Arcobacter venerupis (LMG 26156, CECT 7836, F67-11)

Name: Arcobacter venerupis (LMG 26156, CECT 7836, F67-11)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 168554

Type strain

Strain Designation F67-11

Culture col. no. LMG 26156 CECT 7836 F67-11

NCBI tax ID(s) 1054033

Links

Arcobacter venerupis F67-11 is a bacterium of the family Arcobacteraceae.

genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 371537

LMG 26156,LMG 25156,CECT 7836

@ref 20215

Last LPSN update 2026-05-29 08:46:24

Domain Bacteria

Phylum Pseudomonadota

Class Epsilonproteobacteria

Order Campylobacterales

Family Arcobacteraceae

Genus Arcobacter

Species Arcobacter venerupis

Full scientific name Arcobacter venerupis Levican et al. 2012

Synonyms (2)

Pseudarcobacter venerupis
Pseudoarcobacter venerupis

Morphology

Cell morphology

@ref	Gram stain	Confidence
125438	negative	98.427
125439	negative	95.57

Culture and growth conditions

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	obligate aerobe	96.352

Spore formation

@ref	Spore formation	Confidence
125439		98.025

Isolation, sampling and environmental information

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
20215	1	Risk group

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	ASM1320166v1 assembly for Arcobacter venerupis LMG 26156	complete	GCA_013201665	1054033.6		1054033	90.72
66792	ASM402340v1 assembly for Arcobacter venerupis CECT7836	contig	GCA_004023405	1054033.5	2899730368	1054033	24.55

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
20215	Arcobacter venerupis strain CECT7836T 16S ribosomal RNA gene, partial sequence	MG195902	1506		1054033
124043	Arcobacter venerupis partial 16S rRNA gene, type strain F67-11T	HE565359	1401		1054033

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: ASM1320166v1 assembly for Arcobacter venerupis LMG 26156
BV-BRC: 1054033.6

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	motility	BacteriaNetⓘ	yes	42.74	no
125439	gram_stain	BacteriaNetⓘ	negative	95.57	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	96.35	no
125439	spore_formation	BacteriaNetⓘ	no	98.03	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Arcobacter venerupis LMG 26156
NCBI: GCA_013201665.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	98.43	no
125438	anaerobic	anaerobicⓘ	no	53.60	no
125438	aerobic	aerobicⓘ	no	57.50	no
125438	spore-forming	spore-formingⓘ	no	88.60	no
125438	thermophilic	thermophileⓘ	no	89.41	no
125438	flagellated	motile2+ⓘ	yes	79.92	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 )
#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) .
#124043	Isabel Schober, Julia Koblitz: Data extracted from sequence databases, automatically matched based on designation and taxonomy .
#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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