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

Last updated: December 23,2024

1. Install "fitgcp" package

This guide let you learn how to install fitgcp on Ubuntu 20.10 (Groovy Gorilla)


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

Learn how to uninstall fitgcp on Ubuntu 20.10 (Groovy Gorilla):


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

Package: fitgcp
Architecture: all
Version: 0.0.20150429-4
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: 23164
Depends: python3:any, python3-scipy, python3-numpy, python3-pysam
Filename: pool/universe/f/fitgcp/fitgcp_0.0.20150429-4_all.deb
Size: 3676100
MD5sum: 1ed10c6bc35be72f4dc306625d6411a7
SHA1: 14a380feec025339985eb2278a2305cd28721c13
SHA256: 84db1dfa5f67d30932fcd301132ca64bb6077f6b0fb34ec1ea6e797fd494cdb5
SHA512: ec6900e1397d07a58d6af67cc44aef6a629b4aba6a62cc415f62f5d0b7d0d5ea997b6d3ae148a0c9a475919060f7ac5870f0c4909b296ca9fc99cd791e654409
Homepage: http://sourceforge.net/projects/fitgcp/
Description-en: fitting genome coverage distributions with mixture models
Genome coverage, the number of sequencing reads mapped to a position in
a genome, is an insightful indicator of irregularities within sequencing
experiments. While the average genome coverage is frequently used within
algorithms in computational genomics, the complete information available
in coverage profiles (i.e. histograms over all coverages) is currently
not exploited to its full extent. Thus, biases such as fragmented or
erroneous reference genomes often remain unaccounted for. Making this
information accessible can improve the quality of sequencing experiments
and quantitative analyses.
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fitGCP is a framework for fitting mixtures of probability distributions
to genome coverage profiles. Besides commonly used distributions, fitGCP
uses distributions tailored to account for common artifacts. The mixture
models are iteratively fitted based on the Expectation-Maximization
algorithm.
Description-md5: 6d250046a14f2fb020b753ee88032582