How to Install and Uninstall r-bioc-grohmm Package on Ubuntu 21.10 (Impish Indri)

Last updated: December 28,2024

1. Install "r-bioc-grohmm" package

Please follow the steps below to install r-bioc-grohmm on Ubuntu 21.10 (Impish Indri)

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

2. Uninstall "r-bioc-grohmm" package

Here is a brief guide to show you how to uninstall r-bioc-grohmm on Ubuntu 21.10 (Impish Indri):

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

3. Information about the r-bioc-grohmm package on Ubuntu 21.10 (Impish Indri)

Package: r-bioc-grohmm
Architecture: amd64
Version: 1.24.0-1
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: 4499
Depends: r-base-core (>= 4.0.3-1), r-api-4.0, r-api-bioc-3.12, r-cran-mass, r-bioc-s4vectors (>= 0.17.25), r-bioc-iranges (>= 2.13.12), r-bioc-genomeinfodb, r-bioc-genomicranges (>= 1.31.8), r-bioc-genomicalignments (>= 1.15.6), r-bioc-rtracklayer (>= 1.39.7), libc6 (>= 2.14)
Suggests: r-bioc-biocstyle, r-bioc-genomicfeatures, r-bioc-edger, r-bioc-org.hs.eg.db
Filename: pool/universe/r/r-bioc-grohmm/r-bioc-grohmm_1.24.0-1_amd64.deb
Size: 4436652
MD5sum: bc210096cd27028d9538dcf762bcbfba
SHA1: f1b2ce82051a126cebff3a0a62b15459793f8081
SHA256: e546f78ce0bb079b0833eeac69e339dda3686438c000d558a228b0636001a7a3
SHA512: ed7b6a2aba63becb7587bbb9f5552b60891f75eed174faa848b592f80bfa9935f1ac0c869b1aa1d008dfaed514545f9d7bc92bc4e2a8811a1f8f2e63dbeeedc9
Homepage: https://bioconductor.org/packages/groHMM/
Description-en: GRO-seq Analysis Pipeline
This BioConductor package provides a pipeline for the analysis of GRO-
seq data. Among the more advanced features, r-bioc-grohmm predicts the
boundaries of transcriptional activity across the genome de novo using a
two-state hidden Markov model (HMM).
.
The used model essentially divides the genome into transcribed and non-
transcribed regions in a strand specific manner. HMMs are used to
identify the leading edge of Pol II at genes activated by a stimulus in
GRO-seq time course data. This approach allows the genome-wide
interrogation of transcription rates in cells.
Description-md5: e4ad61448703c8dc65b7219979430710