The StrainDiscoveryDatabase: an open framework for standardized microbial strain data
- 1Leibniz Institute DSMZ German Collection of Microorganisms and Cell Cultures
Abstract
The vast amount of existing data on microbial strains holds immense potential to revolutionize bioindustry through the application of Artificial Intelligence (AI). However, the training of robust predictive AI models requires large-scale, unified, and non-redundant microbial datasets, which is currently severely hindered by the deep fragmentation of the data and the existence of synonymous strain identifiers in different culture collections. To overcome these infrastructural bottlenecks, we have established the StrainDiscoveryDatabase (SDD), a comprehensive, machine-readable dataset encompassing over 6.2 million harmonized data points for 256,889 microbial strains. The SDD does not rely on its own data repository, but rather on existing data that is retrieved on the fly from highly curated databases. Through an automated pipeline phenotypic, genotypic, and contextual data are systematically retrieved via the Application Programming Interfaces (APIs) of the Bacterial Diversity database (BacDive), the Microbial Resource Research Infrastructure Information System (MIRRI-IS) and the catalogue of the DSMZ. In order to reliably resolve synonymous strain identifiers, the StrainInfo database and its identification tools are employed, enabling the accurate deduplication and unification of records from disparate sources. The resulting aggregated, globally unique dataset is provided in a highly standardized JSON format in strict adherence to the FAIR data principles. By bridging isolated database silos and linking distributed knowledge to discrete biological entities, the SDD provides a high-quality, foundational resource designed to accelerate trait-based strain discovery, large-scale comparative analysis, and machine learning applications, thereby supporting the translation of the extensive existing knowledge on microbial traits to bioindustrial applications.
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BibTeX
@misc{witte2026straindiscoverydatabase,
title = {{The StrainDiscoveryDatabase: an open framework for standardized microbial strain data}},
author = {J. F. Witte and A. Lissin and I. Schober and C. Ebeling and H. Lueken and J. Koblitz and A. Yurkov and B. Bunk and J. M. Lopez-Coronado and A. Vaello and A. Zuzuarregui and R. Aznar and A. Garcia-Donate and A. M. P. Melo and G. Verkleij and R. P. de Vries and V. Robert and W. Meyer and J. Overmann and L. C. Reimer},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.09.750348},
url = {https://www.biorxiv.org/content/10.64898/2026.09.09.750348v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - The StrainDiscoveryDatabase: an open framework for standardized microbial strain data
AU - J. F. Witte
AU - A. Lissin
AU - I. Schober
AU - C. Ebeling
AU - H. Lueken
AU - J. Koblitz
AU - A. Yurkov
AU - B. Bunk
AU - J. M. Lopez-Coronado
AU - A. Vaello
AU - A. Zuzuarregui
AU - R. Aznar
AU - A. Garcia-Donate
AU - A. M. P. Melo
AU - G. Verkleij
AU - R. P. de Vries
AU - V. Robert
AU - W. Meyer
AU - J. Overmann
AU - L. C. Reimer
PY - 2026
DA - 2026/09/09
PB - bioRxiv
DO - 10.64898/2026.09.09.750348
UR - https://www.biorxiv.org/content/10.64898/2026.09.09.750348v1
AB - The vast amount of existing data on microbial strains holds immense potential to revolutionize bioindustry through the application of Artificial Intelligence (AI). However, the training of robust predictive AI models requires large-scale, unified, and non-redundant microbial datasets, which is currently severely hindered by the deep fragmentation of the data and the existence of synonymous strain identifiers in different culture collections. To overcome these infrastructural bottlenecks, we have established the StrainDiscoveryDatabase (SDD), a comprehensive, machine-readable dataset encompassing over 6.2 million harmonized data points for 256,889 microbial strains. The SDD does not rely on its own data repository, but rather on existing data that is retrieved on the fly from highly curated databases. Through an automated pipeline phenotypic, genotypic, and contextual data are systematically retrieved via the Application Programming Interfaces (APIs) of the Bacterial Diversity database (BacDive), the Microbial Resource Research Infrastructure Information System (MIRRI-IS) and the catalogue of the DSMZ. In order to reliably resolve synonymous strain identifiers, the StrainInfo database and its identification tools are employed, enabling the accurate deduplication and unification of records from disparate sources. The resulting aggregated, globally unique dataset is provided in a highly standardized JSON format in strict adherence to the FAIR data principles. By bridging isolated database silos and linking distributed knowledge to discrete biological entities, the SDD provides a high-quality, foundational resource designed to accelerate trait-based strain discovery, large-scale comparative analysis, and machine learning applications, thereby supporting the translation of the extensive existing knowledge on microbial traits to bioindustrial applications.
ER -