Strain identifier
BacDive ID: 162374
Type strain: 
Species: Geomicrobium sp.
Strain history: T. Kudo F414.
NCBI tax ID(s): 1460636 (species)
version 9.3 (current version)
General
@ref: 67770
BacDive-ID: 162374
keywords: genome sequence, Bacteria, spore-forming, mesophilic
description: Geomicrobium sp. JCM 19039 is a spore-forming, mesophilic bacterium that was isolated from Sword-tail newt .
NCBI tax id
- NCBI tax id: 1460636
- Matching level: species
strain history
- @ref: 67770
- history: T. Kudo F414.
doi: 10.13145/bacdive162374.20250331.9.3
Name and taxonomic classification
LPSN
- @ref: 20215
- description: domain/bacteria
- keyword: phylum/bacillota
- domain: Bacteria
- phylum: Bacillota
- class: Bacilli
- order: Caryophanales
- family: Bacillaceae
- genus: Geomicrobium
- species: Geomicrobium sp.
- full scientific name: Geomicrobium Echigo et al. 2010
@ref: 67770
domain: Bacteria
phylum: Firmicutes
class: Bacilli
order: Caryophanales
family: Bacillaceae
genus: Geomicrobium
species: Geomicrobium sp.
full scientific name: Geomicrobium sp.
type strain: no
Morphology
cell morphology
- @ref: 125439
- motility: yes
- confidence: 90.4
Culture and growth conditions
culture temp
- @ref: 67770
- growth: positive
- type: growth
- temperature: 30
Physiology and metabolism
oxygen tolerance
- @ref: 125439
- oxygen tolerance: obligate aerobe
- confidence: 96.2
spore formation
- @ref: 125439
- spore formation: yes
- confidence: 95.3
Isolation, sampling and environmental information
isolation
- @ref: 67770
- sample type: Sword-tail newt (Cynops ensicauda popei)
- host species: Cynops ensicauda popei
- geographic location: main island of Okinawa
- country: Japan
- origin.country: JPN
- continent: Asia
Sequence information
Genome sequences
| @ref | description | accession | assembly level | database | NCBI tax ID |
|---|---|---|---|---|---|
| 66792 | Geomicrobium sp. JCM 19039 | 1460636.3 | wgs | patric | 1460636 |
| 66792 | Geomicrobium sp. JCM 19039 | 2609460117 | draft | img | 1460636 |
| 67770 | Geomicrobium sp. JCM 19039 | GCA_000698145 | contig | ncbi | 1460636 |
Genome-based predictions
predictions
| @ref | model | trait | description | prediction | confidence | training_data |
|---|---|---|---|---|---|---|
| 125438 | gram-positive | gram-positive | Positive reaction to Gram-staining | yes | 79.603 | no |
| 125438 | anaerobic | anaerobic | Ability to grow under anoxygenic conditions (including facultative anaerobes) | no | 99.561 | no |
| 125438 | spore-forming | spore-forming | Ability to form endo- or exospores | yes | 78.214 | no |
| 125438 | aerobic | aerobic | Ability to grow under oxygenic conditions (including facultative aerobes) | yes | 79.055 | no |
| 125438 | thermophile | thermophilic | Ability to grow at temperatures above or equal to 45°C | no | 90.322 | yes |
| 125438 | motile2+ | flagellated | Ability to perform flagellated movement | yes | 78.501 | no |
| 125439 | BacteriaNet | spore_formation | Ability to form endo- or exospores | yes | 95.3 | |
| 125439 | BacteriaNet | motility | Ability to perform movement | yes | 90.4 | |
| 125439 | BacteriaNet | gram_stain | Reaction to gram-staining | variable | 82.8 | |
| 125439 | BacteriaNet | oxygen_tolerance | Oxygenic conditions needed for growth | obligate aerobe | 96.2 |
External links
@ref: 67770
culture collection no.: JCM 19039
Reference
| @id | authors | title | doi/url |
|---|---|---|---|
| 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 | 10.1099/ijsem.0.004332 |
| 66792 | Julia Koblitz, Joaquim Sardà, Lorenz Christian Reimer, Boyke Bunk, Jörg Overmann | Automatically annotated for the DiASPora project (Digital Approaches for the Synthesis of Poorly Accessible Biodiversity Information) | https://diaspora-project.de/progress.html#genomes |
| 67770 | Curators of the JCM | https://jcm.brc.riken.jp/en/ | |
| 125438 | Julia Koblitz, Lorenz Christian Reimer, Rüdiger Pukall, Jörg Overmann | Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets | 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 | https://github.com/GenomeNet/deepG |