pgdb

ProteomeGenomics Database Creation - Nextflow Pipeline

https://github.com/bigbio/pgdb

Science Score: 41.0%

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  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
  • .zenodo.json file
  • DOI references
    Found 12 DOI reference(s) in README
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    Links to: zenodo.org
  • Academic email domains
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  • Scientific vocabulary similarity
    Low similarity (10.4%) to scientific vocabulary

Keywords

cosmic ensembl mass-spectrometry nextflow nf-core proteogenomics proteomics
Last synced: 6 months ago · JSON representation ·

Repository

ProteomeGenomics Database Creation - Nextflow Pipeline

Basic Info
  • Host: GitHub
  • Owner: bigbio
  • License: mit
  • Language: Nextflow
  • Default Branch: dev
  • Homepage:
  • Size: 2.9 MB
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  • Watchers: 6
  • Forks: 11
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Topics
cosmic ensembl mass-spectrometry nextflow nf-core proteogenomics proteomics
Created about 5 years ago · Last pushed over 3 years ago
Metadata Files
Readme Changelog Contributing License Code of conduct Citation

README.md

nf-core/pgdb nf-core/pgdb

GitHub Actions CI Status GitHub Actions Linting Status AWS CI Cite with Zenodo

Nextflow run with conda run with docker run with singularity Launch on Nextflow Tower

Get help on Slack Follow on Twitter Watch on YouTube

Introduction

nf-core/pgdb is a bioinformatics pipeline to generate proteogenomics databases. pgdb allows users to create proteogenomics databases using EMSEMBL as the reference proteome database. Three different major databases can be attached to the final proteogenomics database:

  • The reference proteome (ENSEMBL Reference proteome)
  • Non canonical proteins: pseudo-genes, sORFs, lncRNA.
  • Variants: COSMIC, cBioPortal, GENOMAD variants

The pipeline allows to estimate decoy proteins with different methods and attach them to the final proteogenomics database.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible.

Quick Start

  1. Install Nextflow (>=21.10.3)

  2. Install any of Docker, Singularity (you can follow this tutorial), Podman, Shifter or Charliecloud for full pipeline reproducibility (you can use Conda both to install Nextflow itself and also to manage software within pipelines. Please only use it within pipelines as a last resort; see docs).

  3. Download the pipeline and test it on a minimal dataset with a single command:

console nextflow run nf-core/pgdb -profile test,YOURPROFILE --outdir <OUTDIR>

Note that some form of configuration will be needed so that Nextflow knows how to fetch the required software. This is usually done in the form of a config profile (YOURPROFILE in the example command above). You can chain multiple config profiles in a comma-separated string.

  • The pipeline comes with config profiles called docker, singularity, podman, shifter, charliecloud and conda which instruct the pipeline to use the named tool for software management. For example, -profile test,docker.
  • Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.
  • If you are using singularity, please use the nf-core download command to download images first, before running the pipeline. Setting the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options enables you to store and re-use the images from a central location for future pipeline runs.
  • If you are using conda, it is highly recommended to use the NXF_CONDA_CACHEDIR or conda.cacheDir settings to store the environments in a central location for future pipeline runs.
  1. Start running your own analysis!

bash nextflow run nf-core/pgdb -profile <docker/singularity/podman/conda/institute> --ncrna true --pseudogenes true --altorfs true

This will create a proteogenomics database with the ENSEMBL reference proteome and non canonical proteins like pseudo genes, non coding rnas or alternative open reading frames.

See usage docs for all of the available options when running the pipeline.

Pipeline Summary

By default, the pipeline currently performs the following:

ProteoGenomics Database

  • Download protein databases from ENSEMBL
  • Translate from Genomics Variant databases into ProteoGenomics Databases (COSMIC, GNOMAD)
  • Add to a Reference proteomics database, non-coding RNAs + pseudogenes.
  • Compute Decoy for a proteogenomics databases

Documentation

The nf-core/pgdb pipeline comes with documentation about the pipeline: usage and output.

Credits

nf-core/pgdb was originally written by Husen M. Umer (EMBL-EBI) & Yasset Perez-Riverol (Karolinska Institute)

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

Citations

The pgdb pipeline should be cited using the following citation:

Umer HM, Audain E, Zhu Y, Pfeuffer J, Sachsenberg T, Lehtiö J, Branca R, Perez-Riverol Y. Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides.

Bioinformatics. 2021 Dec 14;38(5):1470–2. doi: 10.1093/bioinformatics/btab838. Epub ahead of print. PMID: 34904638; PMCID: PMC8825679.

additionally you can cite the pipeline directly with the following doi: 10.5281/zenodo.4722662

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: BigBio Stack
  • Login: bigbio
  • Kind: organization
  • Email: proteomicsstack@gmail.com
  • Location: Cambridge, UK

Provide big data solutions Bioinformatics

Citation (CITATIONS.md)

# nf-core/pgdb: Citations

## [pgdb](https://pubmed.ncbi.nlm.nih.gov/34904638/)

> Husen M Umer, Enrique Audain, Yafeng Zhu, Julianus Pfeuffer, Timo Sachsenberg, Janne Lehtiö, Rui M Branca, Yasset Perez-Riverol, Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides, Bioinformatics, Volume 38, Issue 5, 1 March 2022, Pages 1470–1472, https://doi.org/10.1093/bioinformatics/btab838

## [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

- [pypgatk](https://pubmed.ncbi.nlm.nih.gov/34904638/)

  > Husen M Umer, Enrique Audain, Yafeng Zhu, Julianus Pfeuffer, Timo Sachsenberg, Janne Lehtiö, Rui M Branca, Yasset Perez-Riverol, Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides, Bioinformatics, Volume 38, Issue 5, 1 March 2022, Pages 1470–1472, https://doi.org/10.1093/bioinformatics/btab838

## Data sources

- [ENSEMBL](https://pubmed.ncbi.nlm.nih.gov/31691826/)

  > Yates, A. D., Achuthan, P., Akanni, W., Allen, J., Allen, J., Alvarez-Jarreta, J., ... & Flicek, P. (2020). Ensembl 2020. Nucleic acids research, 48(D1), D682-D688.

- [COSMIC](https://pubmed.ncbi.nlm.nih.gov/15188009/)

  > Bamford, S., Dawson, E., Forbes, S., Clements, J., Pettett, R., Dogan, A., ... & Wooster, R. (2004). The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website. British journal of cancer, 91(2), 355-358.

- [cBioPortal](https://pubmed.ncbi.nlm.nih.gov/23550210/)

  > Gao, J., Aksoy, B. A., Dogrusoz, U., Dresdner, G., Gross, B., Sumer, S. O., ... & Schultz, N. (2013). Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Science signaling, 6(269), pl1-pl1.

## 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)

- [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.

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