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

Last updated: May 20,2024

1. Install "kmc" package

Learn how to install kmc on Ubuntu 20.10 (Groovy Gorilla)


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

This guide let you learn how to uninstall kmc on Ubuntu 20.10 (Groovy Gorilla):


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

Package: kmc
Architecture: amd64
Version: 2.3+dfsg-8
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: 2349
Depends: libbz2-1.0, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 6), zlib1g (>= 1:1.2.6)
Filename: pool/universe/k/kmc/kmc_2.3+dfsg-8_amd64.deb
Size: 513252
MD5sum: aec292a6b62d0fce15967ff8dc174745
SHA1: ed9eb58afb11eec77fde2c8bf183a4c45c4f9e00
SHA256: 60bce460127f5101e41156e4b06c682ce920a92a928521b175d702cf6e2f8755
SHA512: 42a963ec6ce30d89d64eaab6f7dc6aa98d9f55fde257cc0a8d8e4890c17b3e94c88e09c455f4ed9b98537d37b015e87b23d743acc98279dd8c87bd6c38a01e63
Homepage: http://sun.aei.polsl.pl/kmc
Description-en: count kmers in genomic sequences
The kmc software is designed for counting k-mers (sequences of
consecutive k symbols) in a set of reads. K-mer counting is
important for many bioinformatics applications, e.g. developing de Bruijn
graph assemblers.
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Building de Bruijn graphs is a commonly used approach for genome
assembly with data from second-generation sequencing.
Unfortunately, sequencing errors (frequent in practice)
result in huge memory requirements for de Bruijn graphs, as well
as long build time. One of the popular approaches to handle this
problem is filtering the input reads in such a way that unique k-mers
(very likely obtained as a result of an error) are discarded.
.
Thus, KMC scans the raw reads and produces a compact representation
of all non-unique reads accompanied with number of their occurrences.
The algorithm implemented in KMC makes use mostly of disk space rather
than RAM, which allows one to use KMC even on rather typical personal
computers. When run on high-end servers (what is necessary for KMC
competitors) it outperforms them in both memory requirements and
speed of computation. The disk space necessary for computation is in
order of the size of input data (usually it is smaller).
Description-md5: ebf89f936ea92086de01b356a765be39