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Bioinformatics. 2018 Aug 20. doi: 10.1093/bioinformatics/bty689. [Epub ahead of print]

MCO: towards an ontology and unified vocabulary for a framework-based annotation of microbial growth conditions.

Author information

1
Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México.

Abstract

Motivation:

A major component in increasing our understanding of the biology of an organism is the mapping of its genotypic potential into its phenotypic expression profiles. This mapping is executed by the machinery of gene regulation, which is essentially studied by changes in growth conditions. Although many efforts have been made to systematize the annotation of experimental conditions in microbiology, the available annotations are not based on a consistent and controlled vocabulary, making difficult the identification of biologically meaningful comparisons of knowledge derived from different experiments or laboratories.

Results:

We curated terms related to experimental conditions that affect gene expression in Escherichia coli K-12. Since this is the best-studied microorganism, the collected terms are the seed for the Microbial Conditions Ontology (MCO), a controlled and structured vocabulary that can be expanded to annotate microbial conditions in general. Moreover, we developed an annotation framework to describe experimental conditions, providing the foundation to identify regulatory networks that operate under particular conditions.

Availability:

As far as we know, MCO is the first ontology for growth conditions of any bacterial organism, and it is available at http://regulondb.ccg.unam.mx and https://github.com/microbial-conditions-ontology. Furthermore, we will disseminate MCO throughout the Open Biological and Biomedical Ontology (OBO) Foundry in order to set a standard for the annotation of gene expression data. This will enable comparison of data from diverse data sources.

Supplementary information:

Supplementary data are available at Bioinformatics online.

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