recipe fastppm

Fast Perfect Phylogeny Mixture Regression using Tree-Structured Dual Dynamic Programming

Homepage:

https://github.com/elkebir-group/fastppm

License:

MIT

Recipe:

/fastppm/meta.yaml

`fastppm` (fast perfect phylogeny mixtures) is a C++/Python library for fast estimation of unknown frequency matrices, given variant and total read counts over an n-clonal tree.

package fastppm

(downloads) docker_fastppm

Versions:

1.1.1-0,  1.1.0-0

Depends:

  • on libgcc >=13

  • on libstdcxx >=13

  • 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 fastppm

to add into an existing workspace instead, run:

pixi add fastppm

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 fastppm

Alternatively, to install into a new environment, run:

conda create -n envname fastppm

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/fastppm:<tag>

(see fastppm/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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