Bacteroides sp. (DSM 24820, BEI HM-23, HM-23)

Name: Bacteroides sp. (DSM 24820, BEI HM-23, HM-23)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 169201

Type strain

Strain Designation HM-23, 1/1/6

Culture col. no. DSM 24820 BEI HM-23 HM-23 1/1/6

NCBI tax ID(s) 29523

Special Collections DSMZ

HM-23 is a prokaryote that was isolated from human gut biopsy, clinical sample.

genome sequence

Name and taxonomic classification

Culture Collection Numbers

DSM 24820 BEI HM-23 HM-23 1/1/6

@ref 20215

Last LPSN update 2025-12-16 10:10:31

Domain Pseudomonadati

Phylum Bacteroidota

Class Bacteroidia

Order Bacteroidales

Family Bacteroidaceae

Genus Bacteroides

Full scientific name Bacteroides Castellani and Chalmers 1919 (Approved Lists 1980)

Synonyms (1)

Capsularis

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	negative	98.4
125438	negative	94.29

Culture and growth conditions

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	anaerobe	92.6

Spore formation

@ref	Spore formation	Confidence
125438		90.442
125439		99.6

Isolation, sampling and environmental information

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent	Latitude	Longitude
68438	human gut biopsy, clinical sample	Alberta	Canada	CAN	North America	51.0486	-114.071 51.0486/-114.071

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	Bact_sp_1_1_6_V2 assembly for Bacteroides thetaiotaomicron 1_1_6	scaffold	GCA_000159875	469586.3	646206272	818	59.12

Genome browser: GCA_000159875

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Bacteroides sp. 1_1_6
PATRIC: 469586.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	99.60	no
125439	motility	BacteriaNetⓘ	no	80.80	no
125439	gram_stain	BacteriaNetⓘ	negative	98.40	no
125439	oxygen_tolerance	BacteriaNetⓘ	anaerobe	92.60	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Bacteroides sp. 1_1_6
PATRIC: 469586.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	94.29	no
125438	anaerobic	anaerobicⓘ	yes	81.61	no
125438	spore-forming	spore-formingⓘ	no	90.44	no
125438	aerobic	aerobicⓘ	no	90.84	no
125438	thermophilic	thermophileⓘ	no	96.97	yes
125438	flagellated	motile2+ⓘ	no	87.50	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) .
#68438	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 24820
#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 .

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