aGOtool

aGOtool performs protein-abundance–aware Gene Ontology and UniProt keyword enrichment for post-translational modification (PTM) datasets to correct mass-spectrometry–based detection bias and reveal PTM-specific functional signals.


Key Features:

  • Protein-abundance–aware enrichment: Performs GO and UniProt keyword enrichment that weights background term frequencies by protein abundance distributions.
  • Statistical abundance correction: Implements a statistical correction that reweights GO-term frequencies according to protein abundance to reduce bias from highly expressed proteins.
  • Proteome binning: Partitions the proteome into abundance bins and reweights GO-term associations in proportion to the frequency of modification in each bin.
  • Abundance-corrected reference distribution: Generates an abundance-corrected reference distribution for enrichment testing.
  • PTM type support: Supports analysis of multiple PTM types, including phosphorylation, ubiquitination, acetylation, and succinylation.
  • Background comparisons: Enables comparisons of enrichment results across genome, observed proteome, and abundance-corrected backgrounds.
  • Bias reduction and sensitivity: Reduces false enrichment driven by sampling bias in mass-spectrometry–based PTM identification and enhances detection of PTM-specific and under-represented functional categories.

Scientific Applications:

  • PTM-specific functional inference: Distinguishing PTM-associated biological processes from artifacts of protein abundance in proteomics datasets.
  • Comparative PTM analysis: Comparing enrichment across different PTM types and across genome, observed proteome, and abundance-corrected backgrounds to identify PTM-specific or shared functions.
  • Reproducible PTM annotation: Supporting reproducible functional annotation of PTMs across diverse organisms and cell types.

Methodology:

Partitions the proteome into abundance bins, reweights GO-term associations proportional to the frequency of modification in each bin, and constructs an abundance-corrected reference distribution for enrichment analysis.

Topics

Collections

Details

Tool Type:
api
Operating Systems:
Linux, Windows, Mac
Programming Languages:
Shell, Python
Added:
9/3/2017
Last Updated:
11/24/2024

Operations

Publications

Schölz C, Lyon D, Refsgaard JC, Jensen LJ, Choudhary C, Weinert BT. Avoiding abundance bias in the functional annotation of posttranslationally modified proteins. Nature Methods. 2015;12(11):1003-1004. doi:10.1038/nmeth.3621. PMID:26513550.

Documentation