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Tuberculosis (Edinb). 2015 Sep;95(5):570-4. doi: 10.1016/j.tube.2015.05.012. Epub 2015 Jun 30.

A tuberculosis ontology for host systems biology.

Author information

1
Department of Biostatistics, University of Washington, School of Public Health, Seattle, WA, USA.
2
Center for Tuberculosis Research, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
3
LabKey Corporation, Seattle, WA, USA.
4
Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA, USA.
5
Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA.
6
Department of Pathobiological Sciences, Louisiana State University School of Medicine, Baton Rouge, LA, USA; Tulane National Primate Research Center, Covington, LA, USA.
7
Tulane National Primate Research Center, Covington, LA, USA.
8
Knowledge Synthesis Inc., Berkeley, CA, USA. Electronic address: hugh@knowledgesynthesis.com.

Abstract

A major hurdle facing tuberculosis (TB) investigators who want to utilize a rapidly growing body of data from both systems biology approaches and omics technologies is the lack of a standard vocabulary for data annotation and reporting. Lacking a means to readily compare samples from different research groups, a significant quantity of potentially informative data is largely ignored by researchers. To facilitate standardizing data across studies, a simple ontology of TB terms was developed to provide a common vocabulary for annotating data sets. New terminology was developed to address animal models and experimental systems, and existing clinically focused terminology was modified and adapted. This ontology can be used to annotate host TB data in public databases and collaborations, thereby standardizing database searches and allowing researchers to more easily compare results. To demonstrate the utility of a standard TB ontology for host systems biology, a web application was developed to annotate and compare human and animal model gene expression data sets.

KEYWORDS:

GEO; Gene expression; Mycobacterium; Ontology; Transcriptomics

PMID:
26190839
PMCID:
PMC4554888
DOI:
10.1016/j.tube.2015.05.012
[Indexed for MEDLINE]
Free PMC Article

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