recipe dipcall

Dipcall is a reference-based variant calling pipeline for a pair of phased haplotype assemblies.

Homepage:

https://github.com/lh3/dipcall

License:

MIT

Recipe:

/dipcall/meta.yaml

Links:

doi: 10.1038/s41592-018-0054-7

Dipcall is a reference-based variant calling pipeline for a pair of phased haplotype assemblies. It was originally developed for constructing the syndip benchmark dataset and has been applied to other phased assemblies, too. Dipcall can call small variants and long INDELs as long as they are contained in minimap2 alignment.

package dipcall

(downloads) docker_dipcall

Versions:

0.3-00.2-20.2-10.2-0

Depends:
  • on bedtk

  • on htsbox

  • on k8

  • on make

  • on minimap2

  • on perl *

  • on samtools

  • on unimap

  • on winnowmap

Additional platforms:

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 dipcall

to add into an existing workspace instead, run:

pixi add dipcall

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 dipcall

Alternatively, to install into a new environment, run:

conda create -n envname dipcall

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/dipcall:<tag>

(see dipcall/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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