How to Install and Uninstall r-bioc-bitseq Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: May 06,2024

1. Install "r-bioc-bitseq" package

Please follow the instructions below to install r-bioc-bitseq on Ubuntu 20.10 (Groovy Gorilla)

$ sudo apt update $ sudo apt install r-bioc-bitseq

2. Uninstall "r-bioc-bitseq" package

Please follow the instructions below to uninstall r-bioc-bitseq on Ubuntu 20.10 (Groovy Gorilla):

$ sudo apt remove r-bioc-bitseq $ sudo apt autoclean && sudo apt autoremove

3. Information about the r-bioc-bitseq package on Ubuntu 20.10 (Groovy Gorilla)

Package: r-bioc-bitseq
Architecture: amd64
Version: 1.32.0+dfsg-1build1
Priority: optional
Section: universe/gnu-r
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 5985
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-api-bioc-3.11, r-bioc-rsamtools (>= 1.99.3), r-bioc-s4vectors, r-bioc-iranges, r-bioc-rhtslib (>= 1.15.5), libc6 (>= 2.29), libcurl4 (>= 7.18.0), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 5.2)
Suggests: r-bioc-edger, r-bioc-deseq
Filename: pool/universe/r/r-bioc-bitseq/r-bioc-bitseq_1.32.0+dfsg-1build1_amd64.deb
Size: 1141756
MD5sum: 9eb8d4ac6b5fe7448556176028486487
SHA1: 8a662b8499c178d3e7a8fd705a33408102fea066
SHA256: 6ec2a67cfb7bfad8dab6ad632d26a22f1854a7c7f009c03e4b6ea74044893f29
SHA512: f7303a834797c68e9528342c1b1c7828f07bdd8d211de6c1aa3b18253cebf912ca84e591b9712afc54688c704e3bb2934383feec8ae06f9f88b036cc059659e5
Homepage: https://bioconductor.org/packages/BitSeq/
Description-en: transcript expression inference and analysis for RNA-seq data
The BitSeq package is targeted for transcript expression
analysis and differential expression analysis of RNA-seq data
in two stage process. In the first stage it uses Bayesian
inference methodology to infer expression of individual
transcripts from individual RNA-seq experiments. The second
stage of BitSeq embraces the differential expression analysis
of transcript expression. Providing expression estimates from
replicates of multiple conditions, Log-Normal model of the
estimates is used for inferring the condition mean transcript
expression and ranking the transcripts based on the likelihood
of differential expression.
Description-md5: 22ff866a439a9bfddee6c325ea799b9b