How to Install and Uninstall surankco Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: November 22,2024

1. Install "surankco" package

Please follow the guidelines below to install surankco on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                surankco
            
                    
    
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2. Uninstall "surankco" package

Here is a brief guide to show you how to uninstall surankco on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                surankco
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the surankco package on Ubuntu 20.10 (Groovy Gorilla)

Package: surankco
Architecture: all
Version: 0.0.r5+dfsg-2
Priority: optional
Section: universe/science
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Med Packaging Team
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 157
Depends: libhtsjdk-java, r-base-core, r-cran-mass, r-cran-optparse, r-cran-randomforest
Filename: pool/universe/s/surankco/surankco_0.0.r5+dfsg-2_all.deb
Size: 76688
MD5sum: 31c8545766a482d45ee85e7bb786951c
SHA1: 46a633b02aed3bb0ffca7b3117b474f0159bf4ff
SHA256: d950df909c221494fde57a1675e9ba6834121517de20114af48fe1fc480103c4
SHA512: 160668dedc64d440b4db1f323da1a2709f3752696e1bd24fefe0bdea8056322a798f61b914c9624bc55d529d43ce017274792a41959ba65045c6b33c9080cd10
Homepage: https://sourceforge.net/projects/surankco/
Description-en: Supervised Ranking of Contigs in de novo Assemblies
SuRankCo is a machine learning based software to score and rank
contigs from de novo assemblies of next generation sequencing data. It
trains with alignments of contigs with known reference genomes and
predicts scores and ranking for contigs which have no related
reference genome yet.
Description-md5: 713e9eb3de12c80c60bcafd63bb396f3