Algoriphagus locisalis (DSM 23445, KCTC 12310, CIP 108874)

Name: Algoriphagus locisalis (DSM 23445, KCTC 12310, CIP 108874)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 17707

Type strain

Strain Designation MSS-170

Culture col. no. DSM 23445 KCTC 12310 CIP 108874 JCM 12597 MSS-170

NCBI tax ID(s) 305507

Special Collections DSMZ JCM KCTC CIP Literature strains

Links

Algoriphagus locisalis MSS-170 is an aerobe, mesophilic, Gram-negative prokaryote that was isolated from seawater.

Gram-negative rod-shaped aerobe mesophilic genome sequence 16S sequence

Name and taxonomic classification

SI-ID 232815

KCTC 12310,JCM 12597,CIP 108874,DSM 23445

@ref 20215

Last LPSN update 2025-12-16 09:51:09

Domain Pseudomonadati

Phylum Bacteroidota

Class Cytophagia

Order Cytophagales

Family Cyclobacteriaceae

Genus Algoriphagus

Species Algoriphagus locisalis

Full scientific name Algoriphagus locisalis Yoon et al. 2005

Morphology

Cell morphology

@ref	Gram stain	Cell length	Cell width	Cell shape	Confidence
31457	negative	2.25 µm	0.55 µm	rod-shaped
67771				rod-shaped
67771	negative
125438					92.5
125438	negative				96.989
125439	negative				99.7

Pigmentation

31457

Productionyes

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
17213	BACTO MARINE BROTH (DIFCO 2216) (DSMZ Medium 514)		Medium recipe at MediaDive	Name: BACTO MARINE BROTH (DIFCO 2216) (DSMZ Medium 514) Composition: NaCl 19.45 g/l MgCl2 5.9 g/l Bacto peptone 5.0 g/l Na2SO4 3.24 g/l CaCl2 1.8 g/l Yeast extract 1.0 g/l KCl 0.55 g/l NaHCO3 0.16 g/l Fe(III) citrate 0.1 g/l KBr 0.08 g/l SrCl2 0.034 g/l H3BO3 0.022 g/l Na2HPO4 0.008 g/l Na-silicate 0.004 g/l NaF 0.0024 g/l (NH4)NO3 0.0016 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
17213	positive	growth	28	mesophilic
31457	positive	growth	04-35
31457	positive	optimum	30	mesophilic
67770	positive	growth	30	mesophilic
67771	positive	growth	30	mesophilic

Culture pH

@ref	Ability	Type	PH
31457	positive	optimum	7.5
31457	positive	growth	5.5-8

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
31457	aerobe
67771	aerobe
125439	obligate aerobe	90.6

Spore formation

@ref	Spore formation	Confidence
125439		98.4

Halophily

@ref	Salt	Growth	Tested relation	Concentration
31457	NaCl	positive	growth	0-9 %
31457	NaCl	positive	optimum	2 %

Observation

@ref	Observation
31457	aggregates in clumps
67770	quinones: MK-7
67771	quinones: MK-7

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
31457	22599 ChEBI	arabinose	+	carbon source
31457	17057 ChEBI	cellobiose	+	carbon source
31457	4853 ChEBI	esculin	+	hydrolysis
31457	28757 ChEBI	fructose	+	carbon source
31457	28260 ChEBI	galactose	+	carbon source
31457	17234 ChEBI	glucose	+	carbon source
31457	17716 ChEBI	lactose	+	carbon source
31457	17306 ChEBI	maltose	+	carbon source
31457	37684 ChEBI	mannose	+	carbon source
31457	28053 ChEBI	melibiose	+	carbon source
31457	16634 ChEBI	raffinose	+	carbon source
31457	26546 ChEBI	rhamnose	+	carbon source
31457	17814 ChEBI	salicin	+	carbon source
31457	17992 ChEBI	sucrose	+	carbon source
31457	27082 ChEBI	trehalose	+	carbon source
31457	53424 ChEBI	tween 20	+	carbon source
31457	53423 ChEBI	tween 40	+	carbon source
31457	53425 ChEBI	tween 60	+	carbon source
31457	53426 ChEBI	tween 80	+	carbon source
31457	18222 ChEBI	xylose	+	carbon source

Enzymes

@ref	Value	Activity	Ec
31457	catalase	+	1.11.1.6
31457	cytochrome oxidase	+	1.9.3.1

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Environmental	#Aquatic	#Marine

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
31457	seawater
67770	Seawater of a marine solar saltern of the Yellow Sea		Republic of Korea	KOR	Asia
67771	From sea water, marine solar saltern	the Yellow Sea	Republic of Korea	KOR	Asia

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
17213	1	Risk group (German classification) According to TRBA 466

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
67770	IMG-taxon 2639762564 annotated assembly for Algoriphagus locisalis DSM 23445	scaffold	GCA_900116615	305507.7	2639762564	305507	74.4

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
31457	Algoriphagus locisalis strain MSS-170 16S ribosomal RNA gene, partial sequence	AY835922	1477		305507

GC content

@ref	GC-content (mol%)	Method
31457	42
67770	42	high performance liquid chromatography (HPLC)
67771	42.0

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Algoriphagus locisalis DSM 23445
NCBI: GCA_900116615

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	98.40	no
125439	motility	BacteriaNetⓘ	no	65.50	no
125439	gram_stain	BacteriaNetⓘ	negative	99.70	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	90.60	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Algoriphagus locisalis strain DSM 23445
PATRIC: 305507.7

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	96.99	yes
125438	anaerobic	anaerobicⓘ	no	92.12	no
125438	spore-forming	spore-formingⓘ	no	83.44	no
125438	aerobic	aerobicⓘ	yes	83.99	yes
125438	thermophilic	thermophileⓘ	no	97.52	yes
125438	flagellated	motile2+ⓘ	no	92.50	yes

Literature

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Algoriphagus locisalis sp. nov., isolated from a marine solar saltern.	Yoon JH, Kang SJ, Oh TK	Int J Syst Evol Microbiol	10.1099/ijs.0.63605-0	2005

References

#17213	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 23445
#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 )
#31457	Barberan A, Caceres Velazquez H, Jones S, Fierer N.: Hiding in Plain Sight: Mining Bacterial Species Records for Phenotypic Trait Information. mSphere 2: 2017 ( DOI 10.1128/mSphere.00237-17 , PubMed 28776041 ) - originally annotated from #27759 (see below)
#67770	Japan Collection of Microorganism (JCM) ; Curators of the JCM;
#67771	Korean Collection for Type Cultures (KCTC) ; Curators of the KCTC;
#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 )

Rate our new design

Content

About us

Algoriphagus locisalis (DSM 23445, KCTC 12310, CIP 108874)

Name and taxonomic classification

Morphology

Cell morphology

Pigmentation

Culture and growth conditions

Culture medium

Culture temp

Culture pH

Physiology and metabolism

Oxygen tolerance

Spore formation

Halophily

Observation

Metabolite utilization

Enzymes

Isolation, sampling and environmental information

Isolation source categories

Isolation

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_900116615

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

Help

Social Media