#  [](https://www.nextflow.io/) [](https://nf-co.re/) [](https://zenodo.org/badge/latestdoi/150448201) [](https://doi.org/10.3389/fmicb.2020.550420) [](https://github.com/nf-core/ampliseq/actions) [](https://github.com/nf-core/ampliseq/actions) [](https://www.nextflow.io/) [](https://bioconda.github.io/) [](https://hub.docker.com/r/nfcore/ampliseq) [](https://nfcore.slack.com/channels/ampliseq) ## Introduction **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](https://www.nextflow.io), 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`](https://nf-co.re/usage/installation) 2. Install any of [`Docker`](https://docs.docker.com/engine/installation/), [`Singularity`](https://www.sylabs.io/guides/3.0/user-guide/) or [`Podman`](https://podman.io/) for full pipeline reproducibility _(please only use [`Conda`](https://conda.io/miniconda.html) as a last resort; see [docs](https://nf-co.re/usage/configuration#basic-configuration-profiles))_ 3. Download the pipeline and test it on a minimal dataset with a single command: ```bash nextflow run nf-core/ampliseq -profile test, ``` > Please check [nf-core/configs](https://github.com/nf-core/configs#documentation) to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use `-profile ` 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! ```bash nextflow run nf-core/ampliseq -profile --input "data" --FW_primer GTGYCAGCMGCCGCGGTAA --RV_primer GGACTACNVGGGTWTCTAAT --metadata "data/Metadata.tsv" ``` See [usage docs](https://nf-co.re/ampliseq/usage) for all of the available options when running the pipeline. ## Documentation The nf-core/ampliseq pipeline comes with documentation about the pipeline: [usage](https://nf-co.re/ampliseq/usage) and [output](https://nf-co.re/ampliseq/output). The workflow processes raw data from FastQ inputs ([FastQC](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)), trims primer sequences from the reads ([Cutadapt](https://journal.embnet.org/index.php/embnetjournal/article/view/200)), imports data into [QIIME2](https://www.nature.com/articles/s41587-019-0209-9), generates amplicon sequencing variants (ASV, [DADA2](https://www.nature.com/articles/nmeth.3869)), classifies features against the [SILVA](https://www.arb-silva.de/) [v132](https://www.arb-silva.de/documentation/release-132/) 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](https://www.ncbi.nlm.nih.gov/pubmed/26028277)). See the [output documentation](docs/output.md) for more details of the results. ## Credits These scripts were originally written for use at the [Quantitative Biology Center (QBiC)](http://www.qbic.life) and [Microbial Ecology, Center for Applied Geosciences](http://www.uni-tuebingen.de/de/104325), part of Eberhard Karls Universität Tübingen (Germany) by Daniel Straub ([@d4straub](https://github.com/d4straub)) and Alexander Peltzer ([@apeltzer](https://github.com/apeltzer)). ## 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 `#ampliseq` channel](https://nfcore.slack.com/channels/ampliseq) (you can join with [this invite](https://nf-co.re/join/slack)). ## Citation 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](https://doi.org/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](https://zenodo.org/badge/latestdoi/150448201) 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). > ReadCube: [Full Access Link](https://rdcu.be/b1GjZ)