Link Gene Ontology IDs to cell types
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Updated
Sep 2, 2023 - R
The mission of the GO Consortium is to develop a comprehensive, computational model of biological systems, ranging from the molecular to the organism level, across the multiplicity of species in the tree of life.
The Gene Ontology (GO) knowledgebase is the world’s largest source of information on the functions of genes. This knowledge is both human-readable and machine-readable, and is a foundation for computational analysis of large-scale molecular biology and genetics experiments in biomedical research.
Link Gene Ontology IDs to cell types
RNA-seq pipeline to identify wasp-induced gene expression in Drosophila
Differential Gene Expression (DGE) Analysis in Curated Microarray Data of Breast Cancer Subtypes
Sample code for cross-species analysis of MYC-driven transgenic zebrafish cancer model
This is a version of topGO using roxygen2 and Rmd
Set of small R scripts helpful in various bioinformatics projects
Functional analysis of metabolic and transcriptomic data
miRinGO: Prediction of GO terms indirectly targeted by human microRNAs
Code for representing Gene Ontology Enrichment Analysis as a circular barplot.
Gene prioritisation tool using a weighted network methodology based on Gene Ontology and Human Phenotype Ontology annotations to infer closely related genes to given genes of interest.
scrape REVIGO Gene Ontology web app and plot results
R Package for Gene Ontology Label Discernment and Identification.
ChIP-seq/RNA-seq analysis software suite for gene expression heatmaps
A standalone interactive application for detecting biological significance on a set of genes
⛳ GO-terms Semantic Similarity Measures
Released 1999