How to Install and Uninstall python-freecontact Package on Ubuntu 16.04 LTS (Xenial Xerus)

Last updated: May 19,2024

1. Install "python-freecontact" package

Learn how to install python-freecontact on Ubuntu 16.04 LTS (Xenial Xerus)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                python-freecontact
            
                    
    
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2. Uninstall "python-freecontact" package

This is a short guide on how to uninstall python-freecontact on Ubuntu 16.04 LTS (Xenial Xerus):


    
        
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                sudo apt remove
            
                        
                python-freecontact
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the python-freecontact package on Ubuntu 16.04 LTS (Xenial Xerus)

Package: python-freecontact
Priority: extra
Section: universe/python
Installed-Size: 165
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Med Packaging Team
Architecture: amd64
Version: 1.1-2
Depends: python (<< 2.8), python (>= 2.7~), libboost-python1.58.0, libc6 (>= 2.14), libfreecontact0v5, libgcc1 (>= 1:4.1.1), libstdc++6 (>= 5.2)
Filename: pool/universe/p/python-freecontact/python-freecontact_1.1-2_amd64.deb
Size: 81668
MD5sum: 5ccfacb095b034cc099bfcedce0500ca
SHA1: ef787666f731aa52581318a4c357eab70a90225c
SHA256: da017dbfdec29d89151bdca46e980010f4fd50c42e1fc8c3096801190c1e11c7
Description-en: fast protein contact predictor - binding for Python
FreeContact is a protein residue contact predictor optimized for speed.
Its input is a multiple sequence alignment. FreeContact can function as an
accelerated drop-in for the published contact predictors
EVfold-mfDCA of DS. Marks (2011) and
PSICOV of D. Jones (2011).
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FreeContact is accelerated by a combination of vector instructions, multiple
threads, and faster implementation of key parts.
Depending on the alignment, 8-fold or higher speedups are possible.
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A sufficiently large alignment is required for meaningful results.
As a minimum, an alignment with an effective (after-weighting) sequence count
bigger than the length of the query sequence should be used. Alignments with
tens of thousands of (effective) sequences are considered good input.
.
jackhmmer(1) from the hmmer package, or hhblits(1) from hhsuite
can be used to generate the alignments, for example.
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This package contains the Python binding.
Description-md5: efc9e3135a1169e28d02aea95102dd67
Homepage: http://rostlab.org/
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Origin: Ubuntu