Read Protein Data Bank (PDB) files, performs its analysis, and 
    presents the result using different visualization types including 3D. The 
    package also has additional capability for handling Virus Report data from 
    the National Center for Biotechnology Information (NCBI) database.
    Nature Structural Biology 10, 980 (2003) <doi:10.1038/nsb1203-980>.
    US National Library of Medicine (2021) <https://www.ncbi.nlm.nih.gov/datasets/docs/reference-docs/data-reports/virus/>.

Analysis of protein expression data can be done through Principal Component Analysis (PCA), and this R package is designed to streamline the analysis. This package enables users to perform PCA and it generates biplot and scree plot for advanced graphical visualization. Optionally, it supports grouping/clustering visualization with PCA loadings and confidence ellipses. With this R package, researchers can quickly explore complex protein datasets, interpret variance contributions, and visualize sample clustering through intuitive biplots. For more details, see Jolliffe (2001) <doi:10.1007/b98835>, Gabriel (1971) <doi:10.1093/biomet/58.3.453>, Zhang et al. (2024) <doi:10.1038/s41467-024-53239-9>, and Anandan et al. (2022) <doi:10.1038/s41598-022-07781-5>.

Bayesian toolbox for quantitative proteomics. In particular, this 
    package provides functions to generate synthetic datasets, execute Bayesian
    differential analysis methods, and display results as, described in the 
    associated article Marie Chion and Arthur Leroy (2023) <arXiv:2307.08975>.

Calculates the percentage coefficient of variation (CV) for mass spectrometry-based proteomic data. The CV can be calculated with the traditional formula for raw (non log transformed) intensity data, or log transformed data.

Seq2seq time-feature analysis based on variational model, with a wide range of distributions available for the latent variable.

Calculates a comprehensive list of features from profile hidden Markov models (HMMs) of proteins. 
    Adapts and ports features for use with HMMs instead of Position Specific Scoring Matrices, in order to take 
    advantage of more accurate multiple sequence alignment by programs such as 'HHBlits' Remmert et al. (2012) 
    <DOI:10.1038/nmeth.1818> and 'HMMer' Eddy (2011) <DOI:10.1371/journal.pcbi.1002195>. Features calculated by 
    this package can be used for protein fold classification, protein structural class prediction, 
    sub-cellular localization and protein-protein interaction, among other tasks. Some examples of features 
    extracted are found in Song et al. (2018) <DOI:10.3390/app8010089>, Jin & Zhu (2021) <DOI:10.1155/2021/8629776>, 
    Lyons et al. (2015) <DOI:10.1109/tnb.2015.2457906> and Saini et al. (2015) <DOI:10.1016/j.jtbi.2015.05.030>.