How to Install and Uninstall r-cran-shazam Package on Kali Linux

Last updated: May 20,2024

1. Install "r-cran-shazam" package

Learn how to install r-cran-shazam on Kali Linux


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                r-cran-shazam
            
                    
    
    Copied

2. Uninstall "r-cran-shazam" package

In this section, we are going to explain the necessary steps to uninstall r-cran-shazam on Kali Linux:


    
        
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                sudo apt remove
            
                        
                r-cran-shazam
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the r-cran-shazam package on Kali Linux

Package: r-cran-shazam
Version: 1.2.0-1
Installed-Size: 2675
Maintainer: Debian R Packages Maintainers
Architecture: all
Depends: r-api-4.0, r-cran-ggplot2 (>= 3.4.0), r-cran-alakazam (>= 1.3.0), r-cran-ape, r-cran-diptest, r-cran-doparallel, r-cran-dplyr (>= 1.0), r-cran-foreach, r-cran-igraph (>= 1.5.0), r-cran-iterators, r-cran-kernsmooth, r-cran-lazyeval, r-cran-mass, r-cran-progress, r-cran-rlang, r-cran-scales, r-cran-seqinr, r-cran-stringi (>= 1.1.3), r-cran-tidyr, r-cran-tidyselect
Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat
Size: 2194220
SHA256: 0754aaadd0c84eede5b697869bce8dc16b520e219fcbc3fcc3bc86d40f226823
SHA1: b7af302d00f1183a652c73f142674ea0900e8e75
MD5sum: f2d576119efcccdad4b27bc68186741d
Description: Immunoglobulin Somatic Hypermutation Analysis
Provides a computational framework for Bayesian estimation of
antigen-driven selection in immunoglobulin (Ig) sequences, providing an
intuitive means of analyzing selection by quantifying the degree of
selective pressure. Also provides tools to profile mutations in Ig
sequences, build models of somatic hypermutation (SHM) in Ig sequences,
and make model-dependent distance comparisons of Ig repertoires.
.
SHazaM is part of the Immcantation analysis framework for Adaptive
Immune Receptor Repertoire sequencing (AIRR-seq) and provides tools for
advanced analysis of somatic hypermutation (SHM) in immunoglobulin (Ig)
sequences. Shazam focuses on the following analysis topics:
.
* Quantification of mutational load
SHazaM includes methods for determine the rate of observed and
expected mutations under various criteria. Mutational profiling
criteria include rates under SHM targeting models, mutations specific
to CDR and FWR regions, and physicochemical property dependent
substitution rates.
* Statistical models of SHM targeting patterns
Models of SHM may be divided into two independent components:
1) a mutability model that defines where mutations occur and
2) a nucleotide substitution model that defines the resulting mutation.
Collectively these two components define an SHM targeting
model. SHazaM provides empirically derived SHM 5-mer context mutation
models for both humans and mice, as well tools to build SHM targeting
models from data.
* Analysis of selection pressure using BASELINe
The Bayesian Estimation of Antigen-driven Selection in Ig Sequences
(BASELINe) method is a novel method for quantifying antigen-driven
selection in high-throughput Ig sequence data. BASELINe uses SHM
targeting models can be used to estimate the null distribution of
expected mutation frequencies, and provide measures of selection
pressure informed by known AID targeting biases.
* Model-dependent distance calculations
SHazaM provides methods to compute evolutionary distances between
sequences or set of sequences based on SHM targeting models. This
information is particularly useful in understanding and defining
clonal relationships.
Description-md5:
Homepage: https://cran.r-project.org/package=shazam
Section: science
Priority: optional
Filename: pool/main/r/r-cran-shazam/r-cran-shazam_1.2.0-1_all.deb