Ureibacillus acetophenoni (DSM 23394, CCUG 57911, KCTC 13605)

Name: Ureibacillus acetophenoni (DSM 23394, CCUG 57911, KCTC 13605)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 130209

Type strain

Strain Designation JC23

Culture col. no. DSM 23394 CCUG 57911 KCTC 13605 NBRC 105754 JC23

NCBI tax ID(s) 614649

Special Collections DSMZ CCUG KCTC

History <- C. Sasikala, Centre Environment, Inst. Sci. and Technol., J. N. T. Univ., Hyderabad, India; JC23 <- M. Azmatunnisa

Links

Ureibacillus acetophenoni JC23 is a bacterium that was isolated from organic solvent acetophenone.

genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 408527

KCTC 13605,NBRC 105754,CCUG 57911

@ref 20215

Last LPSN update 2026-02-09 11:31:10

Domain Bacteria

Phylum Bacillota

Class Bacilli

Order Caryophanales

Family Caryophanaceae

Genus Ureibacillus

Species Ureibacillus acetophenoni

Full scientific name Ureibacillus acetophenoni (Azmatunnisa et al. 2015) Gupta and Patel 2020

Synonyms (1)

Lysinibacillus acetophenoni

Morphology

Culture and growth conditions

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	obligate aerobe	97.5

Murein

@ref	Murein short key	Type
22049	A11.31	A4alpha L-Lys-D-Asp

Isolation, sampling and environmental information

Isolation source categories

Cat1
#Engineered

Isolation

@ref	Sample type	Sampling date	Country	Country ISO 3 Code	Continent
22049	organic solvent acetophenone		India	IND	Asia
61523	Acetophenon,organic solvent	2009-01-15	India	IND	Asia

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	IMG-taxon 2740891860 annotated assembly for Ureibacillus acetophenoni JC23	scaffold	GCA_900220965	614649.3	2740891860	614649	65.89

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
22049	Lysinibacillus acetophenoni partial 16S rRNA gene, type strain JC23T	FN179488	1472		614649

GC content

@ref	GC-content (mol%)	Method
22049	41	high performance liquid chromatography (HPLC)

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Ureibacillus acetophenoni JC23
NCBI: GCA_900220965

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	yes	58.50	no
125439	motility	BacteriaNetⓘ	yes	55.40	no
125439	gram_stain	BacteriaNetⓘ	positive	63.90	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	97.50	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Ureibacillus acetophenoni JC23
NCBI: GCA_900220965.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	yes	73.35	no
125438	anaerobic	anaerobicⓘ	no	92.55	no
125438	aerobic	aerobicⓘ	yes	81.04	no
125438	spore-forming	spore-formingⓘ	yes	88.40	no
125438	thermophilic	thermophileⓘ	no	91.86	no
125438	flagellated	motile2+ⓘ	yes	82.61	no

Literature

Title	Authors	Journal	DOI	Year
Robust Demarcation of the Family Caryophanaceae (Planococcaceae) and Its Different Genera Including Three Novel Genera Based on Phylogenomics and Highly Specific Molecular Signatures.	Gupta RS, Patel S.	Front Microbiol	10.3389/fmicb.2019.02821	2019

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 )
#22049	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 23394
#61523	Culture Collection University of Gothenburg (CCUG) ; Curators of the CCUG; CCUG 57911
#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) .
#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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Ureibacillus acetophenoni (DSM 23394, CCUG 57911, KCTC 13605)

Name and taxonomic classification

Morphology

Culture and growth conditions

Physiology and metabolism

Oxygen tolerance

Murein

Isolation, sampling and environmental information

Isolation source categories

Isolation

Interaction and safety

Sequence information

Genome sequences

Genome browser: GCA_900220965

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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