*nfcore/ampliseq* is a bioinformatics analysis pipeline used for 16S rRNA amplicon sequencing data (currently supported is Illumina paired end data).

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible.

Quick Start

  1. Install nextflow

  2. Install any of Docker, Singularity or Podman for full pipeline reproducibility (please only use Conda as a last resort; see docs)

  3. Download the pipeline and test it on a minimal dataset with a single command:

    nextflow run nf-core/ampliseq -profile test,<docker/singularity/podman/conda/institute>  

    Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.

  4. Start running your own analysis!

    nextflow run nf-core/ampliseq -profile <docker/singularity/podman/conda/institute> --input "data" --FW_primer GTGYCAGCMGCCGCGGTAA --RV_primer GGACTACNVGGGTWTCTAAT --metadata "data/Metadata.tsv"  

See usage docs for all of the available options when running the pipeline.


The nf-core/ampliseq pipeline comes with documentation about the pipeline: usage and output.

The workflow processes raw data from FastQ inputs (FastQC), trims primer sequences from the reads (Cutadapt), imports data into QIIME2, generates amplicon sequencing variants (ASV, DADA2), classifies features against the SILVA v132 database, excludes unwanted taxa, produces absolute and relative feature/taxa count tables and plots, plots alpha rarefaction curves, computes alpha and beta diversity indices and plots thereof, and finally calls differentially abundant taxa (ANCOM). See the output documentation for more details of the results.


These scripts were originally written for use at the Quantitative Biology Center (QBiC) and Microbial Ecology, Center for Applied Geosciences, part of Eberhard Karls Universität Tübingen (Germany) by Daniel Straub (@d4straub) and Alexander Peltzer (@apeltzer).

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


If you use nf-core/ampliseq for your analysis, please cite the ampliseq article as follows:

Daniel Straub, Nia Blackwell, Adrian Langarica-Fuentes, Alexander Peltzer, Sven Nahnsen, Sara Kleindienst *Interpretations of Environmental Microbial Community Studies Are Biased by the Selected 16S rRNA (Gene) Amplicon Sequencing Pipeline* *Frontiers in Microbiology* 2020, 11:2652 doi: 10.3389/fmicb.2020.550420.

You can cite the nf-core/ampliseq zenodo record for a specific version using the following doi: 10.5281/zenodo.1493841

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.
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