metapep

From metagenomes to epitopes and beyond

Science Score: 77.0%

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
✓
.zenodo.json file
Found .zenodo.json file
✓
DOI references
Found 10 DOI reference(s) in README
✓
Academic publication links
Links to: ncbi.nlm.nih.gov
✓
Committers with academic emails
3 of 9 committers (33.3%) 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 (10.2%) to scientific vocabulary

Keywords

nextflow nf-core pipeline workflow

Keywords from Contributors

metagenomics nanopore bioinformatics assembly long-read-sequencing annotation binning nanopore-sequencing pipelines workflows

Last synced: 6 months ago · JSON representation ·

Repository

From metagenomes to epitopes and beyond

Basic Info

Host: GitHub
Owner: nf-core
License: mit
Language: Nextflow
Default Branch: master
Homepage: https://nf-co.re/metapep
Size: 8.4 MB

Statistics

Stars: 12
Watchers: 84
Forks: 7
Open Issues: 15
Releases: 1

Topics

nextflow nf-core pipeline workflow

Created about 4 years ago · Last pushed 9 months ago

Metadata Files

Readme Changelog Contributing License Code of conduct Citation

Introduction

nf-core/metapep is a bioinformatics best-practice analysis pipeline for epitope prediction specifically designed for metagenomes. It integrates multiple types of input (proteins, taxa, assemblies and bins), generates peptides and predicts their MHC-/HLA-affinity.

nf-core/metapep workflow overview

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.

Pipeline summary

Download proteins for input type taxa from Entrez.
Predict proteins for input type assembly or bins using Prodigal.
Generate peptides from proteins.
Split peptide files into chunks for parallel prediction and report stats.
Predict epitopes for given alleles and peptides using SYFPEITHI, MHCflurry or MHCnuggets.
Produce plots.

Usage

[!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.

bash nextflow run nf-core/metapep \ -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/metapep was written by Léon Kuchenbecker at the Kohlbacher lab, Sabrina Krakau and Till Englert at the QBiC.

We thank the following people for their extensive assistance in the development of this pipeline:

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 #metapep channel (you can join with this invite).

Citations

If you use nf-core/metapep for your analysis, please cite it using the following doi: 10.5281/zenodo.14202996

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/metapep: 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.

## [nf-test](https://www.biorxiv.org/content/10.1101/2024.05.25.595877v1)

> L. Forer, S. Schönherr Improving the Reliability and Quality of Nextflow Pipelines with nf-test. bioRxiv 2024.05.25.595877; doi: 10.1101/2024.05.25.595877

## Pipeline tools

- [Entrez](https://pubmed.ncbi.nlm.nih.gov/15608257/)

  > Maglott D, Ostell J, Pruitt KD, Tatusova T. Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res. 2005 Jan 1;33(Database issue):D54-8. doi: 10.1093/nar/gki031. Update in: Nucleic Acids Res. 2007 Jan;35(Database issue):D26-31. PMID: 15608257; PMCID: PMC539985.

- [Epytope](https://academic.oup.com/bioinformatics/article/32/13/2044/1743767)

  > Schubert, B., Walzer, M., Brachvogel, H-P., Sozolek, A., Mohr, C., and Kohlbacher, O. (2016). FRED 2 - An Immunoinformatics Framework for Python. Bioinformatics 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.

- [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.

- [pigz](https://zlib.net/pigz/)

- [Prodigal](https://pubmed.ncbi.nlm.nih.gov/20211023/)

  > Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010 Mar 8;11:119. doi: 10.1186/1471-2105-11-119. PMID: 20211023; PMCID: PMC2848648.

- [SYFPEITHI](https://pubmed.ncbi.nlm.nih.gov/10602881/)

  > Hans-Georg Rammensee, Jutta Bachmann, Niels Nikolaus Emmerich, Oskar Alexander Bachor, Stefan Stevanovic: SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics (1999) 50: 213-219

## Python Packages

- [Python](https://www.python.org/)

  > Python Core Team (2022). Python: A dynamic, open source programming language. Python Software Foundation. https://www.python.org/.

- [biopython](https://academic.oup.com/bioinformatics/article/25/11/1422/330687)

  > Cock PA, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, Friedberg I, Hamelryck T, Kauff F, Wilczynski B and de Hoon MJL (2009) Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics, 25, 1422-1423. https://doi.org/10.1093/bioinformatics/btp163.

- [numpy](https://www.nature.com/articles/s41586-020-2649-2)

  > Harris, C.R., Millman, K.J., van der Walt, S.J. et al. Array programming with NumPy. Nature 585, 357–362 (2020). DOI: 10.1038/s41586-020-2649-2. https://www.nature.com/articles/s41586-020-2649-2.

- [pandas](https://doi.org/10.5281/zenodo.3509134)

  > The pandas development team. (2023). pandas-dev/pandas: Pandas (v2.0.3). Zenodo. https://doi.org/10.5281/zenodo.8092754

## R Packages

- [R](https://www.R-project.org/)

  > R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

- [data.table](https://cran.r-project.org/package=data.table)

  > Dowle Matt (2022). data.table: Extension of 'data.frame'.

- [dplyr](https://dplyr.tidyverse.org)

  > Wickham H, François R, Henry L, Müller K, Vaughan D (2023). dplyr: A Grammar of Data Manipulation. https://dplyr.tidyverse.org, https://github.com/tidyverse/dplyr.

- [ggplot2](https://cran.r-project.org/package=ggplot2)

  > H. Wickham (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.

- [ggpubr](https://cran.r-project.org/package=ggpubr)

  > Kassambara Alboukadel (2023). ggpubr: 'ggplot2' Based Publication Ready Plots.

- [optparse](https://CRAN.R-project.org/package=optparse)

  > Trevor L Davis (2022). optparse: Command Line Option Parser.

- [stringr](https://stringr.tidyverse.org)

  > Wickham H (2022). stringr: Simple, Consistent Wrappers for Common String Operations. https://stringr.tidyverse.org, https://github.com/tidyverse/stringr.

## 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: 2
Watch event: 3
Issue comment event: 19
Push event: 14
Pull request review event: 12
Pull request review comment event: 9
Pull request event: 24
Fork event: 1

Last Year

Create event: 7
Release event: 1
Issues event: 2
Watch event: 3
Issue comment event: 19
Push event: 14
Pull request review event: 12
Pull request review comment event: 9
Pull request event: 24
Fork event: 1

Committers

Last synced: over 2 years ago

All Time

Total Commits: 578
Total Committers: 9
Avg Commits per committer: 64.222
Development Distribution Score (DDS): 0.588

Past Year

Commits: 346
Committers: 6
Avg Commits per committer: 57.667
Development Distribution Score (DDS): 0.312

Top Committers

Name	Email	Commits
Till Englert	t**6@g**m	238
Sabrina Krakau	s**u@q**e	143
Sabrina Krakau	s**c@g**m	84
Leon Kuchenbecker	l**r@u**e	28
AntoniaSchuster	a**r@q**e	28
nf-core-bot	c**e@n**e	24
Till E	6****t	22
Antonia Schuster	5****r	6
Sabrina Krakau	s**u@g**m	5

Committer Domains (Top 20 + Academic)

qbic.uni-tuebingen.de: 2 nf-co.re: 1 uni-tuebingen.de: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 30
Total pull requests: 122
Average time to close issues: 3 months
Average time to close pull requests: 23 days
Total issue authors: 4
Total pull request authors: 7
Average comments per issue: 0.73
Average comments per pull request: 1.68
Merged pull requests: 83
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 6
Pull requests: 18
Average time to close issues: N/A
Average time to close pull requests: 5 days
Issue authors: 2
Pull request authors: 4
Average comments per issue: 0.0
Average comments per pull request: 0.72
Merged pull requests: 8
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

tillenglert (14)
skrakau (14)
AntoniaSchuster (1)
jen-reeve (1)

Pull Request Authors

tillenglert (60)
nf-core-bot (26)
skrakau (23)
AntoniaSchuster (10)
xXFloBaerXx (1)
mashehu (1)
FriederikeHanssen (1)

Top Labels

Issue Labels

enhancement (20) bug (8) documentation (3) duplicate (1)

Pull Request Labels

enhancement (3) documentation (1)

Dependencies

.github/workflows/awsfulltest.yml actions

actions/upload-artifact v3 composite
nf-core/tower-action v3 composite

.github/workflows/awstest.yml actions

actions/upload-artifact v3 composite
nf-core/tower-action v3 composite

.github/workflows/branch.yml actions

mshick/add-pr-comment v1 composite

.github/workflows/ci.yml actions

actions/checkout v3 composite
actions/checkout v2 composite
nf-core/setup-nextflow v1 composite

.github/workflows/fix-linting.yml actions

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

.github/workflows/linting.yml actions

actions/checkout v3 composite
actions/setup-node v3 composite
actions/setup-python v4 composite
actions/upload-artifact v3 composite
mshick/add-pr-comment v1 composite
nf-core/setup-nextflow v1 composite
psf/black stable composite

.github/workflows/linting_comment.yml actions

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

.github/workflows/clean-up.yml actions

actions/stale v7 composite

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

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

pyproject.toml pypi

.github/workflows/download_pipeline.yml actions

actions/setup-python 0a5c61591373683505ea898e09a3ea4f39ef2b9c composite
eWaterCycle/setup-singularity 931d4e31109e875b13309ae1d07c70ca8fbc8537 composite
nf-core/setup-nextflow v1 composite

.github/workflows/release-announcements.yml actions

actions/setup-python 0a5c61591373683505ea898e09a3ea4f39ef2b9c composite
rzr/fediverse-action master composite
zentered/bluesky-post-action 80dbe0a7697de18c15ad22f4619919ceb5ccf597 composite

subworkflows/nf-core/utils_nextflow_pipeline/meta.yml cpan

subworkflows/nf-core/utils_nfcore_pipeline/meta.yml cpan

subworkflows/nf-core/utils_nfvalidation_plugin/meta.yml cpan

modules/nf-core/multiqc/environment.yml pypi

modules/nf-core/prodigal/environment.yml pypi

metapep

Science Score: 77.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

Introduction

Pipeline summary

Usage

Pipeline output

Credits

Contributions and Support

Citations

Owner

Citation (CITATIONS.md)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

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Issue Labels

Pull Request Labels

Dependencies