How to Install and Uninstall r-cran-shazam Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: December 23,2024

1. Install "r-cran-shazam" package

Here is a brief guide to show you how to install r-cran-shazam on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                r-cran-shazam
            
                    
    
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2. Uninstall "r-cran-shazam" package

Please follow the instructions below to uninstall r-cran-shazam on Ubuntu 20.10 (Groovy Gorilla):


    
        
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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 Ubuntu 20.10 (Groovy Gorilla)

Package: r-cran-shazam
Architecture: all
Version: 1.0.2-1
Priority: optional
Section: universe/gnu-r
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 2565
Depends: r-base-core (>= 4.0.2-1build1), r-api-4.0, r-cran-ggplot2 (>= 3.2.0), r-cran-alakazam (>= 1.0.2), r-cran-ape, r-cran-diptest, r-cran-doparallel, r-cran-dplyr (>= 0.8.1), r-cran-foreach, r-cran-igraph, r-cran-iterators, r-cran-kedd, 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
Filename: pool/universe/r/r-cran-shazam/r-cran-shazam_1.0.2-1_all.deb
Size: 2143076
MD5sum: 260e6e2eb2fffac0758bda7cbf88e756
SHA1: 9551b15c157e2d8e130117c2719afc5918cda666
SHA256: 318fa998927ca96981342902ad6aa0ba63a8eecdb984f96b60637288005257b8
SHA512: 9285db47397e88917c2b8c4537259788463e1a044619cc280a97a5864d4b01a7524e3da77e2f1c59673fb33b8d7d5f4fb2277c1de32b8d5ac9c37e6990099174
Homepage: https://cran.r-project.org/package=shazam
Description-en: 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: 34fd8fba9274dcd56ee399bba24b4182