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Omnicrobe, an open-access database of microbial habitats and phenotypes using a comprehensive text mining and data fusion approach
The dramatic increase in the number of microbe descriptions in databases, reports, and papers presents a two-fold challenge for accessing the information: integration of heterogeneous data in a standard ontology-based representation and normalization of the textual descriptions by semantic analysis....
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858090/ https://www.ncbi.nlm.nih.gov/pubmed/36662691 http://dx.doi.org/10.1371/journal.pone.0272473 |
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author | Dérozier, Sandra Bossy, Robert Deléger, Louise Ba, Mouhamadou Chaix, Estelle Harlé, Olivier Loux, Valentin Falentin, Hélène Nédellec, Claire |
author_facet | Dérozier, Sandra Bossy, Robert Deléger, Louise Ba, Mouhamadou Chaix, Estelle Harlé, Olivier Loux, Valentin Falentin, Hélène Nédellec, Claire |
author_sort | Dérozier, Sandra |
collection | PubMed |
description | The dramatic increase in the number of microbe descriptions in databases, reports, and papers presents a two-fold challenge for accessing the information: integration of heterogeneous data in a standard ontology-based representation and normalization of the textual descriptions by semantic analysis. Recent text mining methods offer powerful ways to extract textual information and generate ontology-based representation. This paper describes the design of the Omnicrobe application that gathers comprehensive information on habitats, phenotypes, and usages of microbes from scientific sources of high interest to the microbiology community. The Omnicrobe database contains around 1 million descriptions of microbe properties. These descriptions are created by analyzing and combining six information sources of various kinds, i.e. biological resource catalogs, sequence databases and scientific literature. The microbe properties are indexed by the Ontobiotope ontology and their taxa are indexed by an extended version of the taxonomy maintained by the National Center for Biotechnology Information. The Omnicrobe application covers all domains of microbiology. With simple or rich ontology-based queries, it provides easy-to-use support in the resolution of scientific questions related to the habitats, phenotypes, and uses of microbes. We illustrate the potential of Omnicrobe with a use case from the food innovation domain. |
format | Online Article Text |
id | pubmed-9858090 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-98580902023-01-21 Omnicrobe, an open-access database of microbial habitats and phenotypes using a comprehensive text mining and data fusion approach Dérozier, Sandra Bossy, Robert Deléger, Louise Ba, Mouhamadou Chaix, Estelle Harlé, Olivier Loux, Valentin Falentin, Hélène Nédellec, Claire PLoS One Research Article The dramatic increase in the number of microbe descriptions in databases, reports, and papers presents a two-fold challenge for accessing the information: integration of heterogeneous data in a standard ontology-based representation and normalization of the textual descriptions by semantic analysis. Recent text mining methods offer powerful ways to extract textual information and generate ontology-based representation. This paper describes the design of the Omnicrobe application that gathers comprehensive information on habitats, phenotypes, and usages of microbes from scientific sources of high interest to the microbiology community. The Omnicrobe database contains around 1 million descriptions of microbe properties. These descriptions are created by analyzing and combining six information sources of various kinds, i.e. biological resource catalogs, sequence databases and scientific literature. The microbe properties are indexed by the Ontobiotope ontology and their taxa are indexed by an extended version of the taxonomy maintained by the National Center for Biotechnology Information. The Omnicrobe application covers all domains of microbiology. With simple or rich ontology-based queries, it provides easy-to-use support in the resolution of scientific questions related to the habitats, phenotypes, and uses of microbes. We illustrate the potential of Omnicrobe with a use case from the food innovation domain. Public Library of Science 2023-01-20 /pmc/articles/PMC9858090/ /pubmed/36662691 http://dx.doi.org/10.1371/journal.pone.0272473 Text en © 2023 Dérozier et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Dérozier, Sandra Bossy, Robert Deléger, Louise Ba, Mouhamadou Chaix, Estelle Harlé, Olivier Loux, Valentin Falentin, Hélène Nédellec, Claire Omnicrobe, an open-access database of microbial habitats and phenotypes using a comprehensive text mining and data fusion approach |
title | Omnicrobe, an open-access database of microbial habitats and phenotypes using a comprehensive text mining and data fusion approach |
title_full | Omnicrobe, an open-access database of microbial habitats and phenotypes using a comprehensive text mining and data fusion approach |
title_fullStr | Omnicrobe, an open-access database of microbial habitats and phenotypes using a comprehensive text mining and data fusion approach |
title_full_unstemmed | Omnicrobe, an open-access database of microbial habitats and phenotypes using a comprehensive text mining and data fusion approach |
title_short | Omnicrobe, an open-access database of microbial habitats and phenotypes using a comprehensive text mining and data fusion approach |
title_sort | omnicrobe, an open-access database of microbial habitats and phenotypes using a comprehensive text mining and data fusion approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858090/ https://www.ncbi.nlm.nih.gov/pubmed/36662691 http://dx.doi.org/10.1371/journal.pone.0272473 |
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