Delftia acidovorans NAT is a bacterium that was isolated from forest soil.
genome sequence Bacteria| @ref 20215 |
|
|
| Domain Bacteria |
| Phylum Pseudomonadota |
| Class Betaproteobacteria |
| Order Burkholderiales |
| Family Comamonadaceae |
| Genus Delftia |
| Species Delftia acidovorans |
| Full scientific name Delftia acidovorans (den Dooren de Jong 1926) Wen et al. 1999 |
| Synonyms (2) |
| @ref | Name | Growth | Medium link | Composition | |
|---|---|---|---|---|---|
| 7140 | REACTIVATION WITH LIQUID MEDIUM 830 (DSMZ Medium 830c) | Medium recipe at MediaDive | Name: REACTIVATION WITH LIQUID MEDIUM 830 (DSMZ Medium 830c) Composition: Agar 15.0 g/l Yeast extract 0.5 g/l Proteose peptone 0.5 g/l Casamino acids 0.5 g/l Glucose 0.5 g/l Starch 0.5 g/l K2HPO4 0.3 g/l Na-pyruvate 0.3 g/l MgSO4 x 7 H2O 0.05 g/l Distilled water | ||
| 7140 | MINERAL MEDIUM PH 7.25 (DSMZ Medium 465) | Medium recipe at MediaDive | Name: MINERAL MEDIUM PH 7.25 (DSMZ Medium 465) Composition: Na2HPO4 x 2 H2O 3.5 g/l KH2PO4 1.0 g/l (NH4)2SO4 0.5 g/l MgCl2 x 6 H2O 0.1 g/l Ca(NO3)2 x 4 H2O 0.05 g/l Na2-EDTA 0.0005 g/l H3BO3 0.0003 g/l CoCl2 x 6 H2O 0.0002 g/l FeSO4 x 7 H2O 0.0002 g/l ZnSO4 x 7 H2O 0.0001 g/l Na2MoO4 x 2 H2O 3e-05 g/l MnCl2 x 4 H2O 3e-05 g/l NiCl2 x 6 H2O 2e-05 g/l CuCl2 x 2 H2O 1e-05 g/l Distilled water |
| @ref | Growth | Type | Temperature (°C) | |
|---|---|---|---|---|
| 7140 | positive | growth | 30 |
| @ref | Spore formation | Confidence | |
|---|---|---|---|
| 125439 | 97.4 |
| Cat1 | Cat2 | Cat3 | |
|---|---|---|---|
| #Environmental | #Terrestrial | #Forest | |
| #Environmental | #Terrestrial | #Soil |
| @ref | Sample type | Geographic location | Country | Country ISO 3 Code | Continent | |
|---|---|---|---|---|---|---|
| 7140 | forest soil | Konstanz | Germany | DEU | Europe |
| @ref | Description | Assembly level | INSDC accession | BV-BRC accession | NCBI tax ID | Score | |
|---|---|---|---|---|---|---|---|
| 124043 | ASM1602653v1 assembly for Delftia acidovorans FDAARGOS_939 | complete | 80866 | 95.58 |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125439 | spore_formation | BacteriaNetⓘ | no | 97.40 | no |
| 125439 | motility | BacteriaNetⓘ | yes | 87.20 | no |
| 125439 | gram_stain | BacteriaNetⓘ | negative | 97.70 | no |
| 125439 | oxygen_tolerance | BacteriaNetⓘ | aerobe | 67.30 | no |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125438 | gram-positive | gram-positiveⓘ | no | 97.00 | no |
| 125438 | anaerobic | anaerobicⓘ | no | 97.39 | no |
| 125438 | aerobic | aerobicⓘ | yes | 82.13 | no |
| 125438 | spore-forming | spore-formingⓘ | no | 86.86 | no |
| 125438 | thermophilic | thermophileⓘ | no | 96.94 | yes |
| 125438 | flagellated | motile2+ⓘ | yes | 82.06 | no |
| #7140 | Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 17854 |
| #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 ) |
| #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/bacdive2946.20251217.10
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BacDive in 2025: the core database for prokaryotic strain data