Name: Streptomyces genisteinicus CRPJ-33
Creator: BacDive
License: https://creativecommons.org/licenses/by/4.0/

Strain identifier

BacDive ID: 166980

Type strain:

Species: Streptomyces genisteinicus

Strain Designation: CRPJ-33

Culture col. no.: JCM 34526, MCCC 1K04965

NCBI tax ID(s): 2768068 (species)

Special Collections

When using BacDive for research please cite our paper

Streptomyces genisteinicus CRPJ-33 is a bacterium of the family Streptomycetaceae.

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	*Actinomycetota*
	Class	*Actinomycetes*
	Order	*Kitasatosporales*
	Family	*Streptomycetaceae*
	Genus	*Streptomyces*
	Species	**Streptomyces genisteinicus**
	Full Scientific Name (LPSN)	Streptomyces genisteinicus Hu et al. 2021

Information on morphological and physiological properties Morphology

[Ref.: #125439]	Gram stain	positive Confidence in %99.4
[Ref.: #125438]	Gram stain	positive Confidence in %90.409

Information on physiology and metabolism Physiology and metabolism

[Ref.: #125438]	Oxygen tolerance	aerobe Confidence in %90.501
[Ref.: #125439]	Oxygen tolerance	obligate aerobe Confidence in %99.1

[Ref.: #125439]

Ability of spore formation

yes Confidence in %93.1

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]	Streptomyces genisteinicus strain CRPJ-33 16S ribosomal RNA gene, partial sequence	MT509587	1531	GenBank ENA	2768068 tax ID

Genome sequence information:

	Sequence accession description	Seq. accession number	Assembly level	Sequence database	Associated NCBI tax ID
[Ref.: #66792]	Streptomyces genisteinicus CRPJ-33	GCA_014489615	complete	GenBank ENA	2768068 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: Streptomyces genisteinicus CRPJ-33 NCBI: GCA_014489615.1
[Ref.: #125438]	gram-positive	gram-positiveⓘ	yes	90.409	no
[Ref.: #125438]	anaerobic	anaerobicⓘ	no	97.894	no
[Ref.: #125438]	aerobic	aerobicⓘ	yes	90.501	no
[Ref.: #125438]	spore-forming	spore-formingⓘ	yes	89.472	no
[Ref.: #125438]	thermophile	thermophilicⓘ	no	95	no
[Ref.: #125438]	motile2+	flagellatedⓘ	no	88	no

	Model	Trait	Prediction	Confidence in %
	Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Streptomyces genisteinicus CRPJ-33 NCBI: GCA_014489615
[Ref.: #125439]	BacteriaNet	spore_formationⓘ	yes	93.1
[Ref.: #125439]	BacteriaNet	motilityⓘ	no	88.4
[Ref.: #125439]	BacteriaNet	gram_stainⓘ	positive	99.4
[Ref.: #125439]	BacteriaNet	oxygen_toleranceⓘ	obligate aerobe	99.1

Availability in culture collections External links

[Ref.: #20215]

Culture collection no.

JCM 34526, MCCC 1K04965

Literature:

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Streptomyces genisteinicus sp. nov., a novel genistein-producing actinomycete isolated from a Chinese medicinal plant and proposal of Streptomyces michiganensis Corbaz et al. 1957 as a later heterotypic synonym of Streptomyces xanthochromogenes Arishima et al. 1956.	Hu S, Li K, Wang Y, Guo Y, Zhou M, Tang X, Gao J	Int J Syst Evol Microbiol	10.1099/ijsem.0.004954	2021	*

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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