Thiomonas metallidurans (DSM 25617, FB-cd)

Name: Thiomonas metallidurans (DSM 25617, FB-cd)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 139871

Type strain

Strain Designation FB-cd

Culture col. no. DSM 25617 FB-cd

Special Collections DSMZ

Thiomonas metallidurans FB-cd is a mesophilic Proteobacterium that was isolated from creek sediment.

mesophilic genome sequence 16S sequence

Name and taxonomic classification

Culture Collection Numbers

DSM 25617 FB-cd

@ref 42479

Domain Bacteria

Phylum Proteobacteria

Class Betaproteobacteria

Order Burkholderiales

Family Comamonadaceae

Genus Thiomonas

Species Thiomonas metallidurans

Full scientific name Thiomonas metallidurans

Strain designation FB-cd

Type strain no

Morphology

Cell morphology

@ref	Gram stain	Confidence
125438	negative	95.716
125439	negative	93.1

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
42479	R2A MEDIUM (MICROAEROBIC) (DSMZ Medium 830d)		Medium recipe at MediaDive	Name: R2A MEDIUM (MICROAEROBIC) (DSMZ Medium 830d) Composition: Starch 0.5 g/l Glucose 0.5 g/l Casamino acids 0.5 g/l Proteose peptone 0.5 g/l Yeast extract 0.5 g/l Na-pyruvate 0.3 g/l K2HPO4 0.3 g/l MgSO4 x 7 H2O 0.05 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
42479	positive	growth	25	mesophilic

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	obligate aerobe	92.5

Spore formation

@ref	Spore formation	Confidence
125438		90.421
125439		94.9

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Environmental	#Aquatic	#River (Creek)
#Environmental	#Aquatic	#Sediment

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
42479	creek sediment	Thuringia, near Ronneburg, former uranium mining area	Germany	DEU	Europe

Taxonmaps

69479

Global distribution of 16S sequence JN885795 (>99% sequence identity) for Thiomonas from Microbeatlas

Aquatic Samples	594
Soil Samples	151
Animal Samples	98
Plant Samples	10
Total Samples	853

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	ASM73377v1 assembly for Thiomonas sp. FB-Cd	contig	GCA_000733775	1158292.3	2574179766	1158292	72.76

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
42479	Thiomonas sp. FB-Cd 16S ribosomal RNA gene, partial sequence	JN885795	1394		1158292

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Thiomonas sp. FB-Cd
PATRIC: 1158292.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	92.50	no
125439	gram_stain	BacteriaNetⓘ	negative	93.10	no
125439	motility	BacteriaNetⓘ	yes	52.20	no
125439	spore_formation	BacteriaNetⓘ	no	94.90	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Thiomonas sp. FB-Cd
PATRIC: 1158292.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	95.72	no
125438	anaerobic	anaerobicⓘ	no	89.17	no
125438	spore-forming	spore-formingⓘ	no	90.42	no
125438	aerobic	aerobicⓘ	yes	69.55	no
125438	thermophilic	thermophileⓘ	no	94.69	yes
125438	flagellated	motile2+ⓘ	yes	64.08	no

Literature

References

#42479	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 25617
#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 .

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