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BacDive ID 162374
Type strain
Culture col. no. JCM 19039
NCBI tax ID(s) 1460636
For citation purpose refer to the digital object identifier (doi) of the current version.

Archive

version 9.3 (current version):
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

@refdescriptionaccessionassembly leveldatabaseNCBI tax ID
66792Geomicrobium sp. JCM 190391460636.3wgspatric1460636
66792Geomicrobium sp. JCM 190392609460117draftimg1460636
67770Geomicrobium sp. JCM 19039GCA_000698145contigncbi1460636

Genome-based predictions

predictions

@refmodeltraitdescriptionpredictionconfidencetraining_data
125438gram-positivegram-positivePositive reaction to Gram-stainingyes79.603no
125438anaerobicanaerobicAbility to grow under anoxygenic conditions (including facultative anaerobes)no99.561no
125438spore-formingspore-formingAbility to form endo- or exosporesyes78.214no
125438aerobicaerobicAbility to grow under oxygenic conditions (including facultative aerobes)yes79.055no
125438thermophilethermophilicAbility to grow at temperatures above or equal to 45°Cno90.322yes
125438motile2+flagellatedAbility to perform flagellated movementyes78.501no
125439BacteriaNetspore_formationAbility to form endo- or exosporesyes95.3
125439BacteriaNetmotilityAbility to perform movementyes90.4
125439BacteriaNetgram_stainReaction to gram-stainingvariable82.8
125439BacteriaNetoxygen_toleranceOxygenic conditions needed for growthobligate aerobe96.2

External links

@ref: 67770

culture collection no.: JCM 19039

Reference

@idauthorstitledoi/url
20215Parte, 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 DSMZ10.1099/ijsem.0.004332
66792Julia Koblitz, Joaquim Sardà, Lorenz Christian Reimer, Boyke Bunk, Jörg OvermannAutomatically annotated for the DiASPora project (Digital Approaches for the Synthesis of Poorly Accessible Biodiversity Information)https://diaspora-project.de/progress.html#genomes
67770Curators of the JCMhttps://jcm.brc.riken.jp/en/
125438Julia Koblitz, Lorenz Christian Reimer, Rüdiger Pukall, Jörg OvermannPredicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets10.1101/2024.08.12.607695
125439Philipp Münch, René Mreches, Martin Binder, Hüseyin Anil Gündüz, Xiao-Yin To, Alice McHardydeepG: Deep Learning for Genome Sequence Data. R package version 0.3.1https://github.com/GenomeNet/deepG