Maritimibacter lacisalsi (CGMCC 1.12922, JCM 30555)

Name: Maritimibacter lacisalsi (CGMCC 1.12922, JCM 30555)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 163818

Type strain

Culture col. no. CGMCC 1.12922 JCM 30555

NCBI tax ID(s) 1526570

Special Collections JCM

History JCM <-- Z.-P. Liu; Inst. of Microbiol., CAS, China; X12M-4.

Links

Maritimibacter lacisalsi JCM 30555 is a bacterium that was isolated from Surface water sample of a salt lake.

genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 28283

JCM 30555

@ref 20215

Last LPSN update 2026-05-29 08:45:15

Domain Bacteria

Phylum Pseudomonadota

Class Alphaproteobacteria

Order Rhodobacterales

Family Roseobacteraceae

Genus Maritimibacter

Species Maritimibacter lacisalsi

Full scientific name Maritimibacter lacisalsi Zhong et al. 2015

Synonyms (2)

Muriicola lacisalsi
Sinisalibacter lacisalsi

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	negative	95.394
125438	negative	92

Culture and growth conditions

Culture temp

@ref	Growth	Type	Temperature (°C)
67770	positive	growth	30

Physiology and metabolism

Spore formation

@ref	Spore formation	Confidence
125439		99.182

Observation

67770

Observationquinones: Q-10

Isolation, sampling and environmental information

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
67770	Surface water sample of a salt lake	Xiaochaidan Lake in Qaidam basin, Qinghai Province	China	CHN	Asia

Taxonmaps

69479

Global distribution of 16S sequence KJ782425 (>99% sequence identity) for Maritimibacter lacisalsi subclade from Microbeatlas

Aquatic Samples	69
Soil Samples	2
Animal Samples	4
Plant Samples	1
Total Samples	76

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	ASM1464011v1 assembly for Sinisalibacter lacisalsi CGMCC 1.12922	scaffold	GCA_014640115	1526570.3	2995146770	1526570	72.38

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
67770	Sinisalibacter lacisalsi strain X12M-4 16S ribosomal RNA gene, partial sequence	KJ782425	1430		1526570

GC content

@ref	GC-content (mol%)	Method
67770	65.7	thermal denaturation, midpoint method (Tm)

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: ASM1464011v1 assembly for Sinisalibacter lacisalsi CGMCC 1.12922
BV-BRC: 1526570.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	aerobe	85.87	no
125439	gram_stain	BacteriaNetⓘ	negative	95.39	no
125439	motility	BacteriaNetⓘ	yes	57.76	no
125439	spore_formation	BacteriaNetⓘ	no	99.18	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Maritimibacter lacisalsi CGMCC 1.12922
NCBI: GCA_014640115.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	92.00	no
125438	anaerobic	anaerobicⓘ	no	87.25	no
125438	aerobic	aerobicⓘ	yes	72.03	no
125438	spore-forming	spore-formingⓘ	no	82.99	no
125438	thermophilic	thermophileⓘ	no	93.31	no
125438	flagellated	motile2+ⓘ	yes	62.93	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Maritimibacter lacisalsi sp. nov., isolated from a salt lake, and emended description of the genus Maritimibacter Lee et al. 2007.	Zhong ZP, Liu Y, Zhou YG, Liu HC, Wang F, Liu ZP	Int J Syst Evol Microbiol	10.1099/ijsem.0.000437	2015

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) .
#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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Maritimibacter lacisalsi (CGMCC 1.12922, JCM 30555)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Culture temp

Physiology and metabolism

Spore formation

Observation

Isolation, sampling and environmental information

Isolation

Taxonmaps

Interaction and safety

Sequence information

Genome sequences

Genome browser: GCA_014640115

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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