Cite this strain

(DSM 24479, LW4)

Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 158081

Type strain

Strain Designation LW4

Culture col. no. DSM 24479 LW4

NCBI tax ID(s) 427

Special Collections DSMZ

Links

Name and taxonomic classification

LW4 is a mesophilic, Gram-negative bacterium that was isolated from Sediment.

Gram-negative mesophilic genome sequence 16S sequence Bacteria

SI-ID 406919

DSM 24479

Last LPSN update 2024-12-09 10:52:02

Domain Bacteria

Phylum Pseudomonadota

Class Alphaproteobacteria

Order Hyphomicrobiales

Family Methylocystaceae

Genus Methylosinus

Full scientific name Methylosinus (ex Whittenbury et al. 1970) Bowman et al. 1993

Morphology

Cell morphology

@ref	Gram stain	Confidence
125438	negative	97.167
125439	negative	97.3

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
64655	MINERAL MEDIUM (DSMZ Medium 1007)		Medium recipe at MediaDive	Name: MINERAL MEDIUM (DSMZ Medium 1007) Composition: KNO3 0.25 g/l KH2PO4 0.1 g/l MgSO4 x 7 H2O 0.05 g/l CaCl2 x 2 H2O 0.01 g/l EDTA 0.005 g/l FeSO4 x 7 H2O 0.002 g/l CoCl2 x 6 H2O 0.0002 g/l CuCl2 x 5 H2O 0.0001 g/l ZnSO4 x 7 H2O 0.0001 g/l Na2MoO4 3e-05 g/l MnCl2 x 4 H2O 3e-05 g/l NiCl2 x 6 H2O 2e-05 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
64655	positive	growth	25	mesophilic

Physiology and metabolism

Spore formation

@ref	Spore formation	Confidence
125439		92.7

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	obligate aerobe	96.7

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Environmental	#Aquatic	#Sediment
#Environmental	#Terrestrial	#Sediment

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
64655	Sediment	Washington Lake	USA	USA	North America

Safety information

Sequence information

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
64655	Methylosinus sp. LW4 16S ribosomal RNA gene, partial sequence	AY007293	1448		136993

Genome sequences

@ref	Description	Accession	Assembly level	Database	NCBI tax ID
66792	Methylosinus sp. LW4	GCA_000379125	scaffold		136993
66792	Methylosinus sp. LW4	136993.3	wgs	PATRIC	136993
66792	Methylosinus sp. LW4	2516493024	draft	JGI IMG/M	136993

Genome-based predictions

Predictions

Predictions from Diaspora project based on:
Methylosinus sp. LW4
NCBI: GCA_000379125

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	92.70	no
125439	motility	BacteriaNetⓘ	no	55.30	no
125439	gram_stain	BacteriaNetⓘ	negative	97.30	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	96.70	no

Predictions from Diaspora project based on:
Methylosinus sp. LW4 LW4
NCBI: GCA_000379125.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	97.17	no
125438	anaerobic	anaerobicⓘ	no	87.63	no
125438	aerobic	aerobicⓘ	yes	75.01	no
125438	spore-forming	spore-formingⓘ	no	88.22	no
125438	thermophilic	thermophileⓘ	no	96.12	yes
125438	flagellated	motile2+ⓘ	yes	66.63	no

External links

Straininfo Link

@ref	Straininfo	Culture collections
110924	406919	DSM 24479

Culture collection no.

Culture collection no.
DSM 24479
LW4

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 )
#64655	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 24479
#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) .
#110924	Reimer, L.C., Lissin, A.,Schober, I., Witte,J.F., Podstawka, A., Lüken, H., Bunk, B.,Overmann, J.: StrainInfo: A central database for resolving microbial strain identifiers . ( DOI 10.60712/SI-ID406919.1 )
#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 .

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