Ammoniphilus sp. (DSM 103166, CFH 90114)

Name: Ammoniphilus sp. (DSM 103166, CFH 90114)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 169486

Type strain

Culture col. no. DSM 103166 CFH 90114

NCBI tax ID(s) 2493665

Special Collections DSMZ

Links

DSM 103166 is a mesophilic prokaryote that was isolated from sediment.

mesophilic genome sequence 16S sequence

Name and taxonomic classification

SI-ID 398225

DSM 103166

@ref 20215

Last LPSN update 2025-12-16 10:10:35

Domain Bacillati

Phylum Bacillota

Class Bacilli

Order Caryophanales

Family Paenibacillaceae

Genus Ammoniphilus

Full scientific name Ammoniphilus Zaitsev et al. 1998

Morphology

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
68775	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)	Range
68775	positive	growth	30	mesophilic

Physiology and metabolism

Isolation, sampling and environmental information

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
68775	sediment	Shanxi 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
124043	ASM412319v1 assembly for Ammoniphilus sp. CFH 90114	scaffold	GCA_004123195	2493665.3	2883564391	2493665	54.23

Genome browser: GCA_004123195

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
124043	Ammoniphilus sp. strain CFH 90114 16S ribosomal RNA gene, partial sequence.	KY039333	1493		1872495

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Ammoniphilus sp. CFH 90114
PATRIC: 2493665.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	yes	87.50	no
125439	motility	BacteriaNetⓘ	yes	79.70	no
125439	gram_stain	BacteriaNetⓘ	variable	79.10	no
125439	oxygen_tolerance	BacteriaNetⓘ	facultative anaerobe	79.10	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Ammoniphilus sp. CFH 90114
PATRIC: 2493665.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	yes	51.60	no
125438	anaerobic	anaerobicⓘ	no	86.45	no
125438	spore-forming	spore-formingⓘ	yes	88.78	no
125438	aerobic	aerobicⓘ	yes	64.35	no
125438	thermophilic	thermophileⓘ	no	90.33	no
125438	flagellated	motile2+ⓘ	yes	78.37	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 )
#68775	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 103166
#124043	Isabel Schober, Julia Koblitz: Data extracted from sequence databases, automatically matched based on designation and taxonomy .
#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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