nfcore/ampliseq is a bioinformatics analysis pipeline used for amplicon sequencing, supporting denoising of any amplicon and, currently, taxonomic assignment of 16S, ITS and 18S amplicons. Supported is paired-end Illumina or single-end Illumina, PacBio and IonTorrent data. Default is the analysis of 16S rRNA gene amplicons sequenced paired-end with Illumina.
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.
By default, the pipeline currently performs the following:
- Sequencing quality control (FastQC)
- Trimming of reads (Cutadapt)
- Infer Amplicon Sequence Variants (ASVs) (DADA2)
- Taxonomical classification using DADA2 or QIIME2
- 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 (QIIME2)
- Calls differentially abundant taxa (ANCOM)
- Overall pipeline run summaries (MultiQC)
-
Install
Nextflow
(>=21.04.0
) -
Install any of
Docker
,Singularity
,Podman
,Shifter
orCharliecloud
for full pipeline reproducibility (please only useConda
as a last resort; see docs) -
Download the pipeline and test it on a minimal dataset with a single command:
nextflow run nf-core/ampliseq -profile test,<docker/singularity/podman/shifter/charliecloud/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 eitherdocker
orsingularity
and set the appropriate execution settings for your local compute environment. - If you are using
singularity
then the pipeline will auto-detect this and attempt to download the Singularity images directly as opposed to performing a conversion from Docker images. If you are persistently observing issues downloading Singularity images directly due to timeout or network issues then please use the--singularity_pull_docker_container
parameter to pull and convert the Docker image instead. Alternatively, it is highly recommended to use thenf-core download
command to pre-download all of the required containers before running the pipeline and to set theNXF_SINGULARITY_CACHEDIR
orsingularity.cacheDir
Nextflow options to be able to store and re-use the images from a central location for future pipeline runs. - If you are using
conda
, it is highly recommended to use theNXF_CONDA_CACHEDIR
orconda.cacheDir
settings to store the environments in a central location for future pipeline runs.
- 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
-
Start running your own analysis!
#16S rRNA gene amplicon analysis of Illumina paired-end data nextflow run nf-core/ampliseq -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input "data" --FW_primer "GTGYCAGCMGCCGCGGTAA" --RV_primer "GGACTACNVGGGTWTCTAAT" --metadata "data/Metadata.tsv"
See usage docs and parameter docs for all of the available options when running the pipeline.
The nf-core/ampliseq pipeline comes with documentation about the pipeline usage, parameters and output.
nf-core/ampliseq was originally written by Daniel Straub (@d4straub) and Alexander Peltzer (@apeltzer) for use at the Quantitative Biology Center (QBiC) and Microbial Ecology, Center for Applied Geosciences, part of Eberhard Karls Universität Tübingen (Germany).
We thank the following people for their extensive assistance in the development of this pipeline (in alphabetical order):
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
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.