©1996-2019 All
Rights Reserved.
Online Journal of Bioinformatics . You may not store these pages in any form except
for your own personal use. All other usage or distribution is illegal under
international copyright treaties. Permission to use any of these pages in any
other way besides the before mentioned must be
gained in writing from the publisher. This article is exclusively copyrighted
in its entirety to OJB publications. This article may be copied once but may
not be, reproduced or re-transmitted without the
express permission of the editors. This journal satisfies the refereeing requirements
(DEST) for the Higher Education Research Data Collection (Australia). Linking:To link to this page or
any pages linking to this page you must link directly to this page only here
rather than put up your own page.
OJBTM
Online Journal of Bioinformatics ©
Volume 9 (2):108-112, 2008.
CateGOrizer: A Web-Based Program to Batch Analyze Gene
Ontology Classification Categories
Hu Zhi-Liang1, Bao
J2, Reecy JM1
1Department(s) of
Animal Science and 2Computer Science,
Iowa State University, Ames, Iowa, USA
abstract
Zhi-Liang
Hu, Bao J, Reecy JM., CateGOrizer: A Web-Based Program to Batch Analyze Gene
Ontology Classification Categories, Onl J Bioinform., 9(2):108-112, 2008. With
the accelerating rate at which gene-associated research data are accumulated, there
is a growing need for batch analysis of large-scale sequence annotations such
as Gene Ontology (GO). A frustrating problem with GO annotation has been
the inability to properly count the occurrences of GO terms within certain
parental categories under a given classification method such as GO Slim.
The GO term occurrence count by category can also be time consuming when all
possible paths are searched with looped structured query language (SQL).
The CateGOrizer we present here is designed to
overcome these problems. The CateGOrizer
utilizes pre-computed transitive closure paths, performs GO classification
count under any given GO slim through a web interface. Our approach has
significantly reduced the run time and improved flexibility in comparison to
peer programs. However, users are advised to take caution when choosing a
proper classification system, to design a strategy objectively count GO terms
and properly interpret the results.
Key-Words: Analysis, Gene Ontology, classifications
FULL-TEXT (SUBSCRIPTION OR PURCHASE TITLE $25USD)