recipe bioconductor-dada2

Accurate, high-resolution sample inference from amplicon sequencing data







biotools: dada2

The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, and outputs the sequence variants and their sample-wise abundances after removing substitution and chimera errors. Taxonomic classification is available via a native implementation of the RDP naive Bayesian classifier, and species-level assignment to 16S rRNA gene fragments by exact matching.

package bioconductor-dada2

(downloads) docker_bioconductor-dada2



depends bioconductor-biocgenerics:


depends bioconductor-biostrings:


depends bioconductor-iranges:


depends bioconductor-shortread:


depends bioconductor-xvector:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends libstdcxx-ng:


depends r-base:


depends r-ggplot2:


depends r-rcpp:


depends r-rcppparallel:


depends r-reshape2:




You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-dada2

and update with::

   mamba update bioconductor-dada2

To create a new environment, run:

mamba create --name myenvname bioconductor-dada2

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-dada2/tags`_ for valid values for ``<tag>``)

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