Qipengyuania lutea (MCCC 1A09962, LMG 28110, CGMCC 1.12762)

Name: Qipengyuania lutea (MCCC 1A09962, LMG 28110, CGMCC 1.12762)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 162633

Type strain

Culture col. no. MCCC 1A09962 LMG 28110 CGMCC 1.12762 JCM 19853

NCBI tax ID(s) 1463599

Special Collections JCM

History JCM <-- G. Zhang O30.

Links

Qipengyuania lutea JCM 19853 is a bacterium that was isolated from Deep-sea sediment of the west Pacific.

genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 272363

LMG 28110,JCM 19853

@ref 20215

Last LPSN update 2026-05-29 08:44:58

Domain Bacteria

Phylum Pseudomonadota

Class Alphaproteobacteria

Order Sphingomonadales

Family Erythrobacteraceae

Genus Qipengyuania

Species Qipengyuania lutea

Full scientific name Qipengyuania lutea Wang et al. 2025

Synonyms (2)

Parapontixanthobacter aurantiacus
Altererythrobacter aurantiacus

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	negative	96.197
125438	negative	96.591

Culture and growth conditions

Culture temp

@ref	Growth	Type	Temperature (°C)
67770	positive	growth	37

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	obligate aerobe	94.424

Spore formation

@ref	Spore formation	Confidence
125438		91.93
125439		97.889

Observation

67770

Observationquinones: Q-10, Q-9, Q-8

Isolation, sampling and environmental information

Isolation

67770

Sample typeDeep-sea sediment of the west Pacific

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
67770	ASM982763v1 assembly for Parapontixanthobacter aurantiacus MCCC 1A09962	contig	GCA_009827635	1463599.3	2915085477	1463599	75.31

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
67770	Parapontixanthobacter aurantiacus strain O30 16S ribosomal RNA gene, partial sequence	KF924607	1451		1463599

GC content

@ref	GC-content (mol%)	Method
67770	61.2	genome sequence analysis
67770	56.9	high performance liquid chromatography (HPLC)

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: ASM982763v1 assembly for Parapontixanthobacter aurantiacus MCCC 1A09962
NCBI: GCA_009827635

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	94.42	no
125439	gram_stain	BacteriaNetⓘ	negative	96.20	no
125439	motility	BacteriaNetⓘ	no	65.96	no
125439	spore_formation	BacteriaNetⓘ	no	97.89	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Parapontixanthobacter aurantiacus MCCC 1A09962
NCBI: GCA_009827635.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	96.59	no
125438	anaerobic	anaerobicⓘ	no	96.48	no
125438	aerobic	aerobicⓘ	yes	88.21	no
125438	spore-forming	spore-formingⓘ	no	91.93	no
125438	thermophilic	thermophileⓘ	no	94.48	no
125438	flagellated	motile2+ⓘ	no	63.45	no

Literature

Topic	Title	Authors	Journal	DOI	Year
	Identification of groundwater microbes that decrease the concentration of the conservative tracer uranine.	Sugiyama A, Nakata K, Hirano SI, Hasegawa T.	Heliyon	10.1016/j.heliyon.2024.e27397	2024
Phylogeny	Actirhodobacter atriluteus gen. nov., sp. nov., isolated from the surface water of the Yellow Sea.	Xue HP, Zhang DF, Xu L, Wang XN, Zhang AH, Huang JK, Liu C	Antonie Van Leeuwenhoek	10.1007/s10482-021-01576-w	2021
Phylogeny	Altererythrobacter aurantiacus sp. nov., isolated from deep-sea sediment.	Zhang G, Yang Y, Wang L	Antonie Van Leeuwenhoek	10.1007/s10482-016-0726-1	2016

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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Qipengyuania lutea (MCCC 1A09962, LMG 28110, CGMCC 1.12762)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Culture temp

Physiology and metabolism

Oxygen tolerance

Spore formation

Observation

Isolation, sampling and environmental information

Isolation

Interaction and safety

Sequence information

Genome sequences

Genome browser: GCA_009827635

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

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