Bacteroides oleiciplenus (DSM 22535, JCM 16102, CCUG 60913)

Name: Bacteroides oleiciplenus (DSM 22535, JCM 16102, CCUG 60913)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 1630

Type strain

Culture col. no. DSM 22535 JCM 16102 CCUG 60913 YIT 12058 KCTC 15130

NCBI tax ID(s) 742727 626931

Special Collections DSMZ CCUG JCM KCTC Literature strains

Links

Bacteroides oleiciplenus DSM 22535 is an anaerobe, mesophilic, Gram-negative prokaryote that was isolated from human faeces .

Gram-negative rod-shaped anaerobe mesophilic genome sequence 16S sequence

Name and taxonomic classification

SI-ID 402131

JCM 16102,DSM 22535,CCUG 60913T

@ref 20215

Last LPSN update 2025-12-16 09:47:09

Domain Pseudomonadati

Phylum Bacteroidota

Class Bacteroidia

Order Bacteroidales

Family Bacteroidaceae

Genus Bacteroides

Species Bacteroides oleiciplenus

Full scientific name Bacteroides oleiciplenus Watanabe et al. 2010

Morphology

Cell morphology

@ref	Gram stain	Cell length	Cell width	Cell shape	Confidence
29531	negative	1.4-55.0 µm	1-1.9 µm	rod-shaped
125438	negative				93.019
125439	negative				95.3

Colony morphology

62496

Incubation period1 day

Pigmentation

29531

Productionyes

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
16383	CHOPPED MEAT MEDIUM (DSMZ Medium 78)		Medium recipe at MediaDive	Name: CHOPPED MEAT MEDIUM (DSMZ Medium 78) Composition: Ground beef 500.0 g/l Casitone 30.0 g/l Agar 15.0 g/l Ethanol 9.5 g/l (optional) K2HPO4 5.0 g/l Yeast extract 5.0 g/l L-Cysteine HCl 0.5 g/l Haemin 0.005 g/l (optional) Resazurin 0.001 g/l Vitamin K3 0.0005 g/l (optional) Vitamin K1 (optional) NaOH (optional) Distilled water
16383	COLUMBIA BLOOD MEDIUM (DSMZ Medium 693)		Medium recipe at MediaDive	Name: COLUMBIA BLOOD MEDIUM (DSMZ Medium 693) Composition: Defibrinated sheep blood 50.0 g/l Columbia agar base

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
16383	positive	growth	37	mesophilic
29531	positive	growth	37	mesophilic
29531	positive	optimum	37	mesophilic
62496	positive	growth	37	mesophilic
67770	positive	growth	37	mesophilic

Culture pH

@ref	Ability	Type	PH
29531	positive	optimum	7
29531	positive	growth	7

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance
16383	anaerobe
29531	anaerobe
62496	anaerobe

Spore formation

@ref	Spore formation	Confidence
29531
125439		90.6

Observation

@ref	Observation
29531	aggregates in chains
67770	quinones: MK-10, MK-11

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
29531	22599 ChEBI	arabinose	+	carbon source
68380	29016 ChEBI	arginine	-	hydrolysis	from API rID32A
68367	17057 ChEBI	cellobiose	+	builds acid from	from API 20A
68367	17634 ChEBI	D-glucose	+	builds acid from	from API 20A
68367	16899 ChEBI	D-mannitol	-	builds acid from	from API 20A
68380	16024 ChEBI	D-mannose	+	fermentation	from API rID32A
68367	16024 ChEBI	D-mannose	+	builds acid from	from API 20A
68367	65327 ChEBI	D-xylose	+	builds acid from	from API 20A
68367	4853 ChEBI	esculin	+	hydrolysis	from API 20A
68367	5291 ChEBI	gelatin	-	hydrolysis	from API 20A
29531	17754 ChEBI	glycerol	+	carbon source
68367	17754 ChEBI	glycerol	-	builds acid from	from API 20A
68367	30849 ChEBI	L-arabinose	+	builds acid from	from API 20A
68380	29985 ChEBI	L-glutamate	+	degradation	from API rID32A
68367	62345 ChEBI	L-rhamnose	+	builds acid from	from API 20A
68367	17716 ChEBI	lactose	+	builds acid from	from API 20A
68367	17306 ChEBI	maltose	+	builds acid from	from API 20A
29531	29864 ChEBI	mannitol	+	carbon source
68367	6731 ChEBI	melezitose	-	builds acid from	from API 20A
68380	17632 ChEBI	nitrate	-	reduction	from API rID32A
68380	16634 ChEBI	raffinose	+	fermentation	from API rID32A
68367	16634 ChEBI	raffinose	+	builds acid from	from API 20A
29531	26546 ChEBI	rhamnose	+	carbon source
29531	17814 ChEBI	salicin	+	carbon source
68367	17814 ChEBI	salicin	-	builds acid from	from API 20A
29531	30911 ChEBI	sorbitol	+	carbon source
68367	30911 ChEBI	sorbitol	-	builds acid from	from API 20A
68367	17992 ChEBI	sucrose	+	builds acid from	from API 20A
29531	27082 ChEBI	trehalose	+	carbon source
68367	27082 ChEBI	trehalose	-	builds acid from	from API 20A
68380	27897 ChEBI	tryptophan	+	energy source	from API rID32A
68367	27897 ChEBI	tryptophan	+	energy source	from API 20A
68380	16199 ChEBI	urea	-	hydrolysis	from API rID32A
68367	16199 ChEBI	urea	-	hydrolysis	from API 20A

Metabolite production

@ref	Chebi-ID	Metabolite	Production
68380	35581 ChEBI	indole		from API rID32A
68367	35581 ChEBI	indole		from API 20A

Metabolite tests

@ref	Chebi-ID	Metabolite	Indole test
68380	35581 ChEBI	indole	+	from API rID32A
68367	35581 ChEBI	indole	+	from API 20A

Enzymes

@ref	Value	Activity	Ec
68380	alanine arylamidase	+	3.4.11.2	from API rID32A
68380	alkaline phosphatase	+	3.1.3.1	from API rID32A
68380	alpha-arabinosidase	+	3.2.1.55	from API rID32A
68380	alpha-fucosidase	-	3.2.1.51	from API rID32A
29531	alpha-galactosidase	+	3.2.1.22
68380	alpha-galactosidase	+	3.2.1.22	from API rID32A
68380	alpha-glucosidase	+	3.2.1.20	from API rID32A
68380	arginine dihydrolase	-	3.5.3.6	from API rID32A
68380	beta-galactosidase	+	3.2.1.23	from API rID32A
68380	beta-Galactosidase 6-phosphate	-		from API rID32A
68380	beta-glucosidase	+	3.2.1.21	from API rID32A
68367	beta-glucosidase	+	3.2.1.21	from API 20A
68380	beta-glucuronidase	-	3.2.1.31	from API rID32A
29531	catalase	+	1.11.1.6
68367	gelatinase	-		from API 20A
68380	glutamate decarboxylase	+	4.1.1.15	from API rID32A
68380	glutamyl-glutamate arylamidase	+		from API rID32A
68380	glycin arylamidase	-		from API rID32A
68380	histidine arylamidase	-		from API rID32A
68380	L-arginine arylamidase	-		from API rID32A
68380	leucine arylamidase	-	3.4.11.1	from API rID32A
68380	leucyl glycin arylamidase	+	3.4.11.1	from API rID32A
68380	N-acetyl-beta-glucosaminidase	+	3.2.1.52	from API rID32A
68380	phenylalanine arylamidase	-		from API rID32A
68380	proline-arylamidase	-	3.4.11.5	from API rID32A
68380	pyrrolidonyl arylamidase	-	3.4.19.3	from API rID32A
68380	serine arylamidase	-		from API rID32A
68380	tryptophan deaminase	+	4.1.99.1	from API rID32A
68380	tyrosine arylamidase	-		from API rID32A
68380	urease	-	3.5.1.5	from API rID32A
68367	urease	-	3.5.1.5	from API 20A

API 20A

@ref	IND	URE	GLU	MAN	LAC	SAC	MAL	SAL	XYL	ARA	GEL	ESC	GLY	CEL	MNE	MLZ	RAF	SOR	RHA	TRE	CAT	Spores presentSPOR	GramGRAM	Morphology coccus="+" rod="-"COCC
16383	+	-	+	-	+	+	+	-	+	+	-	+	-	+	+	-	+	-	+	-	not determinedn.d.	not determinedn.d.	not determinedn.d.	not determinedn.d.

API rID32A

@ref	URE	ADH (Arg)	alpha GAL	beta GAL	beta-Galactosidase 6-phosphatebeta GP	alpha GLU	beta GLU	alpha ARA	beta GUR	beta-N-Acetyl-beta-glucosaminidasebeta NAG	MNE	RAF	GDC	alpha FUC	Reduction of nitrateNIT	IND	PAL	L-arginine arylamidaseArgA	ProA	LGA	Phenylalanine arylamidasePheA	Leucine arylamidaseLeuA	PyrA	Tyrosine arylamidaseTyrA	Alanine arylamidaseAlaA	Glycin arylamidaseGlyA	Histidine arylamidaseHisA	Glutamyl-glutamate arylamidaseGGA	Serine arylamidaseSerA
16383	-	-	+	+	-	+	+	+	-	+	+	+	+	-	-	+	+	-	-	+	-	-	-	-	+	-	-	+	-

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Host	#Human	#Male
#Host Body Product	#Gastrointestinal tract	#Feces (Stool)

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent	Sampling date
16383	human faeces (healtyh Japanese adult male)	Tokyo	Japan	JPN	Asia
62496	Human feces,healthy adult man		Japan	JPN	Asia	2009
67770	Human feces

Taxonmaps

69479

Global distribution of 16S sequence AB547644 (>99% sequence identity) for Bacteroides from Microbeatlas

Aquatic Samples	659
Soil Samples	312
Animal Samples	54909
Plant Samples	284
Total Samples	56164

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
16383	1	Risk group (German classification) According to TRBA 466

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	Bact_olei_YIT_12058_V1 assembly for Bacteroides oleiciplenus YIT 12058	scaffold	GCA_000315485	742727.4	2534681837	742727	72.05

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
20218	Bacteroides oleiciplenus gene for 16S ribosomal RNA, partial sequence, strain: JCM 16102	AB547644	1485		742727
16383	Bacteroides oleiciplenus gene for 16S rRNA, partial sequence, strain: YIT 12058	AB490803	1476		742727

GC content

@ref	GC-content (mol%)	Method
16383	43.6	high performance liquid chromatography (HPLC)
29531	43.6

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Bacteroides oleiciplenus YIT 12058
NCBI: GCA_000315485

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	90.60	no
125439	motility	BacteriaNetⓘ	no	72.30	no
125439	gram_stain	BacteriaNetⓘ	negative	95.30	no
125439	oxygen_tolerance	BacteriaNetⓘ	anaerobe	89.70	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Bacteroides oleiciplenus YIT 12058 YIT 12058
NCBI: GCA_000315485.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	93.02	yes
125438	anaerobic	anaerobicⓘ	yes	80.62	yes
125438	aerobic	aerobicⓘ	no	87.54	yes
125438	spore-forming	spore-formingⓘ	no	83.99	yes
125438	thermophilic	thermophileⓘ	no	95.55	yes
125438	flagellated	motile2+ⓘ	no	88.50	yes

Literature

Topic	Title	Authors	Journal	DOI	Year
Metabolism	Strain-level fitness in the gut microbiome is an emergent property of glycans and a single metabolite.	Park SY, Rao C, Coyte KZ, Kuziel GA, Zhang Y, Huang W, Franzosa EA, Weng JK, Huttenhower C, Rakoff-Nahoum S.	Cell	10.1016/j.cell.2022.01.002	2022
	Lactobacillus murinus Reduces Susceptibility to Secondary MRSA Infection in IAV-Infected Mice Through Promoting a T Cell-Independent IgA Response.	Chen Q, Lin Y, Wang K, Li J, Li P, Song H.	Microorganisms	10.3390/microorganisms13071709	2025
Pathogenicity	3-O-Acetyl-11-Keto-beta-Boswellic Acid Suppresses Colitis-Associated Colorectal Cancer by Inhibiting the NF-Kb Signaling Pathway and Remodeling Gut Microbiota.	Xu F, Li W, Zheng XJ, Hao Y, Yang YH, Yang H, Zhang S, Cao WX, Li XX, Zhang X, Du GH, Ji TF, Wang JH.	Oncol Res	10.32604/or.2025.062386	2025
	Gut microbiota and viral respiratory infections: microbial alterations, immune modulation, and impact on disease severity: a narrative review.	El-Khoury G, Hajjar C, Geitani R, Karam Sarkis D, Butel MJ, Barbut F, Abifadel M, Kapel N.	Front Microbiol	10.3389/fmicb.2025.1605143	2025
Genetics	Microbiome variation at the clam-sediment interface may explain changes in local productivity of Chamelea gallina in the North Adriatic sea.	Trapella G, Cinti N, Parma L, De Marco A, Dell'Acqua AN, Turroni S, Rampelli S, Scicchitano D, Iuffrida L, Bonaldo A, Franzellitti S, Candela M, Palladino G.	BMC Microbiol	10.1186/s12866-023-03146-8	2023
	Gut microbiome dynamics and predictive value in hospitalized COVID-19 patients: a comparative analysis of shallow and deep shotgun sequencing.	Kopera K, Gromowski T, Wydmanski W, Skonieczna-Zydecka K, Muszynska A, Zielinska K, Wierzbicka-Wos A, Kaczmarczyk M, Kadaj-Lipka R, Cembrowska-Lech D, Januszkiewicz K, Kotfis K, Witkiewicz W, Nalewajska M, Feret W, Marlicz W, Loniewski I, Labaj PP, Rydzewska G, Kosciolek T.	Front Microbiol	10.3389/fmicb.2024.1342749	2024
	Early Developmental Exposure to Triclosan Impacts Fecal Microbial Populations, IgA and Functional Activities of the Rat Microbiome.	Lahiani M, Gokulan K, Sutherland V, Cunny HC, Cerniglia CE, Khare S.	J Xenobiot	10.3390/jox14010012	2024
Pathogenicity	Microbial and host factors contribute to bloodstream infection in a pediatric acute lymphocytic leukemia mouse model.	Song Y, Perlman K, Gyarmati P.	Heliyon	10.1016/j.heliyon.2022.e11340	2022
	Microbiome Profiling Using Shotgun Metagenomic Sequencing Identified Unique Microorganisms in COVID-19 Patients With Altered Gut Microbiota.	Li S, Yang S, Zhou Y, Disoma C, Dong Z, Du A, Zhang Y, Chen Y, Huang W, Chen J, Song D, Chen Z, Liu P, Li S, Zheng R, Liu S, Razzaq A, Chen X, Tao S, Yu C, Feng T, Liao W, Peng Y, Jiang T, Huang J, Wu W, Hu L, Wang L, Li S, Xia Z.	Front Microbiol	10.3389/fmicb.2021.712081	2021
Genetics	Alterations in the Gut Fungal Community in a Mouse Model of Huntington's Disease.	Kong G, Le Cao KA, Hannan AJ.	Microbiol Spectr	10.1128/spectrum.02192-21	2022
	Gut microbiome of captive wolves is more similar to domestic dogs than wild wolves indicated by metagenomics study.	Chen L, Sun M, Xu D, Gao Z, Shi Y, Wang S, Zhou Y.	Front Microbiol	10.3389/fmicb.2022.1027188	2022
	919 Syrup Alleviates Postpartum Depression by Modulating the Structure and Metabolism of Gut Microbes and Affecting the Function of the Hippocampal GABA/Glutamate System.	Tian XY, Xing JW, Zheng QQ, Gao PF.	Front Cell Infect Microbiol	10.3389/fcimb.2021.694443	2021
	Immunomodulatory Effects of Probiotics on COVID-19 Infection by Targeting the Gut-Lung Axis Microbial Cross-Talk.	Synodinou KD, Nikolaki MD, Triantafyllou K, Kasti AN.	Microorganisms	10.3390/microorganisms10091764	2022
	Impact of Mediterranean Diet on Disease Activity and Gut Microbiota Composition of Rheumatoid Arthritis Patients.	Picchianti Diamanti A, Panebianco C, Salerno G, Di Rosa R, Salemi S, Sorgi ML, Meneguzzi G, Mariani MB, Rai A, Iacono D, Sesti G, Pazienza V, Lagana B.	Microorganisms	10.3390/microorganisms8121989	2020
Metabolism	Degradation of complex arabinoxylans by human colonic Bacteroidetes.	Pereira GV, Abdel-Hamid AM, Dutta S, D'Alessandro-Gabazza CN, Wefers D, Farris JA, Bajaj S, Wawrzak Z, Atomi H, Mackie RI, Gabazza EC, Shukla D, Koropatkin NM, Cann I.	Nat Commun	10.1038/s41467-020-20737-5	2021
	Gut-Thyroid axis: How gut microbial dysbiosis associated with euthyroid thyroid cancer.	Ishaq HM, Mohammad IS, Hussain R, Parveen R, Shirazi JH, Fan Y, Shahzad M, Hayat K, Li H, Ihsan A, Muhammad KS, Usman M, Zhang S, Yuan L, Ullah S, Paiva-Santos AC, Xu J.	J Cancer	10.7150/jca.66816	2022
Phylogeny	Far infrared radiation induces changes in gut microbiota and activates GPCRs in mice.	Khan I, Pathan S, Li XA, Leong WK, Liao WL, Wong V, Hsiao WLW.	J Adv Res	10.1016/j.jare.2019.12.003	2020
	Impact of cigarette smoking on gut microbial dysbiosis: a structured literature review.	Imade EE, Obayagbona NO.	Gut Microbiome (Camb)	10.1017/gmb.2024.3	2024
	Functional screening of a metagenomic library reveals operons responsible for enhanced intestinal colonization by gut commensal microbes.	Yoon MY, Lee KM, Yoon Y, Go J, Park Y, Cho YJ, Tannock GW, Yoon SS.	Appl Environ Microbiol	10.1128/aem.00581-13	2013
	Variation of Carbohydrate-Active Enzyme Patterns in the Gut Microbiota of Italian Healthy Subjects and Type 2 Diabetes Patients.	Soverini M, Turroni S, Biagi E, Quercia S, Brigidi P, Candela M, Rampelli S.	Front Microbiol	10.3389/fmicb.2017.02079	2017
Pathogenicity	Metformin Strongly Affects Gut Microbiome Composition in High-Fat Diet-Induced Type 2 Diabetes Mouse Model of Both Sexes.	Silamikele L, Silamikelis I, Ustinova M, Kalnina Z, Elbere I, Petrovska R, Kalnina I, Klovins J.	Front Endocrinol (Lausanne)	10.3389/fendo.2021.626359	2021
Metabolism	Two new xylanases with different substrate specificities from the human gut bacterium Bacteroides intestinalis DSM 17393.	Hong PY, Iakiviak M, Dodd D, Zhang M, Mackie RI, Cann I.	Appl Environ Microbiol	10.1128/aem.03176-13	2014
	Gut microbiota and COVID-19: A systematic review.	SeyedAlinaghi S, Afzalian A, Pashaei Z, Varshochi S, Karimi A, Mojdeganlou H, Mojdeganlou P, Razi A, Ghanadinezhad F, Shojaei A, Amiri A, Dashti M, Ghasemzadeh A, Dadras O, Mehraeen E, Afsahi AM.	Health Sci Rep	10.1002/hsr2.1080	2023
Metabolism	Synbiotic-driven improvement of metabolic disturbances is associated with changes in the gut microbiome in diet-induced obese mice.	Ke X, Walker A, Haange SB, Lagkouvardos I, Liu Y, Schmitt-Kopplin P, von Bergen M, Jehmlich N, He X, Clavel T, Cheung PCK.	Mol Metab	10.1016/j.molmet.2019.01.012	2019
Metabolism	Structural and functional aspects of mannuronic acid-specific PL6 alginate lyase from the human gut microbe Bacteroides cellulosilyticus.	Stender EGP, Dybdahl Andersen C, Fredslund F, Holck J, Solberg A, Teze D, Peters GHJ, Christensen BE, Christensen BE, Aachmann FL, Welner DH, Svensson B.	J Biol Chem	10.1074/jbc.ra119.010206	2019
Metabolism	16S rRNA gene profiling and genome reconstruction reveal community metabolic interactions and prebiotic potential of medicinal herbs used in neurodegenerative disease and as nootropics.	Peterson CT, Sharma V, Iablokov SN, Albayrak L, Khanipov K, Uchitel S, Chopra D, Mills PJ, Fofanov Y, Rodionov DA, Peterson SN.	PLoS One	10.1371/journal.pone.0213869	2019
	Metagenomic identification of a novel salt tolerance gene from the human gut microbiome which encodes a membrane protein with homology to a brp/blh-family beta-carotene 15,15'-monooxygenase.	Culligan EP, Sleator RD, Marchesi JR, Hill C.	PLoS One	10.1371/journal.pone.0103318	2014
	CRISPR-Cas Systems in Bacteroides fragilis, an Important Pathobiont in the Human Gut Microbiome.	Tajkarimi M, Wexler HM.	Front Microbiol	10.3389/fmicb.2017.02234	2017
	Type VI secretion systems of human gut Bacteroidales segregate into three genetic architectures, two of which are contained on mobile genetic elements.	Coyne MJ, Roelofs KG, Comstock LE.	BMC Genomics	10.1186/s12864-016-2377-z	2016
Phylogeny	Bacteroides luti sp. nov., an anaerobic, cellulolytic and xylanolytic bacterium isolated from methanogenic sludge.	Hatamoto M, Kaneshige M, Nakamura A, Yamaguchi T.	Int J Syst Evol Microbiol	10.1099/ijs.0.056630-0	2014
Phylogeny	Bacteroides stercorirosoris sp. nov. and Bacteroides faecichinchillae sp. nov., isolated from chinchilla (Chinchilla lanigera) faeces.	Kitahara M, Sakamoto M, Tsuchida S, Kawasumi K, Amao H, Benno Y, Ohkuma M	Int J Syst Evol Microbiol	10.1099/ijs.0.032706-0	2011
Phylogeny	Bacteroides clarus sp. nov., Bacteroides fluxus sp. nov. and Bacteroides oleiciplenus sp. nov., isolated from human faeces.	Watanabe Y, Nagai F, Morotomi M, Sakon H, Tanaka R	Int J Syst Evol Microbiol	10.1099/ijs.0.015107-0	2009

References

#16383	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 22535
#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 )
#20218	Verslyppe, B., De Smet, W., De Baets, B., De Vos, P., Dawyndt P.: StrainInfo introduces electronic passports for microorganisms.. Syst Appl Microbiol. 37: 42 - 50 2014 ( DOI 10.1016/j.syapm.2013.11.002 , PubMed 24321274 )
#29531	Barberan A, Caceres Velazquez H, Jones S, Fierer N.: Hiding in Plain Sight: Mining Bacterial Species Records for Phenotypic Trait Information. mSphere 2: 2017 ( DOI 10.1128/mSphere.00237-17 , PubMed 28776041 ) - originally annotated from #25928 (see below)
#62496	Culture Collection University of Gothenburg (CCUG) ; Curators of the CCUG; CCUG 60913
#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) .
#67770	Japan Collection of Microorganism (JCM) ; Curators of the JCM;
#68367	Automatically annotated from API 20A .
#68380	Automatically annotated from API rID32A .
#69479	João F Matias Rodrigues, Janko Tackmann,Gregor Rot, Thomas SB Schmidt, Lukas Malfertheiner, Mihai Danaila,Marija Dmitrijeva, Daniela Gaio, Nicolas Näpflin and Christian von Mering. University of Zurich.: MicrobeAtlas 1.0 beta .
#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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Bacteroides oleiciplenus (DSM 22535, JCM 16102, CCUG 60913)

Name and taxonomic classification

Morphology

Cell morphology

Colony morphology

Pigmentation

Culture and growth conditions

Culture medium

Culture temp

Culture pH

Physiology and metabolism

Oxygen tolerance

Spore formation

Observation

Metabolite utilization

Metabolite production

Metabolite tests

Enzymes

API 20A

API rID32A

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_000315485

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

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