GenProSeq

DOI: 10.18129/B9.bioc.GenProSeq  

Generating Protein Sequences with Deep Generative Models

Bioconductor version: Release (3.16)

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.

Author: Dongmin Jung [cre, aut]

Maintainer: Dongmin Jung <dmdmjung at gmail.com>

Installation

To install this package, start R (version "4.2") and enter:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("GenProSeq")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("GenProSeq")

 

HTML	R Script	GenProSeq
PDF		Reference Manual
Text		NEWS

Details

biocViews	Proteomics, Software
Version	1.2.0
In Bioconductor since	BioC 3.15 (R-4.2) (1 year)
License	Artistic-2.0
Depends	keras, mclust, R (>= 4.2)
Imports	tensorflow, word2vec, DeepPINCS, ttgsea, CatEncoders, reticulate, stats
LinkingTo
Suggests	knitr, testthat, rmarkdown
SystemRequirements
Enhances
URL
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package	GenProSeq_1.2.0.tar.gz
Windows Binary	GenProSeq_1.2.0.zip
macOS Binary (x86_64)	GenProSeq_1.2.0.tgz
macOS Binary (arm64)	GenProSeq_1.2.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/GenProSeq
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/GenProSeq
Bioc Package Browser	https://code.bioconductor.org/browse/GenProSeq/
Package Short Url	https://bioconductor.org/packages/GenProSeq/
Package Downloads Report	Download Stats