How to Install and Uninstall r-bioc-grohmm Package on Kali Linux

Last updated: September 20,2024

1. Install "r-bioc-grohmm" package

Please follow the step by step instructions below to install r-bioc-grohmm on Kali Linux


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                r-bioc-grohmm
            
                    
    
    Copied

2. Uninstall "r-bioc-grohmm" package

Learn how to uninstall r-bioc-grohmm on Kali Linux:


    
        
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                sudo apt remove
            
                        
                r-bioc-grohmm
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the r-bioc-grohmm package on Kali Linux

Package: r-bioc-grohmm
Version: 1.36.0-1
Installed-Size: 4515
Maintainer: Debian R Packages Maintainers
Architecture: amd64
Depends: r-api-4.0, r-api-bioc-3.18, r-cran-mass, r-bioc-s4vectors (>= 0.17.25), r-bioc-iranges (>= 2.13.12), r-bioc-genomeinfodb, r-bioc-genomicranges (>= 1.31.8), r-bioc-genomicalignments (>= 1.15.6), r-bioc-rtracklayer (>= 1.39.7), libc6 (>= 2.4)
Suggests: r-bioc-biocstyle, r-bioc-genomicfeatures, r-bioc-edger, r-bioc-org.hs.eg.db
Size: 4447120
SHA256: 750c6a83b44d5d1fd6eb7d79ec593e909bc8f3d5bc53b82e259c1312c1a4b1a1
SHA1: 8314b529129d8c1581847e0855619889024d19bb
MD5sum: 6ce7688847aa71c12496cfb2dd0eaf30
Description: GRO-seq Analysis Pipeline
This BioConductor package provides a pipeline for the analysis of GRO-
seq data. Among the more advanced features, r-bioc-grohmm predicts the
boundaries of transcriptional activity across the genome de novo using a
two-state hidden Markov model (HMM).
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The used model essentially divides the genome into transcribed and non-
transcribed regions in a strand specific manner. HMMs are used to
identify the leading edge of Pol II at genes activated by a stimulus in
GRO-seq time course data. This approach allows the genome-wide
interrogation of transcription rates in cells.
Description-md5:
Homepage: https://bioconductor.org/packages/groHMM/
Section: gnu-r
Priority: optional
Filename: pool/main/r/r-bioc-grohmm/r-bioc-grohmm_1.36.0-1_amd64.deb