Cite this strain

Telluribacter humicola (KCTC 42819, JCM 31133)

Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 163956

Type strain

Culture col. no. KCTC 42819 JCM 31133

NCBI tax ID(s) 1720261

Special Collections JCM KCTC

Links

Name and taxonomic classification

Telluribacter humicola JCM 31133 is an aerobe, mesophilic bacterium that was isolated from Soil from Goyang.

aerobe mesophilic genome sequence 16S sequence Bacteria

Culture Collection Numbers

KCTC 42819 JCM 31133

Last LPSN update 2024-12-09 10:52:43

Domain Bacteria

Phylum Bacteroidota

Class Cytophagia

Order Cytophagales

Family Spirosomataceae

Genus Telluribacter

Species Telluribacter humicola

Full scientific name Telluribacter humicola Lee et al. 2017

Morphology

Cell morphology

@ref	Motility	Confidence
125438		90.5
125438		98.664
125439		98.6

Culture and growth conditions

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
67770	positive	growth	30	mesophilic

Physiology and metabolism

Spore formation

@ref	Spore formation	Confidence
125439		96.4

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125438	aerobe	90.118

Isolation, sampling and environmental information

Isolation

@ref	Sample type	Country	Country ISO 3 Code	Continent
67770	Soil from Goyang	Republic of Korea	KOR	Asia

Taxonmaps

69479

Global distribution of 16S sequence KT630891 (>99% sequence identity) for Telluribacter humicola subclade from Microbeatlas

Aquatic Samples	116
Soil Samples	530
Animal Samples	135
Plant Samples	36
Total Samples	817

Safety information

Sequence information

GC content

@ref	GC-content (mol%)	Method
67770	45.9	thermal denaturation, midpoint method (Tm)

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
67770	Telluribacter humicola strain PTR3 16S ribosomal RNA gene, partial sequence	KT630891	1451		1720261

Genome sequences

@ref	Description	Accession	Assembly level	Database	NCBI tax ID
66792	Telluribacter humicola KCTC 42819	GCA_017355935	scaffold		1720261
66792	Telluribacter humicola strain KCTC 42819	1720261.3	wgs	PATRIC	1720261

Genome-based predictions

Predictions

Predictions from Diaspora project based on:
Telluribacter humicola KCTC 42819
NCBI: GCA_017355935

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	96.40	no
125439	motility	BacteriaNetⓘ	no	51.40	no
125439	gram_stain	BacteriaNetⓘ	negative	98.60	no
125439	oxygen_tolerance	BacteriaNetⓘ	aerobe	81.20	no

Predictions from Diaspora project based on:
Telluribacter humicola KCTC 42819
NCBI: GCA_017355935.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	98.66	no
125438	anaerobic	anaerobicⓘ	no	96.43	no
125438	aerobic	aerobicⓘ	yes	90.12	no
125438	spore-forming	spore-formingⓘ	no	84.20	no
125438	thermophilic	thermophileⓘ	no	95.98	yes
125438	flagellated	motile2+ⓘ	no	90.50	no

External links

Literature

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Telluribacter humicola gen. nov., sp. nov., a new member of the family Cytophagaceae isolated from soil in South Korea.	Lee D, Jang JH, Cha S, Seo T	Antonie Van Leeuwenhoek	10.1007/s10482-016-0754-x	2016

Culture collection no.

Culture collection no.
KCTC 42819
JCM 31133

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) .
#67770	Japan Collection of Microorganism (JCM) ; Curators of the JCM;
#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 .

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