Strain identifier

BacDive ID: 3793

Type strain: Yes

Species: Thermoflexibacter ruber

Strain Designation: GEY

Strain history: <- ATCC <- R. A. Lewin, GEY

NCBI tax ID(s): 1003 (species)

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General

@ref: 3637

BacDive-ID: 3793

DSM-Number: 9560

keywords: genome sequence, 16S sequence, Bacteria, mesophilic, Gram-negative

description: Thermoflexibacter ruber GEY is a mesophilic, Gram-negative bacterium that was isolated from hot spring.

NCBI tax id

  • NCBI tax id: 1003
  • Matching level: species

strain history

  • @ref: 3637
  • history: <- ATCC <- R. A. Lewin, GEY

doi: 10.13145/bacdive3793.20250331.9.3

Name and taxonomic classification

LPSN

  • @ref: 20215
  • description: domain/bacteria
  • keyword: phylum/bacteroidota
  • domain: Bacteria
  • phylum: Bacteroidota
  • class: Cytophagia
  • order: Cytophagales
  • family: Thermoflexibacteraceae
  • genus: Thermoflexibacter
  • species: Thermoflexibacter ruber
  • full scientific name: Thermoflexibacter ruber (Lewin 1969) Hahnke et al. 2017
  • synonyms

    • @ref: 20215
    • synonym: Flexibacter ruber

@ref: 3637

domain: Bacteria

phylum: Bacteroidetes

class: Cytophagia

order: Cytophagales

family: Thermoflexibacteraceae

genus: Thermoflexibacter

species: Thermoflexibacter ruber

full scientific name: Thermoflexibacter ruber (Lewin 1969) Hahnke et al. 2017

strain designation: GEY

type strain: yes

Morphology

cell morphology

@refgram stainconfidence
125439negative99.8
125438negative92.603

Culture and growth conditions

culture medium

  • @ref: 3637
  • name: FLEXIBACTER CANADENSIS MEDIUM (DSMZ Medium 357)
  • growth: yes
  • link: https://mediadive.dsmz.de/medium/357
  • composition: Name: FLEXIBACTER CANADENSIS MEDIUM (DSMZ Medium 357) Composition: Agar 10.0 g/l Tris 1.0 g/l Glucose 1.0 g/l Casamino acids 1.0 g/l Sodium glycerophosphate 0.1 g/l CaCl2 x 2 H2O 0.1 g/l KNO3 0.1 g/l MgSO4 x 7 H2O 0.1 g/l HCl 0.0025 g/l FeCl2 x 4 H2O 0.0015 g/l Thiamine-HCl x 2 H2O 0.001 g/l CoCl2 x 6 H2O 0.00019 g/l MnCl2 x 4 H2O 0.0001 g/l ZnCl2 7e-05 g/l Na2MoO4 x 2 H2O 3.6e-05 g/l NiCl2 x 6 H2O 2.4e-05 g/l H3BO3 6e-06 g/l CuCl2 x 2 H2O 2e-06 g/l Vitamin B12 1e-06 g/l Distilled water

culture temp

  • @ref: 3637
  • growth: positive
  • type: growth
  • temperature: 30

Physiology and metabolism

oxygen tolerance

  • @ref: 125439
  • oxygen tolerance: obligate aerobe
  • confidence: 98

spore formation

  • @ref: 125439
  • spore formation: no
  • confidence: 99.2

Isolation, sampling and environmental information

isolation

  • @ref: 3637
  • sample type: hot spring
  • geographic location: Geysir
  • country: Iceland
  • origin.country: ISL
  • continent: Europe

isolation source categories

Cat1Cat2Cat3
#Environmental#Aquatic#Thermal spring
#Condition#Thermophilic (>45°C)

Safety information

risk assessment

  • @ref: 3637
  • biosafety level: 1
  • biosafety level comment: Risk group (German classification)

Sequence information

16S sequences

@refdescriptionaccessionlengthdatabaseNCBI tax ID
20218Flexibacter ruber 16S ribosomal RNAM587881462nuccore1003
20218Flexibacter ruber gene for 16S rRNA, strain:IFO 16675AB0780641461nuccore1003
20218Flexibacter ruber gene for 16S rRNA, partial sequence, strain: NBRC 16677AB6811041437nuccore1003
3637Flexibacter ruber gene for 16S rRNA, strain:IFO 16677AB0780651467nuccore1003

Genome sequences

@refdescriptionaccessionassembly leveldatabaseNCBI tax ID
66792Thermoflexibacter ruber GEY, DSM 9560GCA_900113045scaffoldncbi1003
66792Thermoflexibacter ruber strain GEY, DSM 95601003.8wgspatric1003
66792Thermoflexibacter ruber DSM 95602636415974draftimg1003

Genome-based predictions

predictions

@refmodeltraitdescriptionpredictionconfidencetraining_data
125439BacteriaNetoxygen_toleranceOxygenic conditions needed for growthobligate aerobe98
125439BacteriaNetgram_stainReaction to gram-stainingnegative99.8
125439BacteriaNetmotilityAbility to perform movementno82.1
125439BacteriaNetspore_formationAbility to form endo- or exosporesno99.2
125438gram-positivegram-positivePositive reaction to Gram-stainingno92.603no
125438anaerobicanaerobicAbility to grow under anoxygenic conditions (including facultative anaerobes)no94.42no
125438aerobicaerobicAbility to grow under oxygenic conditions (including facultative aerobes)yes80.81no
125438spore-formingspore-formingAbility to form endo- or exosporesno88.072no
125438thermophilethermophilicAbility to grow at temperatures above or equal to 45°Cno91.567yes
125438motile2+flagellatedAbility to perform flagellated movementno82no

External links

@ref: 3637

culture collection no.: DSM 9560, ATCC 23103

straininfo link

  • @ref: 73305
  • straininfo: 2843

Reference

@idauthorscataloguedoi/urltitlejournalpubmed
3637Curators of the DSMZLeibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH (DSM 9560)https://www.dsmz.de/collection/catalogue/details/culture/DSM-9560
20215Parte, A.C., Sardà Carbasse, J., Meier-Kolthoff, J.P., Reimer, L.C. and Göker, M.10.1099/ijsem.0.004332List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ
20218Verslyppe, B., De Smet, W., De Baets, B., De Vos, P., Dawyndt P.10.1016/j.syapm.2013.11.002StrainInfo introduces electronic passports for microorganisms.Syst Appl Microbiol. 37: 42-50 201424321274
66792Julia Koblitz, Joaquim Sardà, Lorenz Christian Reimer, Boyke Bunk, Jörg Overmannhttps://diaspora-project.de/progress.html#genomesAutomatically annotated for the DiASPora project (Digital Approaches for the Synthesis of Poorly Accessible Biodiversity Information)
66794Antje Chang, Lisa Jeske, Sandra Ulbrich, Julia Hofmann, Julia Koblitz, Ida Schomburg, Meina Neumann-Schaal, Dieter Jahn, Dietmar Schomburg10.1093/nar/gkaa1025BRENDA, the ELIXIR core data resource in 2021: new developments and updatesNucleic Acids Res. 49: D498-D508 202033211880
73305Reimer, L.C., Lissin, A.,Schober, I., Witte,J.F., Podstawka, A., Lüken, H., Bunk, B.,Overmann, J.10.60712/SI-ID2843.1StrainInfo: A central database for resolving microbial strain identifiers
125438Julia Koblitz, Lorenz Christian Reimer, Rüdiger Pukall, Jörg Overmann10.1101/2024.08.12.607695Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets
125439Philipp Münch, René Mreches, Martin Binder, Hüseyin Anil Gündüz, Xiao-Yin To, Alice McHardyhttps://github.com/GenomeNet/deepGdeepG: Deep Learning for Genome Sequence Data. R package version 0.3.1