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

Last updated: September 20,2024

1. Install "emmax" package

This guide covers the steps necessary to install emmax on Ubuntu 20.10 (Groovy Gorilla)


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

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


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

Package: emmax
Architecture: amd64
Version: 0~beta.20100307-1
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: 89
Depends: libatlas3-base, libc6 (>= 2.29), liblapack3 | liblapack.so.3, zlib1g (>= 1:1.1.4)
Filename: pool/universe/e/emmax/emmax_0~beta.20100307-1_amd64.deb
Size: 27100
MD5sum: ca222e58375f479a345db9ddd1b918c6
SHA1: 7ffc2dd5187301c38aced71020afec5c73b07069
SHA256: b0991142016068db53d87d8297d93f2a8307553226be217e052e0caf02020c11
SHA512: ce9c4b3db89a9f864994048da5f2421579e85e815d93da9115bda6ed2c26a89598d4742789a6b3d2623f5626bf8fda486398599e9a27dcd753dfa03348c9a3b1
Homepage: http://genome.sph.umich.edu/wiki/EMMAX
Description-en: genetic mapping considering population structure
EMMAX is a statistical test for large scale human or model organism
association mapping accounting for the sample structure. In addition
to the computational efficiency obtained by EMMA algorithm, EMMAX takes
advantage of the fact that each locus explains only a small fraction of
complex traits, which allows one to avoid repetitive variance component
estimation procedure, resulting in a significant amount of increase in
computational time of association mapping using mixed model.
Description-md5: fa08a2d54a24822f6f716959bc91f62c