Paenibacillus sp. (DSM 101737, FJAT-27812)

Name: Paenibacillus sp. (DSM 101737, FJAT-27812)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 169427

Type strain

Strain Designation FJAT-27812

Culture col. no. DSM 101737 FJAT-27812

NCBI tax ID(s) 58172

Special Collections DSMZ

History <- G. Liu, Fujian Academy of Agricultural Sciences, Fuzhou, China; FJAT-27812

Links

FJAT-27812 is a bacterium that was isolated from soil.

genome sequence Bacteria

Name and taxonomic classification

SI-ID 399623

DSM 101737

@ref 20215

Last LPSN update 2026-02-09 11:41:02

Domain Bacteria

Phylum Bacillota

Class Bacilli

Order Caryophanales

Family Paenibacillaceae

Genus Paenibacillus

Full scientific name Paenibacillus Ash et al. 1994

Synonyms (1)

"Aerobacillus"

Morphology

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
68708	CASO AGAR (MERCK 105458) (DSMZ Medium 220)		Medium recipe at MediaDive	Name: CASO AGAR (MERCK 105458) (DSMZ Medium 220) Composition: Agar 15.0 g/l Casein peptone 15.0 g/l NaCl 5.0 g/l Soy peptone 5.0 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)
68708	positive	growth	30

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	facultative anaerobe	98.4

Spore formation

@ref	Spore formation	Confidence
125438		92.513

Isolation, sampling and environmental information

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
68708	soil	Yunnan province	China	CHN	Asia

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	ASM127390v1 assembly for Paenibacillus sp. FJAT-27812	scaffold	GCA_001273905	1684143.3	2645727630	1684143	68.93

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Paenibacillus sp. FJAT-27812
NCBI: GCA_001273905

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	yes	62.90	no
125439	motility	BacteriaNetⓘ	yes	64.60	no
125439	gram_stain	BacteriaNetⓘ	positive	67.10	no
125439	oxygen_tolerance	BacteriaNetⓘ	facultative anaerobe	98.40	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Paenibacillus sp. FJAT-27812 FJAT-27812
NCBI: GCA_001273905.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	yes	60.11	no
125438	anaerobic	anaerobicⓘ	no	96.87	no
125438	aerobic	aerobicⓘ	yes	70.59	no
125438	spore-forming	spore-formingⓘ	yes	92.51	no
125438	thermophilic	thermophileⓘ	no	97.26	yes
125438	flagellated	motile2+ⓘ	yes	85.33	no

Literature

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) .
#68708	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 101737
#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 .
#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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