pangenome

Renders a collection of sequences into a pangenome graph. https://doi.org/10.1093/bioinformatics/btae609.

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

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
✓
.zenodo.json file
Found .zenodo.json file
✓
DOI references
Found 13 DOI reference(s) in README
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Academic publication links
✓
Committers with academic emails
1 of 8 committers (12.5%) from academic institutions
○
Institutional organization owner
○
JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (10.4%) to scientific vocabulary

Keywords

nextflow nf-core pangenome pipeline workflow

Keywords from Contributors

metagenomics pipelines workflows bioinformatics assembly nanopore dsl2 nf-test annotation long-read-sequencing

Last synced: 6 months ago · JSON representation ·

Repository

Renders a collection of sequences into a pangenome graph. https://doi.org/10.1093/bioinformatics/btae609.

Basic Info

Host: GitHub
Owner: nf-core
License: mit
Language: Nextflow
Default Branch: master
Homepage: https://nf-co.re/pangenome
Size: 4.28 MB

Statistics

Stars: 91
Watchers: 80
Forks: 20
Open Issues: 15
Releases: 5

Topics

nextflow nf-core pangenome pipeline workflow

Created over 5 years ago · Last pushed 8 months ago

Metadata Files

Readme Changelog Contributing License Code of conduct Citation

Introduction

nf-core/pangenome is a bioinformatics best-practice analysis pipeline for pangenome graph construction. The pipeline renders a collection of sequences into a pangenome graph. Its goal is to build a graph that is locally directed and acyclic while preserving large-scale variation. Maintaining local linearity is important for interpretation, visualization, mapping, comparative genomics, and reuse of pangenome graphs.

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.

Pangenome Workflow

Pipeline summary

All versus all alignment (WFMASH)
Graph induction (SEQWISH)
Graph normalization (SMOOTHXG)
Remove redundancy (GFAFFIX)
Graph statistics and qualitative visualizations (ODGI)
Combine diagnostic information into a report (MULTIQC)

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.

Now, you can run the pipeline using:

bash nextflow run nf-core/pangenome -r dev --input <BGZIPPED_FASTA> --n_haplotypes <NUM_HAPS_IN_FASTA> --outdir <OUTDIR> -profile <docker/singularity/podman/shifter/charliecloud/conda/institute>

[!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.

Advantages over `PGGB`

This Nextflow pipeline version's major advantage is that it can distribute the usually computationally heavy all versus all alignment step across a whole cluster. It is capable of splitting the initial approximate alignments into problems of equal size. The base-level alignments are then distributed across several processes. Assuming you have a cluster with 10 nodes and you are the only one using it, we would recommend to set --wfmash_chunks 10. If you have a cluster with 20 nodes, but you have to share it with others, maybe setting it to --wfmash_chunks 10 could be a good fit, because then you don't have to wait too long for your jobs to finish.

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/pangenome was originally adapted from PGGB by Simon Heumos, Michael Heuer.

Simon Heumos is currently the sole developer.

Many thanks to all who have helped out and contributed along the way, including (but not limited to)*:

* Listed in alphabetical order

Acknowledgments

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 #pangenome channel (you can join with this invite), or contact me Simon Heumos.

Citations

If you use nf-core/pangenome for your analysis, please cite the Bioinformatics manuscript using the following doi: 10.1093/bioinformatics/btae609. You can also cite the current release's doi: 10.5281/zenodo.8202636.

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.

Changelog

CHANGELOG

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/pangenome: 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: https://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: https://10.1038/nbt.3820. PubMed PMID: 28398311.

## Pipeline tools

- [GFAFFIX](https://github.com/marschall-lab/GFAffix)

  > Liao W, Asri M, Ebler J, Doerr D, Haukness M, Hickey G, Lu S, Lucas J K, Monlong J, Abel H J, Buonaiuto S, Chang X H, Cheng H, Chu J, Colonna V, Eizenga J M, Feng X, Fischer C, Fulton R S, Garg S, Groza C, Guarracino A, Harvey W T, Heumos S, Howe K, Jain M, Lu T, Markello C, Martin F J, Mitchell M W, Munson K M, Mwaniki M N, Novak A M, Olsen H E, Pesout T, Porubsky D, Prins P, Sibbesen J A, Tomlinson C, Villani F, Vollger M R, Human Pangenome Reference Consortium, Bourque G, Chaisson M J P, Flicek P, Phillippy A M, Zook J M, Eichler E E,Haussler D, Jarvis E D, Miga K H, Wang T, Garrison E, Marschall T, Hall I, Li H, Paten B. A Draft Human Pangenome Reference. Nature 617, 312–324 (2023). https://doi.org/10.1038/s41586-023-05896-x.

- [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: https://10.1093/bioinformatics/btw354. Epub 2016 Jun 16. PubMed PMID: 27312411; PubMed Central PMCID: PMC5039924.

- [NET2COMMUNITIES](https://github.com/pangenome/pggb/blob/master/scripts/net2communities.py)

  > Traag, VA, Waltman, L & van Eck, NJ. From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep 9, 5233 (2019). https://doi.org/10.1038/s41598-019-41695-z.

- [ODGI](https://pubmed.ncbi.nlm.nih.gov/35552372/)

  > Guarracino A, Heumos S, Nahnsen S, Prins P, Garrison E. ODGI: understanding pangenome graphs. Bioinformatics. Volume 38. Issue 13. July 2022. Pages 3319–3326. https://doi.org/10.1093/bioinformatics/btac308.

  > Heumos S, Guarracino A, Schmelzle J N M, Li J, Zhang Z, Nahnsen S, Prins P, Garrison E. Pangenome graph layout by Path-Guided Stochastic Gradient Descent. bioRxiv. https://www.biorxiv.org/content/10.1101/2023.09.22.558964v1.

- [PGGB](https://www.biorxiv.org/content/10.1101/2023.04.05.535718v1)

  > Garrison E, Guarracino A, Heumos S, Villani F, Bao Z, Tattini L, Hagmann J, Vorbrugg S, Marco-Sola S, Kubica S, Ashbrook D G, Thorell K, Rusholme-Pilcher R L, Liti G, Rudbeck E, Nahnsen S, Yang Z, Moses M N, Nobrega F L, Wu Y, Chen H, de Ligt J, Sudmant P H, Soranzo N, Colonna V, Williams R W, Prins P. Building pangenome graphs. bioRxiv. 2023.04.05.535718. doi: https://doi.org/10.1101/2023.04.05.535718.

  > Guarracino A, Buonaiuto S, de Lima L G, Potapova T, Rhie A, Koren S, Rubinstein B, Fischer C, Human Pangenome Reference Consortium, Gerton J L, Phillippy A M, Colonna V, Garrison E. Recombination between heterologous human acrocentric chromosomes. Nature 617, 335–343 (2023). https://doi.org/10.1038/s41586-023-05976-y.

  > Liao W, Asri M, Ebler J, Doerr D, Haukness M, Hickey G, Lu S, Lucas J K, Monlong J, Abel H J, Buonaiuto S, Chang X H, Cheng H, Chu J, Colonna V, Eizenga J M, Feng X, Fischer C, Fulton R S, Garg S, Groza C, Guarracino A, Harvey W T, Heumos S, Howe K, Jain M, Lu T, Markello C, Martin F J, Mitchell M W, Munson K M, Mwaniki M N, Novak A M, Olsen H E, Pesout T, Porubsky D, Prins P, Sibbesen J A, Tomlinson C, Villani F, Vollger M R, Human Pangenome Reference Consortium, Bourque G, Chaisson M J P, Flicek P, Phillippy A M, Zook J M, Eichler E E,Haussler D, Jarvis E D, Miga K H, Wang T, Garrison E, Marschall T, Hall I, Li H, Paten B. A Draft Human Pangenome Reference. Nature 617, 312–324 (2023). https://doi.org/10.1038/s41586-023-05896-x.

- [SAMTOOLS](https://pubmed.ncbi.nlm.nih.gov/19505943/)

  > Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R; 1000 Genome Project Data Processing Subgroup. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009 Aug 15;25(16):2078-9. doi: https://10.1093/bioinformatics/btp352. Epub 2009 Jun 8. PMID: 19505943; PMCID: PMC2723002.

- [SEQWISH](https://pubmed.ncbi.nlm.nih.gov/36448683/)

  > Garrison E, Guarracino A. Unbiased pangenome graphs. Bioinformatics. 2023 Jan 1;39(1):btac743. doi: https://10.1093/bioinformatics/btac743. PMID: 36448683; PMCID: PMC9805579.

- [SMOOTHXG](https://www.biorxiv.org/content/10.1101/2023.04.05.535718v1)

  > Garrison E, Guarracino A, Heumos S, Villani F, Bao Z, Tattini L, Hagmann J, Vorbrugg S, Marco-Sola S, Kubica S, Ashbrook D G, Thorell K, Rusholme-Pilcher R L, Liti G, Rudbeck E, Nahnsen S, Yang Z, Moses M N, Nobrega F L, Wu Y, Chen H, de Ligt J, Sudmant P H, Soranzo N, Colonna V, Williams R W, Prins P. Building pangenome graphs. bioRxiv. 2023.04.05.535718. doi: https://doi.org/10.1101/2023.04.05.535718.

- [VCFLIB](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009123)

  > Garrison E, Kronenberg ZN, Dawson ET, Pedersen BS, Prins P (2022) A spectrum of free software tools for processing the VCF variant call format: vcflib, bio-vcf, cyvcf2, hts-nim and slivar. PLOS Computational Biology 18(5): e1009123. https://doi.org/10.1371/journal.pcbi.1009123.

- [VG](https://pubmed.ncbi.nlm.nih.gov/30125266/)

  > Garrison E et al. Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nature biotechnology vol. 36,9 (2018): 875-879. doi: https://10.1038/nbt.4227.

- [WFMASH](https://github.com/waveygang/wfmash)

  > Guarracino A, Mwaniki N, Marco-Sola S, Garrison E. Wfmash: whole-chromosome pairwise alignment using the hierarchical wavefront algorithm. 2024. https://github.com/waveygang/wfmash.

## 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: https://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: https://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: https://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: https://10.1371/journal.pone.0177459. eCollection 2017. PubMed PMID: 28494014; PubMed Central PMCID: PMC5426675.

GitHub Events

Total

Create event: 13
Release event: 1
Issues event: 18
Watch event: 23
Delete event: 3
Issue comment event: 94
Push event: 28
Pull request review comment event: 22
Pull request review event: 31
Pull request event: 56
Fork event: 3

Last Year

Create event: 13
Release event: 1
Issues event: 18
Watch event: 23
Delete event: 3
Issue comment event: 94
Push event: 28
Pull request review comment event: 22
Pull request review event: 31
Pull request event: 56
Fork event: 3

Committers

Last synced: over 2 years ago

All Time

Total Commits: 427
Total Committers: 8
Avg Commits per committer: 53.375
Development Distribution Score (DDS): 0.124

Past Year

Commits: 291
Committers: 3
Avg Commits per committer: 97.0
Development Distribution Score (DDS): 0.027

Top Committers

Name	Email	Commits
subwaystation	s**s@q**e	374
nf-core-bot	c**e@n**e	21
AndreaGuarracino	a**o@o**m	12
Lukas Heumos	l**s@p**t	10
Michael L Heuer	h**h@a**g	6
Lukas Heumos	l**s@g**m	2
James A. Fellows Yates	j**3@g**m	1
kevinmenden	k**n@t**e	1

Committer Domains (Top 20 + Academic)

t-online.de: 1 acm.org: 1 posteo.net: 1 nf-co.re: 1 qbic.uni-tuebingen.de: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 54
Total pull requests: 118
Average time to close issues: 7 months
Average time to close pull requests: 12 days
Total issue authors: 13
Total pull request authors: 9
Average comments per issue: 1.91
Average comments per pull request: 1.53
Merged pull requests: 84
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 9
Pull requests: 27
Average time to close issues: 7 days
Average time to close pull requests: 3 days
Issue authors: 5
Pull request authors: 4
Average comments per issue: 1.56
Average comments per pull request: 1.89
Merged pull requests: 17
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

subwaystation (36)
heuermh (7)
mictadlo (5)
OZTaekOppa (1)
DPI-NZ (1)
nvnieuwk (1)
msgr0 (1)
dbrami (1)
maxulysse (1)
JWDebler (1)
SarahBeecroft (1)
jen-reeve (1)
chenhijy (1)
apeltzer (1)
Zethson (1)

Pull Request Authors

subwaystation (100)
nf-core-bot (31)
heuermh (13)
AndreaGuarracino (2)
KevinMenden (2)
adamrtalbot (1)
ewels (1)
Zethson (1)
nvnieuwk (1)
heringerp (1)

Top Labels

Issue Labels

enhancement (42) bug (13) documentation (1) help wanted (1) question (1) chore (1) advanced (1)

Pull Request Labels

Dependencies

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pangenome

Science Score: 67.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

Introduction

Pipeline summary

Usage

Advantages over PGGB

Pipeline output

Credits

Acknowledgments

Contributions and Support

Citations

Changelog

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

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Dependencies

Advantages over `PGGB`