Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/78703
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Type: Journal article
Title: Comparative GO: a web application for comparative Gene Ontology and Gene Ontology-based gene selection in bacteria
Author: Fruzangohar, M.
Ebrahimie, E.
Ogunniyi, A.
Mahdi, L.
Paton, J.
Adelson, D.
Citation: PLoS One, 2013; 8(3):1-8
Publisher: Public Library of Science
Issue Date: 2013
ISSN: 1932-6203
1932-6203
Statement of
Responsibility: 
Mario Fruzangohar, Esmaeil Ebrahimie, Abiodun D. Ogunniyi, Layla K. Mahdi, James C. Paton, David L. Adelson
Abstract: The primary means of classifying new functions for genes and proteins relies on Gene Ontology (GO), which defines genes/proteins using a controlled vocabulary in terms of their Molecular Function, Biological Process and Cellular Component. The challenge is to present this information to researchers to compare and discover patterns in multiple datasets using visually comprehensible and user-friendly statistical reports. Importantly, while there are many GO resources available for eukaryotes, there are none suitable for simultaneous, graphical and statistical comparison between multiple datasets. In addition, none of them supports comprehensive resources for bacteria. By using Streptococcus pneumoniae as a model, we identified and collected GO resources including genes, proteins, taxonomy and GO relationships from NCBI, UniProt and GO organisations. Then, we designed database tables in PostgreSQL database server and developed a Java application to extract data from source files and loaded into database automatically. We developed a PHP web application based on Model-View-Control architecture, used a specific data structure as well as current and novel algorithms to estimate GO graphs parameters. We designed different navigation and visualization methods on the graphs and integrated these into graphical reports. This tool is particularly significant when comparing GO groups between multiple samples (including those of pathogenic bacteria) from different sources simultaneously. Comparing GO protein distribution among up- or down-regulated genes from different samples can improve understanding of biological pathways, and mechanism(s) of infection. It can also aid in the discovery of genes associated with specific function(s) for investigation as a novel vaccine or therapeutic targets.
Keywords: Bacteria; Computational Biology; Algorithms; Internet; Software; User-Computer Interface; Databases, Genetic; Gene Ontology
Description: Extent: 8p.
Rights: © 2013 Fruzangohar et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
RMID: 0020126567
DOI: 10.1371/journal.pone.0058759
Appears in Collections:Molecular and Biomedical Science publications
Environment Institute publications

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