Aurantiacibacter xanthus (SM1501, KCTC 42669, CCTCC AB 2015396)

Name: Aurantiacibacter xanthus (SM1501, KCTC 42669, CCTCC AB 2015396)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 166828

Type strain

Strain Designation SM1501

Culture col. no. SM1501 KCTC 42669 CCTCC AB 2015396

NCBI tax ID(s) 1784712

Special Collections KCTC

Links

Aurantiacibacter xanthus SM1501 is a bacterium of the family Erythrobacteraceae.

genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 27978

KCTC 42669

@ref 20215

Last LPSN update 2026-02-09 11:40:25

Domain Bacteria

Phylum Pseudomonadota

Class Alphaproteobacteria

Order Sphingomonadales

Family Erythrobacteraceae

Genus Aurantiacibacter

Species Aurantiacibacter xanthus

Full scientific name Aurantiacibacter xanthus (Li et al. 2017) Xu et al. 2020

Synonyms (1)

Erythrobacter xanthus

Morphology

Cell morphology

@ref	Gram stain	Confidence
125438	negative	96.828
125439	negative	98.7

Culture and growth conditions

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	aerobe	94

Spore formation

@ref	Spore formation	Confidence
125439		98
125438		90.574

Isolation, sampling and environmental information

Taxonmaps

69479

Global distribution of 16S sequence KU529278 (>99% sequence identity) for Aurantiacibacter xanthus subclade from Microbeatlas

Aquatic Samples	115
Soil Samples	13
Animal Samples	33
Plant Samples	5
Total Samples	166

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	ASM358401v1 assembly for Aurantiacibacter xanthus CCTCC AB 2015396	scaffold	GCA_003584015	1784712.3	2839072118	1784712	38.17

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
20215	Aurantiacibacter xanthus strain SM1501 16S ribosomal RNA gene, partial sequence	KU529278	1448		1784712

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Aurantiacibacter xanthus CCTCC AB 2015396
NCBI: GCA_003584015

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	98.00	no
125439	motility	BacteriaNetⓘ	yes	68.60	no
125439	gram_stain	BacteriaNetⓘ	negative	98.70	no
125439	oxygen_tolerance	BacteriaNetⓘ	aerobe	94.00	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Aurantiacibacter xanthus CCTCC AB 2015396
NCBI: GCA_003584015.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	96.83	no
125438	anaerobic	anaerobicⓘ	no	96.10	no
125438	aerobic	aerobicⓘ	yes	83.58	no
125438	spore-forming	spore-formingⓘ	no	90.57	no
125438	thermophilic	thermophileⓘ	no	96.91	no
125438	flagellated	motile2+ⓘ	yes	65.10	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Erythrobacter suaedae sp. nov., isolated from a rhizosphere mudflat of a halophyte (Suaeda japonica).	Lee SD, Kim YJ, Kim IS	Int J Syst Evol Microbiol	10.1099/ijsem.0.003625	2019
Phylogeny	Erythrobacter xanthus sp. nov., isolated from surface seawater of the South China Sea.	Li DD, Zhang YQ, Peng M, Wang N, Wang XJ, Zhang XY, Li PY, Xie BB, Chen XL, Zhang YZ, Qin QL	Int J Syst Evol Microbiol	10.1099/ijsem.0.001991	2017

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) .
#69479	João F Matias Rodrigues, Janko Tackmann,Gregor Rot, Thomas SB Schmidt, Lukas Malfertheiner, Mihai Danaila,Marija Dmitrijeva, Daniela Gaio, Nicolas Näpflin and Christian von Mering. University of Zurich.: MicrobeAtlas 1.0 beta .
#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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Aurantiacibacter xanthus (SM1501, KCTC 42669, CCTCC AB 2015396)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Physiology and metabolism

Oxygen tolerance

Spore formation

Isolation, sampling and environmental information

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_003584015

16S sequences

Genome-based predictions

Predictions

Literature

Literature

References

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