Package Recipe 'papa2'

recipe papa2

Python-first amplicon denoising — byte-identical to R's DADA2

Homepage:: https://github.com/rec3141/papa2
Documentation:: https://rec3141.github.io/papa2
License:: LGPL-3.0-only
Recipe:: /papa2/meta.yaml
Links:: doi: 10.1038/nmeth.3869

papa2 is a complete Python port of the DADA2 amplicon denoising pipeline. All 37 R functions have Python equivalents, producing byte-identical results with no R dependency. Includes quality filtering, dereplication, error learning, denoising, paired-end merging, chimera removal, and taxonomy assignment backed by a compiled C/C++ core.

package papa2¶

Versions:

0.1.0-0

Depends:

on libcxx >=19
on libzlib >=1.3.2,<2.0a0
on numpy >=1.21,<3
on pandas
on python >=3.10,<3.11.0a0
on python_abi 3.10.* *_cp310

Additional platforms:

linux-aarch64, osx-arm64

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install papa2

to add into an existing workspace instead, run:

pixi add papa2

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install papa2

Alternatively, to install into a new environment, run:

conda create -n envname papa2

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

docker pull quay.io/biocontainers/papa2:<tag>

(see papa2/tags for valid values for <tag>).

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/papa2/README.html)