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Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

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DaGO-Fun: tool for Gene Ontology-based functional analysis using term information content measures.

Gaston K Mazandu¹, Nicola J Mulder

¹Computational Biology Group, Department of Clinical Laboratory Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, Observatory, Cape Town, 7925, South Africa. gmazandu@cbio.uct.ac.za.

BMC Bioinformatics

|September 27, 2013

Summary

DaGO-Fun is a new online tool that integrates various Gene Ontology (GO) similarity measures for protein analysis. It aids researchers in exploring, analyzing, and comparing GO terms and proteins, enhancing biological insights.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Gene Ontology (GO) data enhances protein analysis outcomes.
Numerous GO semantic similarity measures exist, integrating biological knowledge.
A unified tool is needed to explore diverse GO similarity approaches and applications.

Purpose of the Study:

To develop a unified online tool for exploring and analyzing Gene Ontology (GO) similarity measures.
To provide a platform for comparing different GO similarity approaches and their biological applications.

Main Methods:

Developed DaGO-Fun, an online tool integrating multiple GO similarity measures.
Utilizes GO data and UniProt protein annotations from the GOA project.
Precomputes GO term information content (IC) for rapid query responses.

Main Results:

DaGO-Fun offers a unified platform for GO term and protein analysis.
The tool incorporates various IC-based GO similarity measures (topology- and annotation-based).
Enables rapid exploration and comparison of GO similarity approaches.

Conclusions:

DaGO-Fun facilitates the selection of appropriate GO similarity measures for specific applications.
Includes biological applications such as gene retrieval, clustering, and term enrichment analysis.
Empowers researchers to effectively leverage GO semantic similarity in their studies.