GAPGOM

DOI: 10.18129/B9.bioc.GAPGOM  

GAPGOM (novel Gene Annotation Prediction and other GO Metrics)

Bioconductor version: Release (3.16)

Collection of various measures and tools for lncRNA annotation prediction put inside a redistributable R package. The package contains two main algorithms; lncRNA2GOA and TopoICSim. lncRNA2GOA tries to annotate novel genes (in this specific case lncRNAs) by using various correlation/geometric scoring methods on correlated expression data. After correlating/scoring, the results are annotated and enriched. TopoICSim is a topologically based method, that compares gene similarity based on the topology of the GO DAG by information content (IC) between GO terms.

Author: Rezvan Ehsani [aut, cre], Casper van Mourik [aut], Finn Drabløs [aut]

Maintainer: Rezvan Ehsani <rezvanehsani74 at gmail.com>

Installation

To install this package, start R (version "4.2") and enter:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("GAPGOM")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

PDF		Reference Manual

Details

biocViews	GO, GeneExpression, GenePrediction, Software
Version	1.14.0
In Bioconductor since	BioC 3.9 (R-3.6) (4 years)
License	MIT + file LICENSE
Depends	R (>= 4.0)
Imports	stats, utils, methods, Matrix, fastmatch, plyr, dplyr, magrittr, data.table, igraph, graph, RBGL, GO.db, org.Hs.eg.db, org.Mm.eg.db, GOSemSim, GEOquery, AnnotationDbi, Biobase, BiocFileCache, matrixStats
LinkingTo
Suggests	org.Dm.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Dr.eg.db, org.Ce.eg.db, org.At.tair.db, org.EcK12.eg.db, org.Bt.eg.db, org.Cf.eg.db, org.Ag.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Pt.eg.db, org.Pf.plasmo.db, org.Mmu.eg.db, org.Ss.eg.db, org.Xl.eg.db, testthat, pryr, knitr, rmarkdown, prettydoc, ggplot2, kableExtra, profvis, reshape2
SystemRequirements
Enhances
URL	https://github.com/Berghopper/GAPGOM/
BugReports	https://github.com/Berghopper/GAPGOM/issues/
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Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package
Windows Binary
macOS Binary (x86_64)
macOS Binary (arm64)
Source Repository	git clone https://git.bioconductor.org/packages/GAPGOM
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/GAPGOM
Package Short Url	https://bioconductor.org/packages/GAPGOM/
Package Downloads Report	Download Stats