Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jan 7;50(D1):D741-D746.
doi: 10.1093/nar/gkab961.

BacDive in 2022: the knowledge base for standardized bacterial and archaeal data

Lorenz Christian Reimer  1 , Joaquim Sardà Carbasse  1 , Julia Koblitz  1 , Christian Ebeling  1 , Adam Podstawka  1 , Jörg Overmann  1
Affiliations

BacDive in 2022: the knowledge base for standardized bacterial and archaeal data

Lorenz Christian Reimer et al. Nucleic Acids Res. .

Abstract

The bacterial metadatabase BacDive (https://bacdive.dsmz.de) has developed into a leading database for standardized prokaryotic data on strain level. With its current release (07/2021) the database offers information for 82 892 bacterial and archaeal strains covering taxonomy, morphology, cultivation, metabolism, origin, and sequence information within 1048 data fields. By integrating high-quality data from additional culture collections as well as detailed information from species descriptions, the amount of data provided has increased by 30% over the past three years. A newly developed query builder tool in the advanced search now allows complex database queries. Thereby bacterial strains can be systematically searched based on combinations of their attributes, e.g. growth and metabolic features for biotechnological applications or to identify gaps in the present knowledge about bacteria. A new interactive dashboard provides a statistic overview over the most important data fields. Additional new features are improved genomic sequence data, integrated NCBI TaxIDs and links to BacMedia, the new sister database on cultivation media. To improve the findability and interpretation of data through search engines, data in BacDive are annotated with bioschemas.org terms.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
Updated features in the strain detail view. (A) Infobox comprising main strain identifier, including BacDive ID, culture collection numbers and NCBI taxonomy ID. (B) Short summary of the most important information on the strain. (C) Species navigation bar, to switch between strains that belong to the same species. (D, E) Gallery and detail view for multimedia content, e.g. electron microscopy images. (F) Metabolic pathway information integrated from EnzymeDetector, displayed in a list sorted descending by the coverage of detected enzymes. (G) Table with genomic sequence data retrieved from NCBI GenBank, PATRIC and JGI.
Figure 2.
Figure 2.
Selected statistics from the dashboard (https://bacdive.dsmz.de/dashboard) providing an overview over BacDive data within 49 graphs. (A) Eight key values covering total strains and species, cell size, optimum cultivation temperature, pH, incubation time, optimum salt condition, and count of 16S sequence accession numbers. (B) Cultivation data showing distribution of growth temperatures for 39 701 strains. (C) Morphology data showing distribution of cell length for 5217 strains. (D) Metabolism data showing a ranking of 706 utilized metabolites ordered descending by strain count. (E) Location data showing the number of strains that were isolated in each country.
See this image and copyright information in PMC

References

    1. Overmann J., Abt B., Sikorski J.. Present and future of culturing bacteria. Annu. Rev. Microbiol. 2017; 71:711–730. - PubMed
    1. Brinker T.J., Hekler A., Enk A.H., Klode J., Hausschild A., Berking C., Schilling B., Haferkamp S., Schadendorf D., Holland-Letz T.et al. .. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. Eur. J. Cancer. 2019; 113:47–54. - PubMed
    1. Spiesman B.J., Gratton C., Hatfield R.G., Hsu W.H., Jepsen S., McCornack B., Patel K., Wang G.. Assessing the potential for deep learning and computer vision to identify bumble bee species from images. Sci. Rep. 2021; 11:7580. - PMC - PubMed
    1. Li G., Rabe K.S., Nielsen J., Engqvist M.K.M.. Machine learning applied to predicting microorganism growth temperatures and enzyme catalytic optima. ACS Synth. Biol. 2019; 8:1411–1420. - PubMed
    1. Feldbauer R., Schulz F., Horn M., Rattei T.. Prediction of microbial phenotypes based on comparative genomics. BMC Bioinformatics. 2015; 16:S1. - PMC - PubMed

Publication types