Acinetobacter sp. (CGMCC 1.12996, JCM 30594)

Name: Acinetobacter sp. (CGMCC 1.12996, JCM 30594)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 163831

Type strain

Culture col. no. CGMCC 1.12996 JCM 30594

NCBI tax ID(s) 472

Special Collections JCM

History G. Zhang; Cold & Arid Reg. Environ. & Eng. Res. Inst., CAS, China; TTH0-4.

Links

JCM 30594 is a bacterium that was isolated from Permafrost region in Tibet Plateau.

genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 131381

JCM 30594

@ref 20215

Last LPSN update 2026-02-09 11:39:42

Domain Bacteria

Phylum Pseudomonadota

Class Gammaproteobacteria

Order Pseudomonadales

Family Moraxellaceae

Genus Acinetobacter

Full scientific name Acinetobacter Brisou and Prévot 1954 (Approved Lists 1980)

Morphology

Cell morphology

@ref	Gram stain	Confidence
125438	negative	99.488
125439	negative	98.4

Culture and growth conditions

Culture temp

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

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	obligate aerobe	99

Spore formation

@ref	Spore formation	Confidence
125439		96.8
125438		94.466

Isolation, sampling and environmental information

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
67770	Permafrost region in Tibet Plateau	Qinghai Province	China	CHN	Asia

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
67770	ASM127871v1 assembly for Acinetobacter sp. TTH0-4	complete	GCA_001278715	1646498.3	2645727723	1646498	99.41

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
67770	Acinetobacter haemolyticus strain TTH0-4 16S ribosomal RNA gene, partial sequence	KF704077	1359		29430

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Acinetobacter sp. TTH0-4
PATRIC: 1646498.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	96.80	no
125439	motility	BacteriaNetⓘ	yes	58.10	no
125439	gram_stain	BacteriaNetⓘ	negative	98.40	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	99.00	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Acinetobacter sp. TTH0-4
PATRIC: 1646498.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	99.49	no
125438	anaerobic	anaerobicⓘ	no	96.59	no
125438	spore-forming	spore-formingⓘ	no	94.47	no
125438	aerobic	aerobicⓘ	yes	85.41	no
125438	thermophilic	thermophileⓘ	no	97.98	no
125438	flagellated	motile2+ⓘ	no	80.71	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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