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

Last updated: May 17,2024

1. Install "garli" package

Please follow the step by step instructions below to install garli on Ubuntu 20.10 (Groovy Gorilla)


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

This is a short guide on how to uninstall garli on Ubuntu 20.10 (Groovy Gorilla):


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

Package: garli
Architecture: amd64
Version: 2.1-3build1
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: 1565
Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libncl2 (>= 2.1.18), libstdc++6 (>= 5.2)
Filename: pool/universe/g/garli/garli_2.1-3build1_amd64.deb
Size: 638328
MD5sum: 8657550fbc63b579f311d5fd3c7f5f3e
SHA1: f2cbb9d91c3496535b67449bf09e64c278eb2772
SHA256: 1649e72b331d43b98bfebc64e03fef8809287c5b3df6ce54c2552fb948c201ee
SHA512: f49189bfcc518253eebca9b55872ba5c9cc34bb4d8f774d1f9e9a5bbeed919ff53733ac8709c5463e3c694c0d0bb2fca121e16177a99f472988dd2fe7525e543
Homepage: https://github.com/Ashod/garli
Description-en: phylogenetic analysis of molecular sequence data using maximum-likelihood
GARLI, Genetic Algorithm for Rapid Likelihood Inference is a program for
inferring phylogenetic trees. Using an approach similar to a classical
genetic algorithm, it rapidly searches the space of evolutionary trees
and model parameters to find the solution maximizing the likelihood
score. It implements nucleotide, amino acid and codon-based models of
sequence evolution, and runs on all platforms. The latest version adds
support for partitioned models and morphology-like datatypes.
Description-md5: 26bd1f70ecc029da7a39ce42be819b22