Cite this strain

Geomicrobium sp. (JCM 19055)

Name: Geomicrobium sp. (JCM 19055)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 162388

Type strain

Culture col. no. JCM 19055

NCBI tax ID(s) 1460649

Special Collections JCM

Links

JCM 19055 is a mesophilic prokaryote that was isolated from Sea cucumber .

mesophilic genome sequence

Name and taxonomic classification

SI-ID 131710

JCM 19055

@ref 20215

Last LPSN update 2025-12-16 10:08:58

Domain Bacillati

Phylum Bacillota

Class Bacilli

Order Caryophanales

Family Bacillaceae

Genus Geomicrobium

Full scientific name Geomicrobium Echigo et al. 2010

Morphology

Culture and growth conditions

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
67770	positive	growth	30	mesophilic

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	obligate aerobe	97.2

Isolation, sampling and environmental information

Isolation

@ref	Sample type	Host species	Geographic location	Country	Country ISO 3 Code	Continent
67770	Sea cucumber (Apostichopus japonicus)	Apostichopus japonicus	Omura Bay, Nagasaki	Japan	JPN	Asia

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
67770	ASM69818v1 assembly for Geomicrobium sp. JCM 19055	contig	GCA_000698185	1460649.3	2619619137	1460649	38.82

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Geomicrobium sp. JCM 19055
NCBI: GCA_000698185

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	yes	85.00	no
125439	motility	BacteriaNetⓘ	yes	85.60	no
125439	gram_stain	BacteriaNetⓘ	variable	76.80	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	97.20	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Geomicrobium sp. JCM 19055
PATRIC: 1460649.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	yes	78.04	no
125438	anaerobic	anaerobicⓘ	no	98.90	no
125438	spore-forming	spore-formingⓘ	yes	80.07	no
125438	aerobic	aerobicⓘ	yes	80.65	no
125438	thermophilic	thermophileⓘ	no	87.52	yes
125438	flagellated	motile2+ⓘ	yes	76.69	no

Literature

References

#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 )
#67770	Japan Collection of Microorganism (JCM) ; Curators of the JCM;
#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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