Acetobacter fallax (NCIMB 8956, LMG 1636)

Name: Acetobacter fallax (NCIMB 8956, LMG 1636)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 166754

Type strain

Culture col. no. NCIMB 8956 LMG 1636

NCBI tax ID(s) 1737473

Links

Acetobacter fallax NCIMB 8956 is a bacterium of the family Acetobacteraceae.

genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 14220

LMG 1636,NCIB 8956,NCIMB 8956

@ref 20215

Last LPSN update 2026-05-29 08:45:58

Domain Bacteria

Phylum Pseudomonadota

Class Alphaproteobacteria

Order Rhodospirillales

Family Acetobacteraceae

Genus Acetobacter

Species Acetobacter fallax

Full scientific name Acetobacter fallax Sombolestani et al. 2020

Morphology

Cell morphology

@ref	Gram stain	Confidence
125438	negative	96.328

Culture and growth conditions

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	obligate aerobe	93.972

Spore formation

@ref	Spore formation	Confidence
125438		90.863
125439		98.213

Isolation, sampling and environmental information

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	ASM1151673v1 assembly for Acetobacter fallax LMG 1636	contig	GCA_011516735	1737473.3	2905232126	1737473	43.3

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
20215	Acetobacter fallax strain LMG 1636 16S ribosomal RNA gene, partial sequence	MN744434	1476		1737473

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: ASM1151673v1 assembly for Acetobacter fallax LMG 1636
BV-BRC: 1737473.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	motility	BacteriaNetⓘ	no	76.50	no
125439	gram_stain	BacteriaNetⓘ	negative	82.31	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	93.97	no
125439	spore_formation	BacteriaNetⓘ	no	98.21	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Acetobacter sp. LMG 1636
PATRIC: 1737473.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	96.33	no
125438	anaerobic	anaerobicⓘ	no	95.06	no
125438	spore-forming	spore-formingⓘ	no	90.86	no
125438	aerobic	aerobicⓘ	yes	84.87	no
125438	thermophilic	thermophileⓘ	no	96.91	no
125438	flagellated	motile2+ⓘ	yes	55.20	no

Literature

Topic	Title	Authors	Journal	DOI	Year
	Comprehensive deciphering prophages in genus Acetobacter on the ecology, genomic features, toxin-antitoxin system, and linkage with CRISPR-Cas system.	Qian C, Ma J, Liang J, Zhang L, Liang X.	Front Microbiol	10.3389/fmicb.2022.951030	2022
Phylogeny	Acetobacter garciniae sp. nov., an acetic acid bacterium isolated from fermented mangosteen peel in Thailand.	Yukphan P, Charoenyingcharoen P, Kingcha Y, Likhitrattanapisal S, Muangham S, Tanasupawat S, Yamada Y	Int J Syst Evol Microbiol	10.1099/ijsem.0.005052	2021
Phylogeny	Novel acetic acid bacteria from cider fermentations: Acetobacter conturbans sp. nov. and Acetobacter fallax sp. nov.	Sombolestani AS, Cleenwerck I, Cnockaert M, Borremans W, Wieme AD, De Vuyst L, Vandamme P	Int J Syst Evol Microbiol	10.1099/ijsem.0.004511	2020

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) .
#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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Acetobacter fallax (NCIMB 8956, LMG 1636)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Physiology and metabolism

Oxygen tolerance

Spore formation

Isolation, sampling and environmental information

Interaction and safety

Sequence information

Genome sequences

Genome browser: GCA_011516735

16S sequences

Genome-based predictions

Predictions

Literature

Literature

References

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