recipe unimap

Unimap is a fork of minimap2 optimized for assembly-to-reference alignment.

Homepage:

https://github.com/lh3/unimap

License:

MIT / MIT

Recipe:

/unimap/meta.yaml

Unimap is a fork of minimap2 optimized for assembly-to-reference alignment. It integrates the minigraph chaining algorithm and can align through long INDELs (up to 100kb by default) much faster than minimap2. Unimap is a better fit for resolving segmental duplications and is recommended over minimap2 for alignment between high-quality assemblies.

Unimap does not replace minimap2 for other types of alignment. It drops the support of multi-part index and short-read mapping. Its long-read alignment is different from minimap2 but is not necessarily better. Unimap is more of a specialized minimap2 at the moment.

package unimap

(downloads) docker_unimap

Versions:

0.1-7,  0.1-6,  0.1-5,  0.1-4,  0.1-3,  0.1-2,  0.1-1,  0.1-0

Depends:

  • on libgcc >=13

  • on libzlib >=1.3.1,<2.0a0

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 unimap

to add into an existing workspace instead, run:

pixi add unimap

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 unimap

Alternatively, to install into a new environment, run:

conda create -n envname unimap

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/unimap:<tag>

(see unimap/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.

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