How to Install and Uninstall deepnano-data Package on Ubuntu 21.10 (Impish Indri)

Last updated: December 27,2024

1. Install "deepnano-data" package

Please follow the guidelines below to install deepnano-data on Ubuntu 21.10 (Impish Indri)


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                deepnano-data
            
                    
    
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2. Uninstall "deepnano-data" package

Please follow the steps below to uninstall deepnano-data on Ubuntu 21.10 (Impish Indri):


    
        
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                sudo apt remove
            
                        
                deepnano-data
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the deepnano-data package on Ubuntu 21.10 (Impish Indri)

Package: deepnano-data
Architecture: all
Version: 0.0+git20170813.e8a621e-3.1build1
Priority: optional
Section: universe/science
Source: deepnano
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Med Packaging Team
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 91300
Suggests: deepnano
Filename: pool/universe/d/deepnano/deepnano-data_0.0+git20170813.e8a621e-3.1build1_all.deb
Size: 86093816
MD5sum: 08d0c8510d794b392146b2d2b7e37e93
SHA1: 1919289d3fbddf50f93510a72a5054523acb6fae
SHA256: 5c0cbab467e816695e42e323d1df58f69a570549d03ee0b14c04249d98205e20
SHA512: 4ab686363c55b2fb88168ec766358155b4f2168632cd372f46f2cfb9622763d4fefd0f257c61fe7e8ffe5815877cbf8dec9e4756ba184b060ec1666fad9d4db8
Homepage: https://bitbucket.org/vboza/deepnano
Description-en: alternative basecaller for MinION reads of genomic sequences (data)
DeepNano is alternative basecaller for Oxford Nanopore MinION reads
based on deep recurrent neural networks.
.
This package contains deepnanos test data.
Description-md5: 2e36bafe46522b0886f010d4f9ed7d56