Cite this strain

(DSM 102557, Root434)

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

BacDive ID 132040

Type strain

Strain Designation Root434

Culture col. no. DSM 102557 Root434

NCBI tax ID(s) 1871043

Special Collections DSMZ Arabidopsis

Links

Name and taxonomic classification

Root434 is an aerobe, mesophilic, Gram-negative bacterium that was isolated from roots of Arabidopsis thaliana ecotype Shakdara .

Gram-negative aerobe mesophilic genome sequence Bacteria

SI-ID 407264

DSM 102557

Last LPSN update 2024-12-09 10:49:07

Domain Bacteria

Phylum Pseudomonadota

Class Betaproteobacteria

Order Burkholderiales

Family Comamonadaceae

Genus Variovorax

Full scientific name Variovorax Willems et al. 1991

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	negative	98.4
125438	negative	97

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
23831	YEAST EXTRACT MANITOL MEDIUM (YEM) (DSMZ Medium 1626)		Medium recipe at MediaDive	Name: YEAST EXTRACT MANITOL MEDIUM (YEM) (DSMZ Medium 1626) Composition: Agar 20.0 g/l Mannitol 5.0 g/l K2HPO4 0.5 g/l Yeast extract 0.5 g/l MgSO4 x 7 H2O 0.2 g/l NaCl 0.1 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
23831	positive	growth	28	mesophilic

Physiology and metabolism

Spore formation

@ref	Spore formation	Confidence
125439		98.1

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125438	aerobe	91.798
125439	obligate aerobe	98.2

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Host	#Plants	#Herbaceous plants (Grass,Crops)
#Host Body-Site	#Plant	#Root (Rhizome)

Isolation

@ref	Sample type	Host species	Geographic location	Country	Country ISO 3 Code	Continent	Latitude	Longitude
23831	roots of Arabidopsis thaliana ecotype Shakdara (Sha)	Arabidopsis thaliana	Potsdam, Golm district (52.416N 12.968E)	Germany	DEU	Europe	52.416	12.968 52.416/12.968

Safety information

Sequence information

Genome sequences

@ref	Description	Accession	Assembly level	Database	NCBI tax ID
66792	Variovorax sp. Root434	GCA_001426595	scaffold		1736536
66792	Variovorax sp. Root434	1736536.3	wgs	PATRIC	1736536
66792	Variovorax sp. Root434	2643221672	draft	JGI IMG/M	1736536

Genome-based predictions

Predictions

Predictions from Diaspora project based on:
Variovorax sp. Root434
NCBI: GCA_001426595

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	98.10	no
125439	motility	BacteriaNetⓘ	yes	85.80	no
125439	gram_stain	BacteriaNetⓘ	negative	98.40	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	98.20	no

Predictions from Diaspora project based on:
Variovorax sp. Root434 Root434
NCBI: GCA_001426595.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	97.00	no
125438	anaerobic	anaerobicⓘ	no	96.27	no
125438	aerobic	aerobicⓘ	yes	91.80	no
125438	spore-forming	spore-formingⓘ	no	89.14	no
125438	thermophilic	thermophileⓘ	no	98.39	yes
125438	flagellated	motile2+ⓘ	yes	54.48	no

External links

Straininfo Link

@ref	Straininfo	Culture collections
90404	407264	DSM 102557

Culture collection no.

Culture collection no.
DSM 102557
Root434

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 )
#23831	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 102557
#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) .
#90404	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-ID407264.1 )
#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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