Microbacterium sp. (DSM 102462, Root322)

Name: Microbacterium sp. (DSM 102462, Root322)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 131797

Type strain

Strain Designation Root322

Culture col. no. DSM 102462 Root322

NCBI tax ID(s) 51671

Special Collections DSMZ Arabidopsis

History DSMZ <- P. Schulze-Lefert, Max Planck Institute for Plant Breeding Research, Cologne, Germany; Root322 <- Y. Bai

Links

Root322 is a bacterium that was isolated from roots of Arabidopsis thaliana ecotype Shakdara .

genome sequence Bacteria

Name and taxonomic classification

SI-ID 406987

DSM 102462

@ref 20215

Last LPSN update 2026-05-29 08:37:42

Domain Bacteria

Phylum Actinomycetota

Class Actinomycetes

Order Micrococcales

Family Microbacteriaceae

Genus Microbacterium

Full scientific name Microbacterium Orla-Jensen 1919 (Approved Lists 1980)

Synonyms (2)

Aureobacterium
Paramicrobacterium

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	positive	94.43

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
23592	PP-MEDIUM (DSMZ Medium 513)		Medium recipe at MediaDive	Name: PP-MEDIUM (DSMZ Medium 513) Composition: Agar 15.0 g/l Polypeptone 10.0 g/l Yeast extract 2.0 g/l MgSO4 x 7 H2O 1.0 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)
23592	positive	growth	28

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	obligate aerobe	99.214

Spore formation

@ref	Spore formation	Confidence
125439		92.837

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
23592	roots of Arabidopsis thaliana ecotype Shakdara (Sha)	Arabidopsis thaliana	Cologne (50.958N 6.856E)	Germany	DEU	Europe	50.958	6.856 50.958/6.856

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	Root322 assembly for Microbacterium sp. Root322	scaffold	GCA_001425645	1736514.3	2643221630	1736514	78.36

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Root322 assembly for Microbacterium sp. Root322
BV-BRC: 1736514.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	99.21	no
125439	gram_stain	BacteriaNetⓘ	positive	94.43	no
125439	motility	BacteriaNetⓘ	no	75.62	no
125439	spore_formation	BacteriaNetⓘ	no	92.84	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Microbacterium sp. Root322 Root322
NCBI: GCA_001425645.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	yes	89.54	no
125438	anaerobic	anaerobicⓘ	no	97.65	no
125438	aerobic	aerobicⓘ	yes	87.02	no
125438	spore-forming	spore-formingⓘ	no	76.06	no
125438	thermophilic	thermophileⓘ	no	95.95	yes
125438	flagellated	motile2+ⓘ	no	76.50	no

Literature

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 )
#23592	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 102462
#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) .
#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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