# ![nf-core/taxprofiler](docs/images/nf-core-taxprofiler_logo_custom_light.png#gh-light-mode-only) ![nf-core/taxprofiler](docs/images/nf-core-taxprofiler_logo_custom_dark.png#gh-dark-mode-only) [![AWS CI](https://img.shields.io/badge/CI%20tests-full%20size-FF9900?labelColor=000000&logo=Amazon%20AWS)](https://nf-co.re/taxprofiler/results)[![Cite with Zenodo](http://img.shields.io/badge/DOI-10.5281/zenodo.7728364-1073c8?labelColor=000000)](https://doi.org/10.5281/zenodo.7728364) [![Nextflow](https://img.shields.io/badge/nextflow%20DSL2-%E2%89%A522.10.1-23aa62.svg)](https://www.nextflow.io/) [![run with conda](http://img.shields.io/badge/run%20with-conda-3EB049?labelColor=000000&logo=anaconda)](https://docs.conda.io/en/latest/) [![run with docker](https://img.shields.io/badge/run%20with-docker-0db7ed?labelColor=000000&logo=docker)](https://www.docker.com/) [![run with singularity](https://img.shields.io/badge/run%20with-singularity-1d355c.svg?labelColor=000000)](https://sylabs.io/docs/) [![Launch on Nextflow Tower](https://img.shields.io/badge/Launch%20%F0%9F%9A%80-Nextflow%20Tower-%234256e7)](https://tower.nf/launch?pipeline=https://github.com/nf-core/taxprofiler) [![Get help on Slack](http://img.shields.io/badge/slack-nf--core%20%23taxprofiler-4A154B?labelColor=000000&logo=slack)](https://nfcore.slack.com/channels/taxprofiler)[![Follow on Twitter](http://img.shields.io/badge/twitter-%40nf__core-1DA1F2?labelColor=000000&logo=twitter)](https://twitter.com/nf_core)[![Follow on Mastodon](https://img.shields.io/badge/mastodon-nf__core-6364ff?labelColor=FFFFFF&logo=mastodon)](https://mstdn.science/@nf_core)[![Watch on YouTube](http://img.shields.io/badge/youtube-nf--core-FF0000?labelColor=000000&logo=youtube)](https://www.youtube.com/c/nf-core) ## Introduction **nf-core/taxprofiler** is a bioinformatics best-practice analysis pipeline for taxonomic classification and profiling of shotgun 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, produces standardised output tables. ## Pipeline summary ![](docs/images/taxprofiler_tube.png) 1. Read QC ([`FastQC`](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) or [`falco`](https://github.com/smithlabcode/falco) as an alternative option) 2. Performs optional read pre-processing - Adapter clipping and merging (short-read: [fastp](https://github.com/OpenGene/fastp), [AdapterRemoval2](https://github.com/MikkelSchubert/adapterremoval); long-read: [porechop](https://github.com/rrwick/Porechop)) - Low complexity and quality filtering (short-read: [bbduk](https://jgi.doe.gov/data-and-tools/software-tools/bbtools/), [PRINSEQ++](https://github.com/Adrian-Cantu/PRINSEQ-plus-plus); long-read: [Filtlong](https://github.com/rrwick/Filtlong)) - Host-read removal (short-read: [BowTie2](http://bowtie-bio.sourceforge.net/bowtie2/); long-read: [Minimap2](https://github.com/lh3/minimap2)) - Run merging 3. Supports statistics for host-read removal ([Samtools](http://www.htslib.org/)) 4. Performs taxonomic classification and/or profiling using one or more of: - [Kraken2](https://ccb.jhu.edu/software/kraken2/) - [MetaPhlAn3](https://huttenhower.sph.harvard.edu/metaphlan/) - [MALT](https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/algorithms-in-bioinformatics/software/malt/) - [DIAMOND](https://github.com/bbuchfink/diamond) - [Centrifuge](https://ccb.jhu.edu/software/centrifuge/) - [Kaiju](https://kaiju.binf.ku.dk/) - [mOTUs](https://motu-tool.org/) - [KrakenUniq](https://github.com/fbreitwieser/krakenuniq) 5. Perform optional post-processing with: - [bracken](https://ccb.jhu.edu/software/bracken/) 6. Standardises output tables ([`Taxpasta`](https://taxpasta.readthedocs.io)) 7. Present QC for raw reads ([`MultiQC`](http://multiqc.info/)) 8. Plotting Kraken2, Centrifuge, Kaiju and MALT results ([`Krona`](https://hpc.nih.gov/apps/kronatools.html)) ## Usage > **Note** > If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/usage/installation) on how > to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline) > 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 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: `databases.csv`: ``` tool,db_name,db_params,db_path kraken2,db2,--quick,///kraken2/testdb-kraken2.tar.gz metaphlan3,db1,,///metaphlan3/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 \ --input samplesheet.csv \ --databases databases.csv \ --outdir \ --run_kraken2 --run_metaphlan3 ``` > **Warning:** > Please provide pipeline parameters via the CLI (as above) 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](https://nf-co.re/usage/configuration#custom-configuration-files). For more details, please refer to the [usage documentation](https://nf-co.re/taxprofiler/usage) and the [parameter documentation](https://nf-co.re/taxprofiler/parameters). ## Pipeline output To see the results of a test run with a full size dataset refer to the [results](https://nf-co.re/taxprofiler/results) tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the [output documentation](https://nf-co.re/taxprofiler/output). ## Credits nf-core/taxprofiler was originally written by James A. Fellows Yates, Sofia Stamouli, Moritz E. Beber, and the nf-core/taxprofiler team. ### Team - [James A. Fellows Yates](https://github.com/jfy133) - [Sofia Stamouli](https://github.com/sofstam) - [Moritz E. Beber](https://github.com/Midnighter) We thank the following people for their contributions to the development of this pipeline: - [Lauri Mesilaakso](https://github.com/ljmesi) - [Tanja Normark](https://github.com/talnor) - [Maxime Borry](https://github.com/maxibor) - [Thomas A. Christensen II](https://github.com/MillironX) - [Jianhong Ou](https://github.com/jianhong) - [Rafal Stepien](https://github.com/rafalstepien) - [Mahwash Jamy](https://github.com/mjamy) ### Acknowledgments We also are grateful for the feedback and comments from: - The general [nf-core/community](https://nf-co.re/community) And specifically to - [Alex Hübner](https://github.com/alexhbnr) - [Lily Andersson Lee](https://github.com/LilyAnderssonLee) ❤️ also goes to [Zandra Fagernäs](https://github.com/ZandraFagernas) for the logo. ## Contributions and Support If you would like to contribute to this pipeline, please see the [contributing guidelines](.github/CONTRIBUTING.md). For further information or help, don't hesitate to get in touch on the [Slack `#taxprofiler` channel](https://nfcore.slack.com/channels/taxprofiler) (you can join with [this invite](https://nf-co.re/join/slack)). ## Citations If you use nf-core/taxprofiler for your analysis, please cite it using the following doi: [10.5281/zenodo.7728364](https://doi.org/10.5281/zenodo.7728364) An extensive list of references for the tools used by the pipeline can be found in the [`CITATIONS.md`](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](https://dx.doi.org/10.1038/s41587-020-0439-x).