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What we can learn about Escherichia coli through application of Gene Ontology.

ABSTRACT: How we classify the genes, products and complexes that are present or absent in genomes, transcriptomes, proteomes and other datasets helps us place biological objects into subsystems with common functions, see how molecular functions are used to implement biological processes and compare the biology of different species and strains. Gene Ontology (GO) is one of the most successful systems for classifying biological function. Although GO is widely used for eukaryotic genomics, it has not yet been widely used for bacterial systems. The potential applications of GO are currently limited by the need to improve the annotation of bacterial genomes with GO and to improve how prokaryotic biology is represented in the ontology. Here, we discuss why GO should be adopted by microbiologists, and describe recent efforts to build and maintain high-quality GO annotation for Escherichia coli as a model system.

SUBMITTER: Hu JC

PROVIDER: S-EPMC3575750 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

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What we can learn about Escherichia coli through application of Gene Ontology.

Hu James C JC Karp Peter D PD Keseler Ingrid M IM Krummenacker Markus M Siegele Deborah A DA

Trends in microbiology 20090701 7

How we classify the genes, products and complexes that are present or absent in genomes, transcriptomes, proteomes and other datasets helps us place biological objects into subsystems with common functions, see how molecular functions are used to implement biological processes and compare the biology of different species and strains. Gene Ontology (GO) is one of the most successful systems for classifying biological function. Although GO is widely used for eukaryotic genomics, it has not yet bee ...[more]

PMID: 19576778

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Project description:BackgroundA recent dialogue in the field of play, learn, and teach outdoors (referred to as "PLaTO" hereafter) demonstrated the need for developing harmonized and consensus-based terminology, taxonomy, and ontology for PLaTO. This is important as the field evolves and diversifies in its approaches, contents, and contexts over time and in different countries, cultures, and settings. Within this paper, we report the systematic and iterative processes undertaken to achieve this objective, which has built on the creation of the global PLaTO-Network (PLaTO-Net).MethodsThis project comprised of four major methodological phases. First, a systematic scoping review was conducted to identify common terms and definitions used pertaining to PLaTO. Second, based on the results of the scoping review, a draft set of key terms, taxonomy, and ontology were developed, and shared with PLaTO members, who provided feedback via four rounds of consultation. Third, PLaTO terminology, taxonomy, and ontology were then finalized based on the feedback received from 50 international PLaTO member participants who responded to ≥ 3 rounds of the consultation survey and dialogue. Finally, efforts to share and disseminate project outcomes were made through different online platforms.ResultsThis paper presents the final definitions and taxonomy of 31 PLaTO terms along with the PLaTO-Net ontology model. The model incorporates other relevant concepts in recognition that all the aspects of the model are interrelated and interconnected. The final terminology, taxonomy, and ontology are intended to be applicable to, and relevant for, all people encompassing various identities (e.g., age, gender, culture, ethnicity, ability).ConclusionsThis project contributes to advancing PLaTO-based research and facilitating intersectoral and interdisciplinary collaboration, with the long-term goal of fostering and strengthening PLaTO's synergistic linkages with healthy living, environmental stewardship, climate action, and planetary health agendas. Notably, PLaTO terminology, taxonomy and ontology will continue to evolve, and PLaTO-Net is committed to advancing and periodically updating harmonized knowledge and understanding in the vast and interrelated areas of PLaTO.

Project description:BackgroundPathogenic Escherichia coli infections cause various diseases in humans and many animal species. However, with extensive E. coli vaccine research, we are still unable to fully protect ourselves against E. coli infections. To more rational development of effective and safe E. coli vaccine, it is important to better understand E. coli vaccine-associated gene interaction networks.MethodsIn this study, we first extended the Vaccine Ontology (VO) to semantically represent various E. coli vaccines and genes used in the vaccine development. We also normalized E. coli gene names compiled from the annotations of various E. coli strains using a pan-genome-based annotation strategy. The Interaction Network Ontology (INO) includes a hierarchy of various interaction-related keywords useful for literature mining. Using VO, INO, and normalized E. coli gene names, we applied an ontology-based SciMiner literature mining strategy to mine all PubMed abstracts and retrieve E. coli vaccine-associated E. coli gene interactions. Four centrality metrics (i.e., degree, eigenvector, closeness, and betweenness) were calculated for identifying highly ranked genes and interaction types.ResultsUsing vaccine-related PubMed abstracts, our study identified 11,350 sentences that contain 88 unique INO interactions types and 1,781 unique E. coli genes. Each sentence contained at least one interaction type and two unique E. coli genes. An E. coli gene interaction network of genes and INO interaction types was created. From this big network, a sub-network consisting of 5 E. coli vaccine genes, including carA, carB, fimH, fepA, and vat, and 62 other E. coli genes, and 25 INO interaction types was identified. While many interaction types represent direct interactions between two indicated genes, our study has also shown that many of these retrieved interaction types are indirect in that the two genes participated in the specified interaction process in a required but indirect process. Our centrality analysis of these gene interaction networks identified top ranked E. coli genes and 6 INO interaction types (e.g., regulation and gene expression).ConclusionsVaccine-related E. coli gene-gene interaction network was constructed using ontology-based literature mining strategy, which identified important E. coli vaccine genes and their interactions with other genes through specific interaction types.

Dataset Information

What we can learn about Escherichia coli through application of Gene Ontology.

Publications

What we can learn about Escherichia coli through application of Gene Ontology.

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