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DESCRIPTION
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DESCRIPTION
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Package: PepMapViz
Title: A Versatile Toolkit for Peptide Mapping, Visualization, and
Comparative Exploration
Version: 1.0.0
Authors@R: c(
person(given = "Zhenru", family = "Zhou", email = "[email protected]", role = c("aut", "cre")),
person(given = "Qui", family = "Phung", email = "[email protected]", role = c("aut")),
person(given = "Corey", family = "Bakalarski", email = "[email protected]", role = c("aut")))
Description: A versatile R visualization package that empowers
researchers with comprehensive visualization tools for seamlessly mapping peptides
to protein sequences, identifying distinct domains and regions of interest,
accentuating mutations, and highlighting post-translational modifications,
all while enabling comparisons across diverse experimental conditions.
Potential applications of PepMapViz include the visualization of cross-software
mass spectrometry results at the peptide level for specific protein and domain
details in a linearized format and post-translational modification coverage
across different experimental conditions; unraveling insights into disease
mechanisms. It also enables visualization of MHC-presented peptide clusters in
different antibody regions predicting immunogenicity in antibody drug development.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.1
Imports: ggplot2, stringr, ggforce, ggh4x, ggnewscale, data.table,
rlang
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
Config/testthat/edition: 3
VignetteBuilder: knitr
biocViews: Immunogenicity, MassSpectrometry, Proteomics, Peptidomics,
Software, Visualization
NeedsCompilation: no
Packaged: 2024-08-22 17:22:35 UTC; zhouz45
Author: Zhenru Zhou [aut, cre],
Qui Phung [aut],
Corey Bakalarski [aut]
Maintainer: Zhenru Zhou <[email protected]>
Note: The following words are correctly spelled domain-specific terms:
MHC, PepMapViz, immunogenicity, linearized, spectrometry, translational. The package includes large datasets in the 'extdata' directory, which are essential for demonstrating the functionality and performance of the tools provided. These datasets are necessary for reproducibility and comprehensive testing.