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proteinannotator

The best protein annotation pipeline in the world. Protein fasta → ??? → Annotations

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

Science Score: 57.0%

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
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    Academic email domains
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    Institutional organization owner
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (9.9%) to scientific vocabulary

Keywords

nextflow nf-core pipeline workflow
Last synced: 6 months ago · JSON representation ·

Repository

The best protein annotation pipeline in the world. Protein fasta → ??? → Annotations

Basic Info
Statistics
  • Stars: 5
  • Watchers: 209
  • Forks: 8
  • Open Issues: 32
  • Releases: 0
Topics
nextflow nf-core pipeline workflow
Created 12 months ago · Last pushed 7 months ago
Metadata Files
Readme Changelog Contributing License Code of conduct Citation

README.md

nf-core/proteinannotator

GitHub Actions CI Status GitHub Actions Linting StatusAWS CICite with Zenodo nf-test

Nextflow nf-core template version run with conda run with docker run with singularity Launch on Seqera Platform

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Introduction

nf-core/proteinannotator is a bioinformatics pipeline that runs statistics of input protein fasta files and identifies the function of proteins based on their sequence data, using state-of-the-art protein annotation tools such as InterProScan.

  1. Run (seqkit stats) to summarize input protein fasta files
  2. Functional Annotation:
    1. (InterProScan) a software tool used to analyze protein sequences by scanning them against the signatures of protein families, domains, and sites in the InterPro database, helping to identify their functional characteristics.
  3. Present QC for raw reads (MultiQC)

Protein annotator metromap. Protein fasta files are summarized with `seqkit stats`, then functionally annotated with InterProScan, DIAMOND-blastp, UniFire, and Kmerseek

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.

First, prepare a samplesheet with your input data that looks as follows:

samplesheet.csv:

csv id,fasta species1,species1_proteins.fasta species2,species2_proteins.fasta

Each row represents a fasta file of proteins from a single species.

Now, you can run the pipeline using:

bash nextflow run nf-core/proteinannotator \ -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/proteinannotator was originally written by Olga Botvinnik.

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

Citations

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

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

Citation (CITATIONS.md) 

# nf-core/proteinannotator: 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.

- [InterProScan](https://academic.oup.com/bioinformatics/article/17/9/847/206564)

> Zdobnov, Evgeni M., and Rolf Apweiler. “InterProScan – an Integration Platform for the Signature-Recognition Methods in InterPro.” Bioinformatics 17, no. 9 (September 1, 2001): 847–48. https://doi.org/10.1093/bioinformatics/17.9.847.

- [MMseqs2](https://www.nature.com/articles/nbt.3988)

> Zdobnov, Evgeni M., and Rolf Apweiler. “InterProScan – an Integration Platform for the Signature-Recognition Methods in InterPro.” Bioinformatics 17, no. 9 (September 1, 2001): 847–48. https://doi.org/10.1093/bioinformatics/17.9.847.

- [mTM-align](https://academic.oup.com/bioinformatics/article/34/10/1719/4769500)

> Dong, Runze, Zhenling Peng, Yang Zhang, and Jianyi Yang. “mTM-Align: An Algorithm for Fast and Accurate Multiple Protein Structure Alignment.” Bioinformatics 34, no. 10 (May 15, 2018): 1719–25. https://doi.org/10.1093/bioinformatics/btx828.

## 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

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  • Pull request review comment event: 14
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Last Year
  • Issues event: 32
  • Watch event: 6
  • Delete event: 1
  • Issue comment event: 67
  • Push event: 109
  • Pull request review comment event: 14
  • Pull request review event: 22
  • Pull request event: 37
  • Fork event: 7
  • Create event: 18

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