# Ommicrobe, an open-access database of microbial habitats, phenotypes and uses extracted from text

Sandra Dérozier, Robert Bossy, Louise Deléger, Mouhamadou Ba, Estelle Chaix, Valentin Loux, Hélène Falentin, Claire Nédellec

► To cite this version:

Sandra Dérozier, Robert Bossy, Louise Deléger, Mouhamadou Ba, Estelle Chaix, et al.. Ommicrobe, an open-access database of microbial habitats, phenotypes and uses extracted from text. JOBIM 2022, Jul 2022, Rennes, France. , Proceedings Posters Demos, pp.42. hal-04061565

HAL Id: hal-04061565

https://hal.inrae.fr/hal-04061565

Submitted on 6 Apr 2023

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Distributed under a Creative Commons Attribution 4.0 International License
# Ommicrobe, an open-access database of microbial habitats, phenotypes and uses extracted from text

S. Dérozier¹, R. Bossy¹, L. Deléger¹, M. Ba¹,², E. Chaix¹, V. Loux¹,², H. Falentin³, C. Nédellec¹

¹ Université Paris-Saclay, INRAE, MalAGE, Jouy-en-Josas, France

² Université Paris-Saclay, INRAE, BioinfOmics, MIGALE Bioinformatics Facility, Jouy-en-Josas 78350, France

³ INRAE, STLO, Rennes, France

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INRAE

https://ommicrobe.migale.inrae.fr

The drastic increase in microbe descriptions, habitats, phenotypes and uses in databases, reports and papers presents a two-fold challenge for the access to the information: (1) a standard representation to integrate heterogeneous data and (2) the normalization of textual descriptions by semantic analysis. Recent information extraction technologies from the text mining domain offer powerful ways to detect and structure textual information along ontology-based representations.

The Ommicrobe database contains around 1 million descriptions of microbe properties and is populated by an Information Extraction workflow:

## a) 6 information sources:

- biological resource catalogues: Inrae **CIRM**, **BacDive** by DSMZ,

- sequence database: **GenBank**,

- scientific literature: **PubMed** abstracts in microbiology.

## b) entity recognition: microbe taxa, habitats, phenotypes and uses.

## c) normalization with taxa from the NCBI taxonomy [2] and concepts from the OntoBiotope ontology [3].

## d) relation extraction between entities.

All information is aggregated, merged and indexed to be accessible from the information system. Ommicrobe offers powerful ways to express simple and complex ontology-based queries.

Ommicrobe also exposes an **API** (Application Programming Interface) that allows users to automatically integrate microbe biodiversity knowledge in external information systems.

<figure><img src="image_3.png" /><figcaption>Figure 1. Text mining process.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Figure 2. Ommicrobe web interface.</figcaption></figure>

**Quickly select** the desired type of search with tabbed navigation (A). Searches (B) are carried out using habitats, phenotypes, uses and taxa names, synonyms and hierarchy from the OntoBiotope ontology and the NCBI taxonomy. Depending on the type of search, several filters are available: e.g. **source** of the information, or **food safety** certification. The user may also **navigate in the hierarchical structure** of OntoBiotope (C) and select the class of interest, habitats, phenotypes or uses. The results (D) are displayed in columns for better readability. The original PubMed or GenBank entry is accessible through a link.

Ommicrobe has been used to **quickly target useful strains** in a **food innovation application** [4]. The Ommicrobe database was used to **identify species** capable of **fermenting soybean juice**. Out of 10 species represented by 229 strains tested in vitro, 9 species, i.e. 179 strains, acidified (pH<6) soybean juice within 48h.

In future work, we plan to extend Ommicrobe to include new data sources such as GOLD, GBIF, ...

## References

1. Reimer LC, Vetcininova A, Carbasse JS, Söhngen C, Gleim D, Ebeling C, Overmann J. BacDive in 2019: bacterial phenotypic data for High-throughput biodiversity analysis. *Nucleic Acids Res.*, 47(D1):D631-D636, 2019.

2. Schoch CL, Ciufo S, Domrachev M, Hotton CL, Kannan S, Khovanskaya R, Leipe D, Mcveigh R, O'Neill K, Robbertse B, Sharma S, Soussov V, Sullivan JP, Sun L, Turner S, Karsch-Mizrachi I. NCBI Taxonomy: a comprehensive update on curation, resources and tools. *Database (Oxford)*, 2020.

3. Chaix E., Deléger L., Bossy R., Nédellec C. Text mining tools for extracting information about microbial biodiversity in food. *Food Microbiology*, 2018.

4. Harlé O., Falentin H., Niay J., Valence F., Courselaud C., Chuat V., Maillard M-B., Guédon E., Deutsch S-M., Thierry A. Diversity of the metabolic profiles of a broad range of lactic acid bacteria in soy juice fermentation. *Food microbiology*, 89, 2020.

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