Name: Sinomicrobium weinanense FJxs
Creator: BacDive
License: https://creativecommons.org/licenses/by/4.0/

Strain identifier

BacDive ID: 170185

Type strain:

Species: Sinomicrobium weinanense

Strain Designation: FJxs

Culture col. no.: CCTCC AB 2019251, KCTC 72740

NCBI tax ID(s): 2842200 (species)

Special Collections

KCTC

When using BacDive for research please cite our paper

Sinomicrobium weinanense FJxs is a bacterium of the family Flavobacteriaceae.

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	*Bacteroidota*
	Class	*Flavobacteriia*
	Order	*Flavobacteriales*
	Family	*Flavobacteriaceae*
	Genus	*Sinomicrobium*
	Species	**Sinomicrobium weinanense**
	Full Scientific Name (LPSN)	Sinomicrobium weinanense Wu et al. 2022

Information on morphological and physiological properties Morphology

[Ref.: #125438]	Gram stain	negative Confidence in %96.083
[Ref.: #125439]	Gram stain	negative Confidence in %99.7

[Ref.: #125438]	Motility	no Confidence in %95.5

Information on physiology and metabolism Physiology and metabolism

[Ref.: #125439]

Ability of spore formation

no Confidence in %99.3

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

Genome sequence information:

	Sequence accession description	Seq. accession number	Assembly level	Sequence database	Associated NCBI tax ID
[Ref.: #66792]	Sinomicrobium weinanense FJxs	GCA_014518365	contig	GenBank ENA	2842200 tax ID	*
[Ref.: #66792]	Sinomicrobium weinanense FJxs	GCA_018863395	scaffold	GenBank ENA	2842200 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: Sinomicrobium weinanense FJxs NCBI: GCA_018863395.1
[Ref.: #125438]	gram-positive	gram-positiveⓘ	no	96.083	no
[Ref.: #125438]	anaerobic	anaerobicⓘ	no	97.186	no
[Ref.: #125438]	aerobic	aerobicⓘ	yes	87.318	no
[Ref.: #125438]	spore-forming	spore-formingⓘ	no	87.833	no
[Ref.: #125438]	thermophile	thermophilicⓘ	no	99.231	no
[Ref.: #125438]	motile2+	flagellatedⓘ	no	95.5	no

	Model	Trait	Prediction	Confidence in %
	Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Sinomicrobium weinanense FJxs NCBI: GCA_018863395
[Ref.: #125439]	BacteriaNet	spore_formationⓘ	no	99.3
[Ref.: #125439]	BacteriaNet	motilityⓘ	no	68.4
[Ref.: #125439]	BacteriaNet	gram_stainⓘ	negative	99.7
[Ref.: #125439]	BacteriaNet	oxygen_toleranceⓘ	anaerobe	88.6

Availability in culture collections External links

[Ref.: #20215]

Culture collection no.

CCTCC AB 2019251, KCTC 72740

Literature:

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Sinomicrobium weinanense sp. nov., a halophilic bacterium isolated from saline-alkali soil.	Wu P, Ren H, Zhu Q, Mei Y, Liang Y, Chen Z	Int J Syst Evol Microbiol	10.1099/ijsem.0.005282	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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