Name: Geomonas terrae Red111
Creator: BacDive
License: https://creativecommons.org/licenses/by/4.0/

Strain identifier

BacDive ID: 167684

Type strain:

Species: Geomonas terrae

Strain Designation: Red111

Culture col. no.: NBRC 114026, MCCC 1K04029

NCBI tax ID(s): 2562681 (species)

When using BacDive for research please cite our paper

Geomonas terrae Red111 is a Gram-negative bacterium of the family Geobacteraceae.

Gram-negative
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	*Geomonas*
	Species	**Geomonas terrae**
	Full Scientific Name (LPSN)	Geomonas terrae Xu et al. 2020

Information on morphological and physiological properties Morphology

[Ref.: #125438]	Gram stain	negative Confidence in %96.327
[Ref.: #125439]	Gram stain	negative Confidence in %98.5

Information on physiology and metabolism Physiology and metabolism

[Ref.: #125439]

Oxygen tolerance

anaerobe Confidence in %98.7

[Ref.: #125439]

Ability of spore formation

no Confidence in %98.7

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]	Geomonas terrae strain Red111 16S ribosomal RNA gene, partial sequence	MH915556	1426	GenBank ENA	2562681 tax ID

Genome sequence information:

	Sequence accession description	Seq. accession number	Assembly level	Sequence database	Associated NCBI tax ID
[Ref.: #66792]	Geomonas terrae Red111	GCA_004791675	scaffold	GenBank ENA	2562681 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: Geomonas terrae Red111 NCBI: GCA_004791675.1
[Ref.: #125438]	gram-positive	gram-positiveⓘ	no	96.327	no
[Ref.: #125438]	anaerobic	anaerobicⓘ	yes	70.089	no
[Ref.: #125438]	aerobic	aerobicⓘ	no	76.495	no
[Ref.: #125438]	spore-forming	spore-formingⓘ	no	79.363	no
[Ref.: #125438]	thermophile	thermophilicⓘ	no	91.695	no
[Ref.: #125438]	motile2+	flagellatedⓘ	yes	70.635	no

	Model	Trait	Prediction	Confidence in %
	Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Geomonas terrae Red111 NCBI: GCA_004791675
[Ref.: #125439]	BacteriaNet	spore_formationⓘ	no	98.7
[Ref.: #125439]	BacteriaNet	motilityⓘ	yes	56.1
[Ref.: #125439]	BacteriaNet	gram_stainⓘ	negative	98.5
[Ref.: #125439]	BacteriaNet	oxygen_toleranceⓘ	anaerobe	98.7

Availability in culture collections External links

[Ref.: #20215]

Culture collection no.

NBRC 114026, MCCC 1K04029

Literature:

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Geomonas oryzae gen. nov., sp. nov., Geomonas edaphica sp. nov., Geomonas ferrireducens sp. nov., Geomonas terrae sp. nov., Four Ferric-Reducing Bacteria Isolated From Paddy Soil, and Reclassification of Three Species of the Genus Geobacter as Members of the Genus Geomonas gen. nov.	Xu Z, Masuda Y, Itoh H, Ushijima N, Shiratori Y, Senoo K	Front Microbiol	10.3389/fmicb.2019.02201	2019	*
Phylogeny	Genome Analysis and Description of Three Novel Diazotrophs Geomonas Species Isolated From Paddy Soils.	Liu GH, Yang S, Tang R, Xie CJ, Zhou SG	Front Microbiol	10.3389/fmicb.2021.801462	2022	*

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) .

#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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