Acinetobacter sp. (CIP 110472, NIPH 817, CCUG 34786)

Name: Acinetobacter sp. (CIP 110472, NIPH 817, CCUG 34786)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 138485

Type strain

Strain Designation Gerner-Smidt 10095

Culture col. no. CIP 110472 NIPH 817 CCUG 34786 Gerner-Smidt 10095

NCBI tax ID(s) 520708

Special Collections CCUG CIP

History 2012, A. Nemec, Inst. Pasteur, Paris, France: strain NIPH 817

Gerner-Smidt 10095 is a bacterium of the family Moraxellaceae.

genome sequence Bacteria

Name and taxonomic classification

Culture Collection Numbers

CIP 110472 NIPH 817 CCUG 34786 Gerner-Smidt 10095

@ref 20215

Last LPSN update 2026-02-09 11:33:08

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	98

Culture and growth conditions

Culture medium

@ref	Name	Growth	Composition
39846	MEDIUM 72- for trypto casein soja agar		Distilled water make up to (1000.000 ml);Trypto casein soy agar (40.000 g)

Culture temp

@ref	Growth	Type	Temperature (°C)
39846	positive	growth	30
52319	negative	growth	44

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125438	aerobe	90.84
125439	obligate aerobe	94.1

Spore formation

@ref	Spore formation	Confidence
125438		93.801
125439		93

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Host	#Human	-
#Host Body-Site	#Other	#Abscess
#Infection	#Patient	-

Isolation

@ref	Sample type	Country	Country ISO 3 Code	Continent
39846		Denmark	DNK	Europe
52319	Human abscess	Denmark	DNK	Europe

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	Acin_sp_NIPH817_V1 assembly for Acinetobacter sp. NIPH 817	scaffold	GCA_000368405	520708.3	2565956670	520708	72.14

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Acinetobacter sp. NIPH 817
NCBI: GCA_000368405

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	93.00	no
125439	motility	BacteriaNetⓘ	no	52.60	no
125439	gram_stain	BacteriaNetⓘ	negative	85.60	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	94.10	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Acinetobacter sp. NIPH 817 NIPH 817
NCBI: GCA_000368405.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	98.00	no
125438	anaerobic	anaerobicⓘ	no	97.39	no
125438	aerobic	aerobicⓘ	yes	90.84	no
125438	spore-forming	spore-formingⓘ	no	93.80	no
125438	thermophilic	thermophileⓘ	no	99.98	no
125438	flagellated	motile2+ⓘ	no	74.66	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 )
#39846	Collection of Institut Pasteur ; Curators of the CIP; CIP 110472
#52319	Culture Collection University of Gothenburg (CCUG) ; Curators of the CCUG; CCUG 34786
#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 .

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