Halocalculus aciditolerans (KCTC 4149, JCM 19596)

Name: Halocalculus aciditolerans (KCTC 4149, JCM 19596)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 162567

Type strain

Culture col. no. KCTC 4149 JCM 19596

NCBI tax ID(s) 1383812

Special Collections JCM KCTC

Links

Halocalculus aciditolerans JCM 19596 is a mesophilic prokaryote that was isolated from Solar salt in Japan.

mesophilic genome sequence 16S sequence

Name and taxonomic classification

SI-ID 26270

KCTC 4149,JCM 19596

@ref 20215

Last LPSN update 2025-12-16 10:09:01

Domain Methanobacteriati

Phylum Methanobacteriota

Class Halobacteria

Order Halobacteriales

Family Halobacteriaceae

Genus Halocalculus

Species Halocalculus aciditolerans

Full scientific name Halocalculus aciditolerans Minegishi et al. 2015

BacDive ID	Other strains from **Halocalculus aciditolerans** (2)	Type strain
162565	H. aciditolerans JCM 19594
162566	H. aciditolerans JCM 19595

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	positive	97

Culture and growth conditions

Culture temp

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

Physiology and metabolism

Isolation, sampling and environmental information

Isolation

67770

Sample typeSolar salt in Japan

Taxonmaps

69479

Global distribution of 16S sequence AB844670 (>99% sequence identity) for Halocalculus aciditolerans subclade from Microbeatlas

Aquatic Samples	4
Soil Samples	5
Animal Samples
Plant Samples
Total Samples	9

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	ASM1464747v1 assembly for Halocalculus aciditolerans JCM 19596	contig	GCA_014647475	1383812.3	2989011748	1383812	74.26

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
67770	Halocalculus aciditolerans gene for 16S ribosomal RNA, partial sequence, strain: MH2-243-1	AB844670	1473		1383812

GC content

@ref	GC-content (mol%)	Method
67770	68.5	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: Halocalculus aciditolerans JCM 19596
NCBI: GCA_014647475

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	yes	64.80	no
125439	motility	BacteriaNetⓘ	no	83.60	no
125439	gram_stain	BacteriaNetⓘ	positive	97.00	no
125439	oxygen_tolerance	BacteriaNetⓘ	anaerobe	71.80	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Halocalculus aciditolerans strain JCM 19596
PATRIC: 1383812.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	78.69	no
125438	anaerobic	anaerobicⓘ	no	82.56	no
125438	spore-forming	spore-formingⓘ	no	86.67	no
125438	aerobic	aerobicⓘ	yes	81.20	no
125438	thermophilic	thermophileⓘ	no	84.30	no
125438	flagellated	motile2+ⓘ	no	87.00	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Halocalculus aciditolerans gen. nov., sp. nov., an acid-tolerant haloarchaeon isolated from commercial salt.	Minegishi H, Echigo A, Kuwahara A, Shimane Y, Kamekura M, Itoh T, Ohkuma M, Usami R.	Int J Syst Evol Microbiol	10.1099/ijs.0.000152	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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Halocalculus aciditolerans (KCTC 4149, JCM 19596)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Culture temp

Physiology and metabolism

Isolation, sampling and environmental information

Isolation

Taxonmaps

Interaction and safety

Sequence information

Genome sequences

Genome browser: GCA_014647475

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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