Package Recipe 'bioconductor-genproseq'

recipe bioconductor-genproseq

Generating Protein Sequences with Deep Generative Models

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/GenProSeq.html
License:: Artistic-2.0
Recipe:: /bioconductor-genproseq/meta.yaml

Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. Machine learning has enabled us to generate useful protein sequences on a variety of scales. Generative models are machine learning methods which seek to model the distribution underlying the data, allowing for the generation of novel samples with similar properties to those on which the model was trained. Generative models of proteins can learn biologically meaningful representations helpful for a variety of downstream tasks. Furthermore, they can learn to generate protein sequences that have not been observed before and to assign higher probability to protein sequences that satisfy desired criteria. In this package, common deep generative models for protein sequences, such as variational autoencoder (VAE), generative adversarial networks (GAN), and autoregressive models are available. In the VAE and GAN, the Word2vec is used for embedding. The transformer encoder is applied to protein sequences for the autoregressive model.

package bioconductor-genproseq¶

Versions:

1.14.0-0, 1.10.0-0, 1.6.0-0, 1.4.2-0, 1.2.0-0

Depends:

on bioconductor-deeppincs >=1.18.0,<1.19.0
on bioconductor-ttgsea >=1.18.0,<1.19.0
on r-base >=4.5,<4.6.0a0
on r-catencoders
on r-keras
on r-mclust
on r-reticulate
on r-tensorflow
on r-word2vec

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 bioconductor-genproseq

to add into an existing workspace instead, run:

pixi add bioconductor-genproseq

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 bioconductor-genproseq

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-genproseq

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/bioconductor-genproseq:<tag>

(see bioconductor-genproseq/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/bioconductor-genproseq/README.html)