dānaSeq illumina_amplicon: primer-aware amplicon denoising with a Python port of DADA2

Motivation: Marker-gene amplicon studies of environmental samples are increasingly assembled from many sequencing runs, several primer pairs and public archive submissions, some with primers already removed. Processing such collections reproducibly on shared high-performance computing (HPC) systems requires a workflow that records which primers each sample carried, groups samples sensibly for error modelling, and runs without a separate R installation. Results: illumina_amplicon is one of six Nextflow DSL2 pipelines in the dānaSeq suite. It takes paired-end Illumina FASTQ files from a directory or a samplesheet, detects primers from the reads against a curated catalogue, splits accessions that contain more than one assay, removes primers with cutadapt, and denoises reads into amplicon sequence variants (ASVs) with papa2, the author's Python port of DADA2, using error models grouped by sequencing run and assay. Optional steps assign taxonomy against one or more reference databases, build a MAFFT/FastTree phylogeny, renormalize counts within taxonomic groups, and compute Bray–Curtis t-SNE ordinations and a CLR-correlation network. Outputs are the ASV and taxonomy tables, per-sample primer and read-count reports, a run manifest recording versions and parameters, and JSON data for a Svelte single-page ASV explorer with six views. The pipeline runs from a conda environment or from a container image published to the GitHub Container Registry with a matching Apptainer SIF, on a workstation or inside a Slurm allocation. Availability and implementation: dānaSeq is released under the MIT license at https://github.com/rec3141/danaSeq (this description corresponds to release v1.3.0, https://github.com/rec3141/danaSeq/releases/tag/v1.3.0); documentation at https://rec3141.github.io/danaSeq/.