Alcanivorax profundi (MCCC 1K03252, KCTC 52694, JCM 31866)

Name: Alcanivorax profundi (MCCC 1K03252, KCTC 52694, JCM 31866)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 164141

Type strain

Culture col. no. MCCC 1K03252 KCTC 52694 JCM 31866

NCBI tax ID(s) 2338368

Special Collections JCM KCTC

History JCM <-- M. Yu and Q. Ren; Coll. of Mar. Life Sci., Ocean Univ. of China, China; MTEO17.

Links

Alcanivorax profundi JCM 31866 is a bacterium that was isolated from Deep seawater at a depth of 1,000 m from the Mariana Trench.

genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 27118

KCTC 52694

@ref 20215

Last LPSN update 2026-05-29 08:45:20

Domain Bacteria

Phylum Pseudomonadota

Class Gammaproteobacteria

Order Oceanospirillales

Family Alcanivoracaceae

Genus Alcanivorax

Species Alcanivorax profundi

Full scientific name Alcanivorax profundi Liu et al. 2019

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	negative	92.308
125438	negative	96.5

Culture and growth conditions

Culture temp

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

Physiology and metabolism

Spore formation

@ref	Spore formation	Confidence
125438		92.978
125439		99.742

Isolation, sampling and environmental information

Isolation

@ref	Sample type	Country
67770	Deep seawater at a depth of 1,000 m from the Mariana Trench	Pacific Ocean

Taxonmaps

69479

Global distribution of 16S sequence KY352038 (>99% sequence identity) for Alcanivorax sp. from Microbeatlas

Aquatic Samples	632
Soil Samples	9
Animal Samples	33
Plant Samples	7
Total Samples	681

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
67770	ASM359712v1 assembly for Alcanivorax profundi MTEO17	contig	GCA_003597125	2338368.3	8068158055	2338368	32.84

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
67770	Alcanivorax profundi strain MTEO17 16S ribosomal RNA gene, partial sequence	KY352038	1499		2338368

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: ASM359712v1 assembly for Alcanivorax profundi MTEO17
NCBI: GCA_003597125

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	67.83	no
125439	motility	BacteriaNetⓘ	yes	76.09	no
125439	gram_stain	BacteriaNetⓘ	negative	92.31	no
125439	spore_formation	BacteriaNetⓘ	no	99.74	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Alcanivorax profundi MTEO17
NCBI: GCA_003597125.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	96.50	no
125438	anaerobic	anaerobicⓘ	no	94.97	no
125438	aerobic	aerobicⓘ	yes	81.46	no
125438	spore-forming	spore-formingⓘ	no	92.98	no
125438	thermophilic	thermophileⓘ	no	96.48	yes
125438	flagellated	motile2+ⓘ	yes	56.69	no

Literature

Topic	Title	Authors	Journal	DOI	Year
	Polyethylene Biodegradation by an Artificial Bacterial Consortium: Rhodococcus as a Competitive Plastisphere Species.	Putcha JP, Kitagawa W.	Microbes Environ	10.1264/jsme2.me24031	2024
Phylogeny	Alcanivorax profundi sp. nov., isolated from deep seawater of the Mariana Trench.	Liu J, Ren Q, Zhang Y, Li Y, Tian X, Wu Y, Tian J, Zhang XH	Int J Syst Evol Microbiol	10.1099/ijsem.0.003145	2018

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;
#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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Alcanivorax profundi (MCCC 1K03252, KCTC 52694, JCM 31866)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Culture temp

Physiology and metabolism

Spore formation

Isolation, sampling and environmental information

Isolation

Taxonmaps

Interaction and safety

Sequence information

Genome sequences

Genome browser: GCA_003597125

16S sequences

Genome-based predictions

Predictions

Literature

Literature

References

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