Name: Oryzomonas rubra Red88
Creator: BacDive
License: https://creativecommons.org/licenses/by/4.0/

Strain identifier

BacDive ID: 167139

Type strain:

Species: Oryzomonas rubra

Strain Designation: Red88

Culture col. no.: MCCC 1K03694, JCM 33033

NCBI tax ID(s): 2509454 (species)

Special Collections

Section

When using BacDive for research please cite our paper

Oryzomonas rubra Red88 is a bacterium of the family Geobacteraceae.

16S sequence
Bacteria
genome sequence

Information on the name and the taxonomic classification. Name and taxonomic classification

	Last LPSN update	09-12-2024 (DD-MM-YYYY)
	Domain	*Bacteria*
	Phylum	*Pseudomonadota*
	Class	*Deltaproteobacteria*
	Order	*Desulfuromonadales*
	Family	*Geobacteraceae*
	Genus	*Oryzomonas*
	Species	**Oryzomonas rubra**
	Full Scientific Name (LPSN)	Oryzomonas rubra corrig. Xu et al. 2020

Synonym

Oryzomonas ruber

Information on morphological and physiological properties Morphology

[Ref.: #125438]	Gram stain	negative Confidence in %96.312
[Ref.: #125439]	Gram stain	negative Confidence in %98.6

Information on physiology and metabolism Physiology and metabolism

[Ref.: #125439]

Oxygen tolerance

anaerobe Confidence in %99.2

[Ref.: #125439]

Ability of spore formation

no Confidence in %98.9

Information on isolation source, the sampling and environmental conditions Isolation, sampling and environmental information

[Ref.: #69479]

Global distribution of 16S sequence MK334372 (>99% sequence identity) for Desulfuromonadales from Microbeatlas

Aquatic Samples	61
Soil Samples	147
Animal Samples	6
Plant Samples	2
Total Samples	216

Information on genomic background e.g. entries in nucleic sequence databass Sequence information

16S Sequence information:

	Sequence accession description	Seq. accession number	Sequence length (bp)	Sequence database	Associated NCBI tax ID
[Ref.: #20215]	Oryzomonas rubra strain Red88 16S ribosomal RNA gene, partial sequence	MK334372	1428	GenBank ENA	2509454 tax ID

Genome sequence information:

	Sequence accession description	Seq. accession number	Assembly level	Sequence database	Associated NCBI tax ID
[Ref.: #66792]	Oryzomonas rubra Red88	GCA_008369015	contig	GenBank ENA	2509454 tax ID	*

Data predicted using genome information as a basis Genome-based predictions

	Model	Trait	Prediction	Confidence in %	In training data
	Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Oryzomonas rubra Red88 NCBI: GCA_008369015.1
[Ref.: #125438]	gram-positive	gram-positiveⓘ	no	96.312	no
[Ref.: #125438]	anaerobic	anaerobicⓘ	yes	66.249	no
[Ref.: #125438]	aerobic	aerobicⓘ	no	73.931	no
[Ref.: #125438]	spore-forming	spore-formingⓘ	no	81.389	no
[Ref.: #125438]	thermophile	thermophilicⓘ	no	93.95	no
[Ref.: #125438]	motile2+	flagellatedⓘ	yes	70.291	no

	Model	Trait	Prediction	Confidence in %
	Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Oryzomonas rubra Red88 NCBI: GCA_008369015
[Ref.: #125439]	BacteriaNet	spore_formationⓘ	no	98.9
[Ref.: #125439]	BacteriaNet	motilityⓘ	no	63.2
[Ref.: #125439]	BacteriaNet	gram_stainⓘ	negative	98.6
[Ref.: #125439]	BacteriaNet	oxygen_toleranceⓘ	anaerobe	99.2

Availability in culture collections External links

[Ref.: #20215]

Culture collection no.

MCCC 1K03694, JCM 33033

Literature:

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Description of Three Novel Members in the Family Geobacteraceae, Oryzomonas japonicum gen. nov., sp. nov., Oryzomonas sagensis sp. nov., and Oryzomonas ruber sp. nov.	Xu Z, Masuda Y, Hayakawa C, Ushijima N, Kawano K, Shiratori Y, Senoo K, Itoh H	Microorganisms	10.3390/microorganisms8050634	2020	*

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) .

#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 .

* These data were automatically processed and therefore are not curated

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