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reportho

nf-core pipeline for comparative analysis of ortholog predictions

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

Science Score: 57.0%

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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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    Low similarity (9.1%) to scientific vocabulary

Keywords

msa nextflow nf-core ortholog phylogeny pipeline proteomics workflow
Last synced: 6 months ago · JSON representation ·

Repository

nf-core pipeline for comparative analysis of ortholog predictions

Basic Info
  • Host: GitHub
  • Owner: nf-core
  • License: mit
  • Language: Nextflow
  • Default Branch: master
  • Homepage: https://nf-co.re/reportho
  • Size: 2.83 MB
Statistics
  • Stars: 11
  • Watchers: 176
  • Forks: 6
  • Open Issues: 8
  • Releases: 2
Topics
msa nextflow nf-core ortholog phylogeny pipeline proteomics workflow
Created almost 2 years ago · Last pushed 8 months ago
Metadata Files
Readme Changelog Contributing License Code of conduct Citation

README.md

nf-core/reportho

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

Nextflow run with conda run with docker run with singularity Launch on Seqera Platform

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Introduction

nf-core/reportho is a bioinformatics pipeline that compares and summarizes orthology predictions for one or a set of query proteins. For each query (or its closest annotated homolog), it fetches ortholog lists from public databases, calculates the agreement of the obtained predictions(pairwise and global) and finally generates a consensus list of orthologs with the desired level of confidence. Optionally, it offers common analysis on the consensus orthologs, such as MSA and phylogeny reconstruction. Additionally, it generates a clean, human-readable report of the results.

nf-core-reportho tube map

  1. Obtain Query Information: identification of Uniprot ID and taxon ID for the query (or its closest homolog if the fasta file is used as input instead of the Uniprot ID).
  2. Fetch Orthologs: fetching of ortholog predictions from public databases, either through API or from local snapshot.
  3. Compare and Assemble: calculation of agreement statistics, creation of ortholog lists, selection of the consensus list.

Steps that follow can be skipped with --skip_downstream in batch analysis.

  1. Fetch Sequences: fetching of protein sequences for the orthologs from Uniprot.
  2. Fetch Structures: fetching of protein structure from the AlphaFold Database. Only performed if --use_structures is true.
  3. Align Sequences: multiple sequence alignment. 3D-COFFEE is used if --use_structures is true, T-COFFEE otherwise.
  4. Reconstruct Phylogeny: character-based phylogenetic reconstruction with ML or ME. Only performed if at least one of --use_iqtree or --use_fastme is true.
  5. Generate Report: human-readable HTML report generation.

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:

csv title="samplesheet_fasta.csv" id,fasta BicD2,data/bicd2.fasta HBB,data/hbb.fasta

or if you know the UniProt ID of the protein you can provide it directly:

csv title="samplesheet.csv" id,query BicD2,Q8TD16 HBB,P68871

[!NOTE] If you provide both a FASTA file and a UniProt ID only the latter will be used.

Now, you can run the pipeline using:

bash nextflow run nf-core/reportho \ -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.

Report image

The code to create the image producing the pipeline report is available under this GitHub repository.

Credits

nf-core/reportho was originally written by Igor Trujnara (@itrujnara).

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 #reportho 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/reportho: 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

- [OMA](htpps://omabrowser.org)

> Adrian M Altenhoff, Clément-Marie Train, Kimberly J Gilbert, Ishita Mediratta, Tarcisio Mendes de Farias, David Moi, Yannis Nevers, Hale-Seda Radoykova, Victor Rossier, Alex Warwick Vesztrocy, Natasha M Glover, Christophe Dessimoz, OMA orthology in 2021: website overhaul, conserved isoforms, ancestral gene order and more, Nucleic Acids Research, Volume 49, Issue D1, 8 January 2021, Pages D373–D379, https://doi.org/10.1093/nar/gkaa1007

- [PANTHER](https://pantherdb.org)

> Thomas PD, Ebert D, Muruganujan A, Mushayahama T, Albou L-P, Mi H. PANTHER: Making genome-scale phylogenetics accessible to all. Protein Science. 2022; 31: 8–22. https://doi.org/10.1002/pro.4218

- [OrthoInspector](https://lbgi.fr/orthoinspector)

> Yannis Nevers, Arnaud Kress, Audrey Defosset, Raymond Ripp, Benjamin Linard, Julie D Thompson, Olivier Poch, Odile Lecompte, OrthoInspector 3.0: open portal for comparative genomics, Nucleic Acids Research, Volume 47, Issue D1, 08 January 2019, Pages D411–D418, https://doi.org/10.1093/nar/gky1068

- [EggNOG](https://eggnog5.embl.de)

> Jaime Huerta-Cepas, Damian Szklarczyk, Davide Heller, Ana Hernández-Plaza, Sofia K Forslund, Helen Cook, Daniel R Mende, Ivica Letunic, Thomas Rattei, Lars J Jensen, Christian von Mering, Peer Bork, eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses, Nucleic Acids Research, Volume 47, Issue D1, 08 January 2019, Pages D309–D314, https://doi.org/10.1093/nar/gky1085

- [UniProt](https://uniprot.org)

> The UniProt Consortium , UniProt: the Universal Protein Knowledgebase in 2023, Nucleic Acids Research, Volume 51, Issue D1, 6 January 2023, Pages D523–D531, https://doi.org/10.1093/nar/gkac1052

- [UniProt ID Mapping](https://uniprot.org/id-mapping)

> Huang H, McGarvey PB, Suzek BE, Mazumder R, Zhang J, Chen Y, Wu CH. A comprehensive protein-centric ID mapping service for molecular data integration. Bioinformatics. 2011 Apr 15;27(8):1190-1. doi: 10.1093/bioinformatics/btr101. PMID: 21478197; PMCID: PMC3072559.

- [AlphaFold](https://deepmind.google/technologies/alphafold)

> Jumper, J., Evans, R., Pritzel, A. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). https://doi.org/10.1038/s41586-021-03819-2

- [AlphaFold Database](https://alphafold.ebi.ac.uk)

> Mihaly Varadi, Stephen Anyango, Mandar Deshpande, Sreenath Nair, Cindy Natassia, Galabina Yordanova, David Yuan, Oana Stroe, Gemma Wood, Agata Laydon, Augustin Žídek, Tim Green, Kathryn Tunyasuvunakool, Stig Petersen, John Jumper, Ellen Clancy, Richard Green, Ankur Vora, Mira Lutfi, Michael Figurnov, Andrew Cowie, Nicole Hobbs, Pushmeet Kohli, Gerard Kleywegt, Ewan Birney, Demis Hassabis, Sameer Velankar, AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models, Nucleic Acids Research, Volume 50, Issue D1, 7 January 2022, Pages D439–D444, https://doi.org/10.1093/nar/gkab1061

- [T-COFFEE](https://tcoffee.org)

> Notredame C, Higgins DG, Heringa J. T-Coffee: A novel method for fast and accurate multiple sequence alignment. J Mol Biol. 2000 Sep 8;302(1):205-17. doi: 10.1006/jmbi.2000.4042. PMID: 10964570.

- [IQTREE](https://iqtree.org)

> B.Q. Minh, H.A. Schmidt, O. Chernomor, D. Schrempf, M.D. Woodhams, A. von Haeseler, R. Lanfear (2020) IQ-TREE 2: New models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol., 37:1530-1534. https://doi.org/10.1093/molbev/msaa015

> D.T. Hoang, O. Chernomor, A. von Haeseler, B.Q. Minh, L.S. Vinh (2018) UFBoot2: Improving the ultrafast bootstrap approximation. Mol. Biol. Evol., 35:518–522. https://doi.org/10.1093/molbev/msx281

- [FastME](https://atgc-montpellier.fr/fastme/)

> Vincent Lefort, Richard Desper, Olivier Gascuel, FastME 2.0: A Comprehensive, Accurate, and Fast Distance-Based Phylogeny Inference Program, Molecular Biology and Evolution, Volume 32, Issue 10, October 2015, Pages 2798–2800, https://doi.org/10.1093/molbev/msv150

## 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
  • Issues event: 23
  • Watch event: 4
  • Delete event: 6
  • Issue comment event: 44
  • Push event: 24
  • Pull request review event: 67
  • Pull request review comment event: 52
  • Pull request event: 28
  • Fork event: 1
  • Create event: 4
Last Year
  • Issues event: 23
  • Watch event: 4
  • Delete event: 6
  • Issue comment event: 44
  • Push event: 24
  • Pull request review event: 67
  • Pull request review comment event: 52
  • Pull request event: 28
  • Fork event: 1
  • Create event: 4

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 9
  • Total pull requests: 12
  • Average time to close issues: 29 days
  • Average time to close pull requests: 17 days
  • Total issue authors: 3
  • Total pull request authors: 3
  • Average comments per issue: 0.56
  • Average comments per pull request: 1.0
  • Merged pull requests: 6
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 9
  • Pull requests: 12
  • Average time to close issues: 29 days
  • Average time to close pull requests: 17 days
  • Issue authors: 3
  • Pull request authors: 3
  • Average comments per issue: 0.56
  • Average comments per pull request: 1.0
  • Merged pull requests: 6
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • itrujnara (16)
  • luisas (5)
  • JoseEspinosa (2)
  • jen-reeve (1)
  • charlie-pugh (1)
Pull Request Authors
  • itrujnara (41)
  • nf-core-bot (9)
  • JoseEspinosa (8)
  • alessiovignoli (1)
  • luisas (1)
Top Labels
Issue Labels
enhancement (17) bug (2) documentation (1)
Pull Request Labels
enhancement (1)

Dependencies

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