goTools

goTools annotates oligonucleotide (oligo) ID lists with Gene Ontology (GO) terms to enable functional characterization and comparison of gene sets across biological processes, cellular components, and molecular functions.


Key Features:

  • Integration with Gene Ontology database: Interfaces with the Gene Ontology (GO) database to retrieve GO terms and annotations for input identifiers.
  • Annotation across GO aspects: Maps oligonucleotide or gene ID lists to GO terms covering biological process, cellular component, and molecular function ontologies.
  • Description and comparison of gene sets: Describes individual gene sets and compares multiple oligo or gene lists to identify shared and distinct GO annotations.
  • Statistical analysis of GO terms: Performs statistical analyses to identify significantly enriched or overrepresented GO terms and highlight biological themes.

Scientific Applications:

  • Functional annotation: Annotates genes or oligos to assign putative biological roles based on GO terms.
  • Comparative analysis: Compares different gene or oligo lists to identify conserved or divergent functional themes across conditions or species.
  • High-throughput data interpretation: Interprets large-scale expression datasets such as RNA sequencing and microarray studies using GO-based annotations.

Methodology:

Processes input oligo ID lists, interfaces with the Gene Ontology database to retrieve and map relevant GO terms (biological process, cellular component, molecular function), and performs statistical analyses to assess significance of GO term enrichment.

Topics

Collections

Details

License:
GPL-2.0
Cost:
Free of charge
Tool Type:
command-line tool, library
Operating Systems:
Linux, Windows, Mac
Programming Languages:
R
Added:
1/17/2017
Last Updated:
11/25/2024

Operations

Data Inputs & Outputs

Gene expression comparison

Inputs

Outputs

Publications

Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry RA, Lawrence M, Love MI, MacDonald J, Obenchain V, Oleś AK, Pagès H, Reyes A, Shannon P, Smyth GK, Tenenbaum D, Waldron L, Morgan M. Orchestrating high-throughput genomic analysis with Bioconductor. Nature Methods. 2015;12(2):115-121. doi:10.1038/nmeth.3252. PMID:25633503. PMCID:PMC4509590.

Documentation

Downloads