taxprofiler

Highly parallelised multi-taxonomic profiling of shotgun short- and long-read metagenomic data

Science Score: 57.0%

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Low similarity (9.4%) to scientific vocabulary

Keywords

bioinformatics classification illumina long-reads metagenomics microbiome nanopore nextflow nf-core pathogen pipeline profiling shotgun taxonomic-classification taxonomic-profiling workflow

Keywords from Contributors

pipelines workflows dsl2 nf-test synapse sra geo ddbj ena fastq

Last synced: 4 months ago · JSON representation ·

Repository

Highly parallelised multi-taxonomic profiling of shotgun short- and long-read metagenomic data

Basic Info

Host: GitHub
Owner: nf-core
License: mit
Language: Nextflow
Default Branch: master
Homepage: https://nf-co.re/taxprofiler
Size: 16.1 MB

Statistics

Stars: 161
Watchers: 169
Forks: 56
Open Issues: 47
Releases: 16

Topics

bioinformatics classification illumina long-reads metagenomics microbiome nanopore nextflow nf-core pathogen pipeline profiling shotgun taxonomic-classification taxonomic-profiling workflow

Created almost 4 years ago · Last pushed 4 months ago

Metadata Files

Readme Changelog Contributing License Code of conduct Citation Codeowners

Introduction

nf-core/taxprofiler is a bioinformatics best-practice analysis pipeline for taxonomic classification and profiling of shotgun short- and long-read metagenomic data. It allows for in-parallel taxonomic identification of reads or taxonomic abundance estimation with multiple classification and profiling tools against multiple databases, and produces standardised output tables for facilitating results comparison between different tools and databases.

Pipeline summary

Read QC (FastQC or falco as an alternative option)
Performs optional read pre-processing
- Adapter clipping and merging (short-read: fastp, AdapterRemoval2; long-read: porechop, Porechop_ABI)
- Low complexity and quality filtering (short-read: bbduk, PRINSEQ++; long-read: Filtlong), Nanoq
- Host-read removal (short-read: BowTie2; long-read: Minimap2)
- Run merging
Supports statistics metagenome coverage estimation (Nonpareil) and for host-read removal (Samtools)
Performs taxonomic classification and/or profiling using one or more of:
- Kraken2
- MetaPhlAn
- MALT
- DIAMOND
- Centrifuge
- Kaiju
- mOTUs
- KrakenUniq
- KMCP
- ganon
Perform optional post-processing with:
- bracken
Standardises output tables (Taxpasta)
Present QC for raw reads (MultiQC)
Plotting Kraken2, Centrifuge, Kaiju and MALT results (Krona)

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.csv" sample,run_accession,instrument_platform,fastq_1,fastq_2,fasta 2612,run1,ILLUMINA,2612_run1_R1.fq.gz,, 2612,run2,ILLUMINA,2612_run2_R1.fq.gz,, 2612,run3,ILLUMINA,2612_run3_R1.fq.gz,2612_run3_R2.fq.gz,

Each row represents a fastq file (single-end), a pair of fastq files (paired end), or a fasta (with long reads).

Additionally, you will need a database sheet that looks as follows:

csv title="databases.csv" tool,db_name,db_params,db_path kraken2,db2,--quick,/<path>/<to>/kraken2/testdb-kraken2.tar.gz metaphlan,db1,,/<path>/<to>/metaphlan/metaphlan_database/

That includes directories or .tar.gz archives containing databases for the tools you wish to run the pipeline against.

Now, you can run the pipeline using:

bash nextflow run nf-core/taxprofiler \ -profile <docker/singularity/.../institute> \ --input samplesheet.csv \ --databases databases.csv \ --outdir <OUTDIR> \ --run_kraken2 --run_metaphlan

[!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/taxprofiler was originally written by James A. Fellows Yates, Sofia Stamouli, Moritz E. Beber, Lili Andersson-Li, and the nf-core/taxprofiler team.

Team

We thank the following people for their contributions to the development of this pipeline:

Acknowledgments

We also are grateful for the feedback and comments from:

The general nf-core/community

And specifically to

Alex Hübner

❤️ also goes to Zandra Fagernäs for the logo.

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

Citations

If you use nf-core/taxprofiler for your analysis, please cite it using the following doi: 10.1101/2023.10.20.563221.

Stamouli, S., Beber, M. E., Normark, T., Christensen II, T. A., Andersson-Li, L., Borry, M., Jamy, M., nf-core community, & Fellows Yates, J. A. (2023). nf-core/taxprofiler: Highly parallelised and flexible pipeline for metagenomic taxonomic classification and profiling. In bioRxiv (p. 2023.10.20.563221). https://doi.org/10.1101/2023.10.20.563221

For the latest version of the code, cite the Zenodo doi: 10.5281/zenodo.7728364

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/taxprofiler: Citations

## [nf-core](https://pubmed.ncbi.nlm.nih.gov/32055031/)

> Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. In Nature Biotechnology (Vol. 38, Issue 3). https://doi.org/10.1038/s41587-020-0439-x

## [Nextflow](https://pubmed.ncbi.nlm.nih.gov/28398311/)

> Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. In Nature Biotechnology (Vol. 35, Issue 4). https://doi.org/10.1038/nbt.3820

## Pipeline tools

- [FastQC](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)

  > Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online].

- [MultiQC](https://pubmed.ncbi.nlm.nih.gov/27312411/)

  > Ewels, P., Magnusson, M., Lundin, S., & Käller, M. (2016). MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 32(19). https://doi.org/10.1093/bioinformatics/btw354

- [falco](https://doi.org/10.12688/f1000research.21142.2)

  > de Sena Brandine, G., & Smith, A. D. (2021). Falco: high-speed FastQC emulation for quality control of sequencing data. F1000Research, 8(1874), 1874. https://doi.org/10.12688/f1000research.21142.2

- [fastp](https://doi.org/10.1093/bioinformatics/bty560)

  > Chen, S., Zhou, Y., Chen, Y., & Gu, J. (2018). fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics , 34(17), i884–i890. https://doi.org/10.1093/bioinformatics/bty560

- [AdapterRemoval2](https://doi.org/10.1186/s13104-016-1900-2)

  > Schubert, M., Lindgreen, S., & Orlando, L. (2016). AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Research Notes, 9, 88. https://doi.org/10.1186/s13104-016-1900-2

- [Nonpareil](https://doi.org/10.1128/mSystems.00039-18)
  - Rodriguez-R, L. M., Gunturu, S., Tiedje, J. M., Cole, J. R., & Konstantinidis, K. T. (2018). Nonpareil 3: Fast Estimation of Metagenomic Coverage and Sequence Diversity. mSystems, 3(3). https://doi.org/10.1128/mSystems.00039-18

- [Porechop](https://github.com/rrwick/Porechop)

  > Wick, R. R., Judd, L. M., Gorrie, C. L., & Holt, K. E. (2017). Completing bacterial genome assemblies with multiplex MinION sequencing. Microbial Genomics, 3(10), e000132. https://doi.org/10.1099/mgen.0.000132

- [Porechop_ABI](https://github.com/bonsai-team/Porechop_ABI)

  > Bonenfant, Q., Noé, L., & Touzet, H. (2023). Porechop_ABI: discovering unknown adapters in Oxford Nanopore Technology sequencing reads for downstream trimming. Bioinformatics Advances, 3(1):vbac085. https://10.1093/bioadv/vbac085

- [Filtlong](https://github.com/rrwick/Filtlong)

  > Wick R (2021) Filtlong, URL: https://github.com/rrwick/Filtlong

- [nanoq](https://github.com/esteinig/nanoq)

  > Steinig, E., & Coin, L. (2022). Nanoq: ultra-fast quality control for nanopore reads. Journal of Open Source Software, 7(69). https://doi.org/10.21105/joss.02991

- [BBTools](http://sourceforge.net/projects/bbmap/)

  > Bushnell B. (2022) BBMap, URL: http://sourceforge.net/projects/bbmap/

- [PRINSEQ++](https://doi.org/10.7287/peerj.preprints.27553v1)

  > Cantu, V. A., Sadural, J., & Edwards, R. (2019). PRINSEQ++, a multi-threaded tool for fast and efficient quality control and preprocessing of sequencing datasets (e27553v1). PeerJ Preprints. https://doi.org/10.7287/peerj.preprints.27553v1

- [Bowtie2](https://doi.org/10.1038/nmeth.1923)

  > Langmead, B., & Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nature Methods, 9(4), 357–359. https://doi.org/10.1038/nmeth.1923

- [minimap2](https://doi.org/10.1093/bioinformatics/bty191)

  > Li, H. (2018). Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics , 34(18), 3094–3100. https://doi.org/10.1093/bioinformatics/bty191

- [SAMTools](https://doi.org/10.1093/gigascience/giab008)

  > Danecek, P., Bonfield, J. K., Liddle, J., Marshall, J., Ohan, V., Pollard, M. O., Whitwham, A., Keane, T., McCarthy, S. A., Davies, R. M., & Li, H. (2021). Twelve years of SAMtools and BCFtools. GigaScience, 10(2). https://doi.org/10.1093/gigascience/giab008

- [Bracken](https://doi.org/10.7717/peerj-cs.104)

  > Lu, J., Breitwieser, F. P., Thielen, P., & Salzberg, S. L. (2017). Bracken: estimating species abundance in metagenomics data. PeerJ. Computer Science, 3(e104), e104. https://doi.org/10.7717/peerj-cs.104

- [Kraken2](https://doi.org/10.1186/s13059-019-1891-0)

  > Wood, D. E., Lu, J., & Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. Genome Biology, 20(1), 257. https://doi.org/10.1186/s13059-019-1891-0

- [KrakenUniq](https://doi.org/10.1186/s13059-018-1568-0)

  > Breitwieser, F. P., Baker, D. N., & Salzberg, S. L. (2018). KrakenUniq: confident and fast metagenomics classification using unique k-mer counts. Genome Biology, 19(1), 198. https://doi.org/10.1186/s13059-018-1568-0

- [MetaPhlAn](https://doi.org/10.1038/s41587-023-01688-w)

  > Blanco-Míguez, A., Beghini, F., Cumbo, F., McIver, L. J., Thompson, K. N., Zolfo, M., Manghi, P., Dubois, L., Huang, K. D., Thomas, A. M., Nickols, W. A., Piccinno, G., Piperni, E., Punčochář, M., Valles-Colomer, M., Tett, A., Giordano, F., Davies, R., Wolf, J., … Segata, N. (2023). Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. Nature Biotechnology, 1–12. https://doi.org/10.1038/s41587-023-01688-w

- [MALT](https://doi.org/10.1038/s41559-017-0446-6)

  > Vågene, Å. J., Herbig, A., Campana, M. G., Robles García, N. M., Warinner, C., Sabin, S., Spyrou, M. A., Andrades Valtueña, A., Huson, D., Tuross, N., Bos, K. I., & Krause, J. (2018). Salmonella enterica genomes from victims of a major sixteenth-century epidemic in Mexico. Nature Ecology & Evolution, 2(3), 520–528. https://doi.org/10.1038/s41559-017-0446-6

- [MEGAN](https://doi.org/10.1371/journal.pcbi.1004957)

  > Huson, D. H., Beier, S., Flade, I., Górska, A., El-Hadidi, M., Mitra, S., Ruscheweyh, H.-J., & Tappu, R. (2016). MEGAN Community Edition - Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data. PLoS Computational Biology, 12(6), e1004957. https://doi.org/10.1371/journal.pcbi.1004957

- [DIAMOND](https://doi.org/10.1038/nmeth.3176)

  > Buchfink, B., Xie, C., & Huson, D. H. (2015). Fast and sensitive protein alignment using DIAMOND. Nature Methods, 12(1), 59–60. https://doi.org/10.1038/nmeth.3176

- [Centrifuge](https://doi.org/10.1101/gr.210641.116)

  > Kim, D., Song, L., Breitwieser, F. P., & Salzberg, S. L. (2016). Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Research, 26(12), 1721–1729. https://doi.org/10.1101/gr.210641.116

- [Kaiju](https://doi.org/10.1038/ncomms11257)

  > Menzel, P., Ng, K. L., & Krogh, A. (2016). Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nature Communications, 7, 11257. https://doi.org/10.1038/ncomms11257

- [mOTUs](https://doi.org/10.1186/s40168-022-01410-z)

  > Ruscheweyh, H.-J., Milanese, A., Paoli, L., Karcher, N., Clayssen, Q., Keller, M. I., Wirbel, J., Bork, P., Mende, D. R., Zeller, G., & Sunagawa, S. (2022). Cultivation-independent genomes greatly expand taxonomic-profiling capabilities of mOTUs across various environments. Microbiome, 10(1), 212. https://doi.org/10.1186/s40168-022-01410-z

- [KMCP](https://doi.org/10.1093/bioinformatics/btac845)

  > Shen, W., Xiang, H., Huang, T., Tang, H., Peng, M., Cai, D., Hu, P., & Ren, H. (2023). KMCP: accurate metagenomic profiling of both prokaryotic and viral populations by pseudo-mapping. Bioinformatics (Oxford, England), 39(1). https://doi.org/10.1093/bioinformatics/btac845

- [ganon](https://doi.org/10.1093/bioinformatics/btaa458)

  > Piro, V. C., Dadi, T. H., Seiler, E., Reinert, K., & Renard, B. Y. (2020). Ganon: Precise metagenomics classification against large and up-to-date sets of reference sequences. Bioinformatics (Oxford, England), 36(Suppl_1), i12–i20. https://doi.org/10.1093/bioinformatics/btaa458

- [Krona](https://doi.org/10.1186/1471-2105-12-385)

  > Ondov, B. D., Bergman, N. H., & Phillippy, A. M. (2011). Interactive metagenomic visualization in a Web browser. BMC Bioinformatics, 12. https://doi.org/10.1186/1471-2105-12-385

- [TAXPASTA](https://doi.org/10.21105/joss.05627)

  > Beber, M. E., Borry, M., Stamouli, S., & Fellows Yates, J. A. (2023). TAXPASTA: TAXonomic Profile Aggregation and STAndardisation. Journal of Open Source Software, 8(87), 5627. https://doi.org/10.21105/joss.05627

## 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/)

  > Dale, R., Grüning, B., Sjödin, A., Rowe, J., Chapman, B. A., Tomkins-Tinch, C. H., Valieris, R., Batut, B., Caprez, A., Cokelaer, T., Yusuf, D., Beauchamp, K. A., Brinda, K., Wollmann, T., Corguillé, G. Le, Ryan, D., Bretaudeau, A., Hoogstrate, Y., Pedersen, B. S., … Köster, J. (2018). Bioconda: Sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7). https://doi.org/10.1038/s41592-018-0046-7

- [BioContainers](https://pubmed.ncbi.nlm.nih.gov/28379341/)

  > Da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: An open-source and community-driven framework for software standardization. Bioinformatics, 33(16). https://doi.org/10.1093/bioinformatics/btx192

- [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, G. M., Sochat, V., & Bauer, M. W. (2017). Singularity: Scientific containers for mobility of compute. PLoS ONE, 12(5). https://doi.org/10.1371/journal.pone.0177459

## Data

- [Maixner (2021)](https://doi.org/10.1016/j.cub.2021.09.031) (CI Test Data)

  > Maixner, F., Sarhan, M. S., Huang, K. D., Tett, A., Schoenafinger, A., Zingale, S., Blanco-Míguez, A., Manghi, P., Cemper-Kiesslich, J., Rosendahl, W., Kusebauch, U., Morrone, S. R., Hoopmann, M. R., Rota-Stabelli, O., Rattei, T., Moritz, R. L., Oeggl, K., Segata, N., Zink, A., … Kowarik, K. (2021). Hallstatt miners consumed blue cheese and beer during the Iron Age and retained a non-Westernized gut microbiome until the Baroque period. Current Biology, 31(23). https://doi.org/10.1016/j.cub.2021.09.031

- [Meslier (2022)](https://doi.org/10.1038/s41597-022-01762-z) (AWS Full Test data)

  > Meslier, V., Quinquis, B., Da Silva, K., Plaza Oñate, F., Pons, N., Roume, H., Podar, M., & Almeida, M. (2022). Benchmarking second and third-generation sequencing platforms for microbial metagenomics. Scientific Data, 9(1). https://doi.org/10.1038/s41597-022-01762-z

GitHub Events

Total

Create event: 33
Release event: 3
Issues event: 48
Watch event: 20
Delete event: 34
Issue comment event: 298
Push event: 202
Pull request event: 109
Pull request review event: 177
Pull request review comment event: 202
Fork event: 21

Last Year

Create event: 33
Release event: 3
Issues event: 48
Watch event: 20
Delete event: 34
Issue comment event: 298
Push event: 202
Pull request event: 109
Pull request review event: 177
Pull request review comment event: 202
Fork event: 21

Committers

Last synced: about 2 years ago

All Time

Total Commits: 1,220
Total Committers: 22
Avg Commits per committer: 55.455
Development Distribution Score (DDS): 0.53

Past Year

Commits: 780
Committers: 13
Avg Commits per committer: 60.0
Development Distribution Score (DDS): 0.565

Top Committers

Name	Email	Commits
James Fellows Yates	j**3@g**m	574
sofstam	s**i@s**e	186
LilyAnderssonLee	l**e@g**m	119
Sofia Stamouli	9****m	90
Moritz E. Beber	m**r@p**t	78
Lili Andersson-Li	6****e	55
ljmesi	3****i	29
nf-core-bot	c**e@n**e	28
Thomas A. Christensen II	2****X	14
JIANHONG OU	j****g	11
Mahwash Jamy	m**y@g**m	9
Rafal Stepien	4****n	5
maxibor	m**y@g**m	4
Lauri Mesilaakso	j**o@g**m	3
hkaspersen	h**n@p**m	3
Rafal Stepien	r**5@g**m	3
Husen M. Umer	h****g	2
Håkon Kaspersen	h**n@v**o	2
Rob Syme	r**e@g**m	2
Mahwash Jamy	4****y	1
Alex Hübner	a**r@g**m	1
ZandraFagernas	4****s	1

Committer Domains (Top 20 + Academic)

vetinst.no: 1 nf-co.re: 1 posteo.net: 1 scilifelab.se: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 144
Total pull requests: 263
Average time to close issues: 4 months
Average time to close pull requests: 11 days
Total issue authors: 42
Total pull request authors: 22
Average comments per issue: 2.13
Average comments per pull request: 2.04
Merged pull requests: 194
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 31
Pull requests: 77
Average time to close issues: 13 days
Average time to close pull requests: 6 days
Issue authors: 17
Pull request authors: 14
Average comments per issue: 1.0
Average comments per pull request: 1.53
Merged pull requests: 46
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

jfy133 (41)
sofstam (24)
LilyAnderssonLee (15)
MajoroMask (8)
muniheart (4)
alexhbnr (4)
Midnighter (4)
artur-matysik (4)
prototaxites (3)
SannaAb (3)
erinyoung (2)
AnotherSimon (2)
kneubehl (1)
vmkalbskopf (1)
maxibor (1)

Pull Request Authors

jfy133 (104)
LilyAnderssonLee (66)
sofstam (47)
nf-core-bot (16)
Midnighter (4)
muniheart (3)
mashehu (3)
mirpedrol (2)
sateeshperi (2)
AlexHoratio (2)
vmikk (2)
husensofteng (2)
ilight1542 (1)
microlei (1)
maxibor (1)

Top Labels

Issue Labels

enhancement (85) bug (51) question (5) low-priority (5) documentation (2) first-release (2) high-priority (2) help wanted (1) feature-request (1) WIP (1) requires-module (1) needs-upstream-fix (1)

Pull Request Labels

enhancement (9) WIP (4) bug (1) high-priority (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

Wandalen/wretry.action v1.0.11 composite
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/adapterremoval/meta.yml cpan

modules/nf-core/bbmap/bbduk/meta.yml cpan

modules/nf-core/bowtie2/align/meta.yml cpan

modules/nf-core/bowtie2/build/meta.yml cpan

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

modules/nf-core/bracken/combinebrackenoutputs/meta.yml cpan

modules/nf-core/cat/fastq/meta.yml cpan

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

modules/nf-core/centrifuge/kreport/meta.yml cpan

modules/nf-core/custom/dumpsoftwareversions/meta.yml cpan

modules/nf-core/diamond/blastx/meta.yml cpan

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

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

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

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

modules/nf-core/ganon/classify/meta.yml cpan

modules/nf-core/ganon/report/meta.yml cpan

modules/nf-core/ganon/table/meta.yml cpan

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

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

modules/nf-core/kaiju/kaiju2krona/meta.yml cpan

modules/nf-core/kaiju/kaiju2table/meta.yml cpan

modules/nf-core/kmcp/profile/meta.yml cpan

modules/nf-core/kmcp/search/meta.yml cpan

modules/nf-core/kraken2/kraken2/meta.yml cpan

modules/nf-core/krakentools/combinekreports/meta.yml cpan

modules/nf-core/krakentools/kreport2krona/meta.yml cpan

modules/nf-core/krakenuniq/preloadedkrakenuniq/meta.yml cpan

modules/nf-core/krona/ktimporttaxonomy/meta.yml cpan

modules/nf-core/krona/ktimporttext/meta.yml cpan

modules/nf-core/malt/run/meta.yml cpan

modules/nf-core/megan/rma2info/meta.yml cpan

modules/nf-core/metaphlan/mergemetaphlantables/meta.yml cpan

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

modules/nf-core/minimap2/align/meta.yml cpan

modules/nf-core/minimap2/index/meta.yml cpan

modules/nf-core/motus/merge/meta.yml cpan

modules/nf-core/motus/profile/meta.yml cpan

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

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

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

modules/nf-core/samtools/fastq/meta.yml cpan

modules/nf-core/samtools/index/meta.yml cpan

modules/nf-core/samtools/stats/meta.yml cpan

modules/nf-core/samtools/view/meta.yml cpan

modules/nf-core/taxpasta/merge/meta.yml cpan

modules/nf-core/taxpasta/standardise/meta.yml cpan

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

pyproject.toml pypi

.github/workflows/release-announcments.yml actions

actions/setup-python v4 composite
rzr/fediverse-action master composite
zentered/bluesky-post-action v0.0.2 composite

modules/nf-core/megan/rma2info/environment.yml pypi

taxprofiler

Science Score: 57.0%

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