Package Recipe 'peptdeep'

recipe peptdeep

The AlphaX deep learning framework for Proteomics

Homepage:: https://github.com/MannLabs/alphapeptdeep
Documentation:: https://alphapeptdeep.readthedocs.io/en/latest/
License:: APACHE / Apache-2.0
Recipe:: /peptdeep/meta.yaml

PeptDeep provides deep learning models for mass spectrometry-based proteomics. It includes built-in models for predicting retention time, collision cross section, and tandem mass spectra for peptides, enabling users to generate predicted spectral libraries from protein sequences.

package peptdeep¶

Versions:

1.4.2-0, 1.4.1-1, 1.4.1-0

Depends:

on alphabase >=1.5.0
on alpharaw >=0.2.0
on click
on lxml
on numba
on numpy <2
on pandas <3.0
on psutil
on pyteomics
on python >=3.8
on pytorch
on scikit-learn
on tqdm
on transformers

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 peptdeep

to add into an existing workspace instead, run:

pixi add peptdeep

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 peptdeep

Alternatively, to install into a new environment, run:

conda create -n envname peptdeep

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/peptdeep:<tag>

(see peptdeep/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/peptdeep/README.html)