How to Install and Uninstall chip-seq Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: November 05,2024

1. Install "chip-seq" package

This tutorial shows how to install chip-seq on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                chip-seq
            
                    
    
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2. Uninstall "chip-seq" package

Please follow the guidelines below to uninstall chip-seq on Ubuntu 20.10 (Groovy Gorilla):


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

Package: chip-seq
Architecture: amd64
Version: 1.5.5-3
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: 511
Depends: libc6 (>= 2.14), chip-seq-data, libmath-round-perl
Filename: pool/universe/c/chip-seq/chip-seq_1.5.5-3_amd64.deb
Size: 103708
MD5sum: b40137687798960e7cbc9ddf1d1014d2
SHA1: 62f38a2a2829f7e15b3cf665e3ec6aa8a47ee725
SHA256: 80e191fd97f3c77dd245145eeab58a01299402bf909525291be13c9b834e4e3d
SHA512: e74380ba3e16581e732b8168a57e71b431899795f364f992599fcbc3245ae1704c3f37f2103bda84a35db10af892ea229ec843a4654d09e600f85b7b9ee1b76d
Homepage: https://ccg.epfl.ch//chipseq
Description-en: tools performing common ChIP-Seq data analysis tasks
The ChIP-Seq software provides a set of tools performing common genome-
wide ChIP- seq analysis tasks, including positional correlation
analysis, peak detection, and genome partitioning into signal-rich and
signal-poor regions. These tools exist as stand-alone C programs and
perform the following tasks:
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1. Positional correlation analysis and generation of an aggregation
plot (AP) (chipcor),
2. Extraction of specific genome annotation features around reference
anchor points (chipextract),
3. Read centering or shifting (chipcenter),
4. Narrow peak caller using a fixed width peak size (chippeak),
5. Broad peak caller used for large regions of enrichment (chippart),
6. Feature selection tool based on a read count threshold (chipscore).
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Because the ChIP-Seq tools are primarily optimized for speed, they use
their own compact format for ChIP-seq data representation called SGA
(Simplified Genome Annotation). SGA is a line-oriented, tab-delimited
plain text format.
Description-md5: 7a2689deb8d8a514746221aea79879eb