Committers: 20
Avg Commits per committer: 25.95
Development Distribution Score (DDS): 0.628

Past Year

Commits: 80
Committers: 4
Avg Commits per committer: 20.0
Development Distribution Score (DDS): 0.475

Top Committers

Name	Email	Commits
Christopher Mohr	c**r@u**e	193
ggabernet	g**t@q**e	107
Alexander Peltzer	a**r@g**m	65
Sabrina Krakau	s**u@q**e	27
nf-core-bot	c**e@n**e	22
marissaDubbelaar	m**r@g**m	19
jonas	j**d@l**e	14
Sabrina Krakau	s**c@g**m	14
Gisela Gabernet	g**t@g**m	13
jonasscheid	j**d@u**e	11
Leon Kuchenbecker	l**r@u**e	10
Alexander Peltzer	a****r	10
mohr	m**r@i**e	3
Christopher Mohr	c**r@q**e	2
Jonas Scheid	j**s@u**e	2
MaxUlysse	m**a@g**m	2
Marissa Dubbelaar	7****r	2
runner	r**r@f**0	1
kevinmenden	k**n@t**e	1
Sabrina Krakau	s**u@g**m	1

Committer Domains (Top 20 + Academic)

qbic.uni-tuebingen.de: 3 uni-tuebingen.de: 3 t-online.de: 1 u-081-c080.eap.uni-tuebingen.de: 1 informatik.uni-tuebingen.de: 1 live.de: 1 nf-co.re: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 55
Total pull requests: 112
Average time to close issues: 8 months
Average time to close pull requests: 19 days
Total issue authors: 12
Total pull request authors: 8
Average comments per issue: 1.38
Average comments per pull request: 1.75
Merged pull requests: 90
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 12
Pull requests: 25
Average time to close issues: about 2 months
Average time to close pull requests: 8 days
Issue authors: 6
Pull request authors: 4
Average comments per issue: 0.75
Average comments per pull request: 2.04
Merged pull requests: 20
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

jonasscheid (18)
marissaDubbelaar (18)
christopher-mohr (5)
ggabernet (3)
steffenlem (3)
gianfilippo (2)
zhenzuo2 (2)
axelwalter (1)
skrakau (1)
jen-reeve (1)
tlitfin (1)

Pull Request Authors

jonasscheid (43)
christopher-mohr (39)
nf-core-bot (31)
ggabernet (14)
axelwalter (3)
alina-bauer (3)
thomas-davis (2)
mapo9 (1)

Top Labels

Issue Labels

enhancement (32) bug (16) dsl2 (8) ready for incorporation (5) status: awaiting feedback (2) wontfix (1) good first issue (1) question (1)

Pull Request Labels

enhancement (2) do not merge (2)

Dependencies

.github/workflows/awsfulltest.yml actions

nf-core/tower-action v3 composite

.github/workflows/awstest.yml actions

nf-core/tower-action v3 composite

.github/workflows/branch.yml actions

mshick/add-pr-comment v1 composite

.github/workflows/ci-external.yml actions

actions/checkout v2 composite

.github/workflows/ci.yml actions

actions/checkout v2 composite

.github/workflows/fix-linting.yml actions

actions/checkout v3 composite
actions/setup-node v2 composite

.github/workflows/linting.yml actions

actions/checkout v2 composite
actions/setup-node v2 composite
actions/setup-python v3 composite
actions/upload-artifact v2 composite

.github/workflows/linting_comment.yml actions

dawidd6/action-download-artifact v2 composite
marocchino/sticky-pull-request-comment v2 composite

modules/nf-core/custom/dumpsoftwareversions/meta.yml cpan

modules/nf-core/gunzip/meta.yml cpan

modules/nf-core/multiqc/meta.yml cpan

pyproject.toml pypi

epitopeprediction

Science Score: 67.0%

Keywords

Keywords from Contributors

Repository

Basic Info

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Topics

Metadata Files

README.md

Introduction

Pipeline summary

Pipeline output

Credits

Contributions and Support

Citations

Owner

Citation (CITATIONS.md)

GitHub Events

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Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

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