nf-core/ampliseq

What nf-core/ampliseq is

nf-core/ampliseq is an automated workflow designed to process and analyse amplicon sequencing data. It supports diverse markers including 16S, 18S, ITS, and CO1, as well as multiple-region schemes such as 5R. Originally authored by Daniel Straub and Alexander Peltzer at the Quantitative Biology Center (QBiC) and Microbial Ecology at Eberhard Karls Universität Tübingen, with later contributions from Daniel Lundin at Linnaeus University, the pipeline standardises amplicon data processing across scientific investigations.

Amplicon data often suffers from pipeline-dependent biases, technical noise, and contaminants. nf-core/ampliseq addresses this by incorporating quality control, read trimming, amplicon sequence variant (ASV) resolution, contamination removal, ribosomal RNA prediction, phylogenetic placement, and diversity analyses across short- and long-read platforms.

The pipeline accommodates paired-end or single-end Illumina reads, as well as PacBio, IonTorrent, and Oxford Nanopore data, defaulting to paired-end Illumina 16S rRNA gene sequences. It delivers taxonomical feature tables, diversity statistics, and downstream R-compatible objects to enable reliable microbial community assessments.

What the pipeline does

Raw sequencing data undergo initial quality checks using FastQC, followed by primer trimming with Cutadapt. For Oxford Nanopore reads, extra preprocessing steps are conducted using Porechop_ABI and Chopper. Amplicon Sequence Variants (ASVs) are resolved using DADA2 for Illumina, PacBio, and IonTorrent reads, or Savont for Oxford Nanopore reads. Users may optionally run post-clustering on ASVs via VSEARCH. Contamination can be identified and filtered out with decontam if control samples or quantification data are supplied.

Downstream, Barrnap predicts whether ASVs correspond to ribosomal RNA sequences, and EPA-NG carries out phylogenetic placement. Taxonomic classification is performed with DADA2 (defaulting to the SBDI-GTDB database) or alternative classifiers such as SINTAX, VSEARCH/LCA, Kraken2, or QIIME2. QIIME2 also excludes undesired taxa, calculates alpha and beta diversity metrics, generates rarefaction curves, and outputs count tables and plots. Phyloseq and TreeSE R objects are generated alongside QC and run summaries from MultiQC and R Markdown.

Key facts

What you can use it for

Bacterial and archaeal 16S rRNA gene profiling

Processing paired-end Illumina reads of regions such as V4 to identify amplicon sequence variants, classify taxonomy against SBDI-GTDB, and assess microbial diversity.

Fungal and eukaryotic metabarcoding

Analysing ITS or 18S rRNA amplicon datasets from diverse environments to taxonomically identify microbial and microeukaryotic communities.

Environmental DNA (eDNA) biodiversity monitoring

Classifying CO1 or other marker gene amplicons, filtering non-target sequences, and generating relative and absolute feature count tables.

Long-read amplicon characterisation

Denoising Oxford Nanopore data with Savont or PacBio reads with DADA2 to resolve full-length or multi-region (such as 5R) amplicon targets.

Run it here

The research agent runs nf-core/ampliseq with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.

Strengths and limits

Questions

How to cite it

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.