Schinkia azotoformans (DSM 5811, NRS 1677, BPs J3)

Name: Schinkia azotoformans (DSM 5811, NRS 1677, BPs J3)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 601

Type strain

Strain Designation BP_s J₃

Culture col. no. DSM 5811 NRS 1677 BPs J3 BP_s J₃

NCBI tax ID(s) 1454

Special Collections DSMZ

History DSMZ <- NRRL; NRS 1677 <- F. Pichinoty, CNRS; BP_sJ₃

Links

Schinkia azotoformans BP_s J₃ is a bacterium of the family Bacillaceae.

genome sequence Bacteria

Name and taxonomic classification

SI-ID 48008

DSM 5811

@ref 20215

Last LPSN update 2026-05-29 08:25:04

Domain Bacteria

Phylum Bacillota

Class Bacilli

Order Caryophanales

Family Bacillaceae

Genus Schinkia

Species Schinkia azotoformans

Full scientific name Schinkia azotoformans (Pichinoty et al. 1983 ex Pichinoty et al. 1976) Gupta et al. 2020

Synonyms (2)

Bacillus azotoformans
Calidifontibacillus azotoformans

BacDive ID	Other strains from **Schinkia azotoformans** (2)	Type strain
599	S. azotoformans 1, BAI, CCUG 28517, NRRL B-14310, DSM 1046, ... (type strain)
600	S. azotoformans BPs, BP_s, BP_s CNRS, ...

Morphology

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
2322	NUTRIENT AGAR (DSMZ Medium 1)		Medium recipe at MediaDive	Name: NUTRIENT AGAR (DSMZ Medium 1; with strain-specific modifications) Composition: Agar 15.0 g/l Peptone 5.0 g/l Yeast 4.0 g/l Meat extract 3.0 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)
2322	positive	growth	30

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	facultative anaerobe	91.559

Isolation, sampling and environmental information

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
2322	1	Risk group (German classification) According to TRBA 466

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	NCBI tax ID	Score
124043	ASM5047962v1 assembly for Schinkia azotoformans NRS-1677	contig	GCA_050479625	1454	65.03

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: ASM5047962v1 assembly for Schinkia azotoformans NRS-1677
NCBI: GCA_050479625

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	facultative anaerobe	91.56	no
125439	gram_stain	BacteriaNetⓘ	variable	66.36	no
125439	motility	BacteriaNetⓘ	yes	66.52	no
125439	spore_formation	BacteriaNetⓘ	yes	78.61	no

Literature

References

#2322	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 5811
#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 )
#124043	Isabel Schober, Julia Koblitz: Data extracted from sequence databases, automatically matched based on designation and taxonomy .
#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 .
#126262	A. Lissin, I. Schober, J. F. Witte, H. Lüken, A. Podstawka, J. Koblitz, B. Bunk, P. Dawyndt, P. Vandamme, P. de Vos, J. Overmann, L. C. Reimer: StrainInfo—the central database for linked microbial strain identifiers. ( DOI 10.1093/database/baaf059 )

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