peptoolkit
A Toolkit for Using Peptide Sequences in Machine Learning and Accelerate Virtual Screening
Science Score: 13.0%
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Low similarity (12.7%) to scientific vocabulary
Repository
A Toolkit for Using Peptide Sequences in Machine Learning and Accelerate Virtual Screening
Basic Info
- Host: GitHub
- Owner: jrcodina
- License: gpl-3.0
- Language: R
- Default Branch: master
- Size: 59.6 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
PepToolkit
The peptoolkit R package is designed for the manipulation and analysis of peptides sequences. It provides functionalities to assist researchers in peptide engineering and proteomics. Users can manipulate peptides by adding amino acids at every position, count occurrences of each amino acid at each position, and transform amino acid counts based on probabilities. The package offers functionalities to select the best versus the worst peptides and analyze these peptides, which includes counting specific residues, reducing peptide sequences, extracting features through One Hot Encoding (OHE), and utilizing Quantitative Structure-Activity Relationship (QSAR) properties (based in the package 'Peptides' by Osorio et al. (2015) doi:10.32614/RJ-2015-001). This package is intended for both researchers and bioinformatics enthusiasts working on peptide-based projects, especially for their use with machine learning.
Installation
You can install the released version of peptoolkit (0.0.1) from CRAN with:
r
install.packages("peptoolkit")
You can also install the development version (0.0.2) from GitHub with:
```r
install.packages("devtools") # Uncomment and run if you don't have the devtools package yet
devtools::install_github("jrcodina/peptoolkit") ```
Example
This is a basic example which shows you how to use the main function:
```r
Default usage
result <- peptoolkit::extractfeaturesQSAR(n = 3)
Providing a custom peptide list
result <- peptoolkit::extractfeaturesQSAR(n = 3, custom.list = TRUE, PeList = c('ACA', 'ADE')) ```
Please refer to function documentation for more details on parameters and their usage.
Citation
If you use peptoolkit in your research, please cite:
Codina J (2023). peptoolkit: A Toolkit for Using Peptide Sequences in Machine Learning and Accelerate Virtual Screening. R package version 0.0.1.
A BibTeX entry for LaTeX users is
@Manual{,
title = {peptoolkit: A Toolkit for Using Peptide Sequences in Machine Learning and Accelerate Virtual Screening},
author = {Josep-Ramon Codina},
year = {2023},
note = {R package version 0.0.1},
}
Owner
- Name: Josep Ramón Codina García-Andrade
- Login: jrcodina
- Kind: user
- Location: Miami, Florida, USA
- Company: Miller School of Medicine, University of Miami
- Website: https://www.linkedin.com/in/jrcodina/
- Repositories: 1
- Profile: https://github.com/jrcodina
Ph.D student BIochemistry and Molecular Biology. Biotechnology major and Masters of Pharmacy, learning Data Analysis.
GitHub Events
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Packages
- Total packages: 1
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Total downloads:
- cran 185 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 1
- Total maintainers: 1
cran.r-project.org: peptoolkit
A Toolkit for Using Peptide Sequences in Machine Learning
- Homepage: https://github.com/jrcodina/peptoolkit
- Documentation: http://cran.r-project.org/web/packages/peptoolkit/peptoolkit.pdf
- License: GPL (≥ 3)
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Latest release: 0.0.1
published almost 3 years ago
Rankings
Maintainers (1)
Dependencies
- R >= 4.3.0 depends
- caret * depends
- Peptides * imports
- dplyr * imports
- stats * imports
- stringr * imports