DaGO-Fun - Database for GO-based Functional Annotation Analysis

Browsing Tools

Browsing Resources

Protein Resources

Protein Interactions

Annotation Analysis

DaGO-Fun General Description

DaGO-Fun is an integrated set of GO-based functional analysis tools for a given set of gene or protein identifiers from UniProt/SwissProt using Information Content (IC) based GO semantic similarity of these proteins/genes. Protein GO annotations are retrieved from the Gene Ontology Annotation (GOA) project and IC of GO terms are computed using the Gene Ontology (GO) Directed Acyclic Graph (DAG). This tool illustrates the usage and presents biological applications of different GO measures published in the context of high-throughput biology technologies.

1. GO Analysis Tools

The DaGO-Fun tool provides a comprehensive and customized set of Gene Ontology (GO) based annotation analysis tools that integrate the large amounts of biological knowledge that GO offers in describing genes or groups of genes through term semantic similarity measurements. These different tools are able to:

  • Measure Information Content based GO term and protein semantic similarity scores (IT-GOM).
  • Identify enriched GO terms accounting for uncertainty in an annotation dataset (GOSS-FEAT).
  • Discover functionally related or similar genes/proteins based on their GO terms (GOSP-FCT).
  • Retrieve genes or proteins by their GO annotations for disease gene and target discovery (GOSP-FIT).

2. Protein Functional Networks and Prediction Tools

In an organism, a biological function is performed by a set of interacting genes or proteins, and mediated through protein-protein interactions. This tool integrates existing genomic sequences and functional data to generate weighted functional interaction networks for Mycobacterial pathogens and for the host for functional analyses at the systems level. Typically, these networks are built by quantitatively assessing the confidence level of protein pairwise interactions detected using diverse biological data sources, including data from:

  • Genomic context (conserved genomic neighborhood and gene fusion events, and phylogenetic distribution patterns)
  • Sequence (sequence similarity and domain data)
  • High-throughput data (microarray or co-expression analysis, text mining, knowledge from pathway databases, and known physical interactions).

Currently, the tool includes three species, namely Mycobaterium tuberculosis (MTB), Mycobacterium leprae and the host (human or Homo sapiens). These functional networks have been used to predict biological functions of uncharacterized proteins in the two mycobacteria to enhance our understanding of these organisms' behaviour, identifying targets within them and filtering these potential targets using Host-Pathogen functional interaction networks.