Corynebacterium kroppenstedtii BK5689_2003 is a mesophilic prokaryote that was isolated from blood culture.
mesophilic genome sequence 16S sequence| @ref 20215 |
|
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| Domain Bacillati |
| Phylum Actinomycetota |
| Class Actinomycetes |
| Order Mycobacteriales |
| Family Corynebacteriaceae |
| Genus Corynebacterium |
| Species Corynebacterium kroppenstedtii |
| Full scientific name Corynebacterium kroppenstedtii Collins et al. 1998 |
| Synonyms (2) |
| @ref | Name | Growth | Medium link | Composition | |
|---|---|---|---|---|---|
| 11987 | COLUMBIA BLOOD MEDIUM (DSMZ Medium 693) | Medium recipe at MediaDive | Name: COLUMBIA BLOOD MEDIUM (DSMZ Medium 693) Composition: Defibrinated sheep blood 50.0 g/l Columbia agar base |
| @ref | Growth | Type | Temperature (°C) | Range | |
|---|---|---|---|---|---|
| 11987 | positive | growth | 37 | mesophilic |
| Cat1 | Cat2 | Cat3 | |
|---|---|---|---|
| #Infection | #Patient | #Blood culture | |
| #Host Body Product | #Fluids | #Blood |
| @ref | Sample type | Geographic location | Country | Country ISO 3 Code | Continent | |
|---|---|---|---|---|---|---|
| 11987 | blood culture | Erlangen | Germany | DEU | Europe |
Global distribution of 16S sequence AY524775 (>99% sequence identity) for Corynebacterium kroppenstedtii subclade from Microbeatlas ![]()
| @ref | Description | Assembly level | INSDC accession | BV-BRC accession | NCBI tax ID | Score | |
|---|---|---|---|---|---|---|---|
| 124043 | ASM1688940v1 assembly for Corynebacterium kroppenstedtii FDAARGOS_1194 | complete | 161879 | 73.48 |
| @ref | Description | Accession | Length | Database | NCBI tax ID | |
|---|---|---|---|---|---|---|
| 11987 | Corynebacterium kroppenstedtii isolate BK5689_2003 16S ribosomal RNA gene, partial sequence | AY524775 | 1444 | 161879 |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125439 | spore_formation | BacteriaNetⓘ | no | 95.10 | no |
| 125439 | motility | BacteriaNetⓘ | no | 94.30 | no |
| 125439 | gram_stain | BacteriaNetⓘ | positive | 96.50 | no |
| 125439 | oxygen_tolerance | BacteriaNetⓘ | microaerophile | 91.70 | no |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125438 | gram-positive | gram-positiveⓘ | yes | 89.55 | no |
| 125438 | anaerobic | anaerobicⓘ | no | 94.08 | no |
| 125438 | spore-forming | spore-formingⓘ | no | 82.25 | no |
| 125438 | aerobic | aerobicⓘ | yes | 50.80 | no |
| 125438 | thermophilic | thermophileⓘ | no | 93.01 | yes |
| 125438 | flagellated | motile2+ⓘ | no | 91.00 | no |
| #11987 | Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 44800 |
| #20215 | Parte, A.C., Sardà Carbasse, J., Meier-Kolthoff, J.P., Reimer, L.C. and Göker, M.: List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ. IJSEM ( DOI 10.1099/ijsem.0.004332 ) |
| #69479 | João F Matias Rodrigues, Janko Tackmann,Gregor Rot, Thomas SB Schmidt, Lukas Malfertheiner, Mihai Danaila,Marija Dmitrijeva, Daniela Gaio, Nicolas Näpflin and Christian von Mering. University of Zurich.: MicrobeAtlas 1.0 beta . |
| #124043 | Isabel Schober, Julia Koblitz: Data extracted from sequence databases, automatically matched based on designation and taxonomy . |
| #125438 | Julia Koblitz, Lorenz Christian Reimer, Rüdiger Pukall, Jörg Overmann: Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets. 2024 ( DOI 10.1101/2024.08.12.607695 ) |
| #125439 | Philipp Münch, René Mreches, Martin Binder, Hüseyin Anil Gündüz, Xiao-Yin To, Alice McHardy: deepG: Deep Learning for Genome Sequence Data. R package version 0.3.1 . |
| #126262 | A. Lissin, I. Schober, J. F. Witte, H. Lüken, A. Podstawka, J. Koblitz, B. Bunk, P. Dawyndt, P. Vandamme, P. de Vos, J. Overmann, L. C. Reimer: StrainInfo—the central database for linked microbial strain identifiers. ( DOI 10.1093/database/baaf059 ) |
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https://doi.org/10.13145/bacdive3186.20251217.10
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BacDive in 2025: the core database for prokaryotic strain data