Herbaspirillum huttiense (JCM 14681)

Name: Herbaspirillum huttiense (JCM 14681)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 161519

Type strain

Culture col. no. JCM 14681

NCBI tax ID(s) 863372

Special Collections JCM

History K. Minamisawa B39 (Herbaspirillum sp.).

Links

Herbaspirillum huttiense JCM 14681 is a bacterium that was isolated from Stem of wild rice W1627.

genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 131120

JCM 14681

@ref 20215

Last LPSN update 2026-02-09 11:39:10

Domain Bacteria

Phylum Pseudomonadota

Class Betaproteobacteria

Order Burkholderiales

Family Oxalobacteraceae

Genus Herbaspirillum

Species Herbaspirillum huttiense

Full scientific name Herbaspirillum huttiense (Leifson 1962) Ding and Yokota 2004

Synonyms (1)

Pseudomonas huttiensis

BacDive ID	Other strains from **Herbaspirillum huttiense** (4)	Type strain
136095	H. huttiense CIP 102439
154181	H. huttiense CCUG 52673
154257	H. huttiense CCUG 52906
154258	H. huttiense CCUG 52907 A

Morphology

Cell morphology

@ref	Gram stain	Confidence
125438	negative	97.3
125439	negative	95.3

Culture and growth conditions

Culture temp

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

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	obligate aerobe	92.4

Spore formation

@ref	Spore formation	Confidence
125439		96.5

Isolation, sampling and environmental information

Isolation

@ref	Sample type	Host species
67770	Stem of wild rice (Oryza meridionalis) W1627	Oryza meridionalis

Taxonmaps

69479

Global distribution of 16S sequence LC595299 (>99% sequence identity) for Herbaspirillum from Microbeatlas

Aquatic Samples	342
Soil Samples	197
Animal Samples	743
Plant Samples	411
Total Samples	1693

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID
67770	ASM33349v1 assembly for Herbaspirillum sp. B39	contig	GCA_000333495	95609.3	2551306658	95609

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
67770	Herbaspirillum sp. B39 gene for 16S ribosomal RNA	AB027694	600		95609
67770	Herbaspirillum huttiense JCM:14681 gene for 16S rRNA, partial sequence	LC595299	1455		863372

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Herbaspirillum sp. B39
NCBI: GCA_000333495

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	96.50	no
125439	motility	BacteriaNetⓘ	yes	79.00	no
125439	gram_stain	BacteriaNetⓘ	negative	95.30	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	92.40	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Herbaspirillum sp. B39 B39
NCBI: GCA_000333495.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	97.30	no
125438	anaerobic	anaerobicⓘ	no	97.16	no
125438	aerobic	aerobicⓘ	yes	87.41	no
125438	spore-forming	spore-formingⓘ	no	89.34	no
125438	thermophilic	thermophileⓘ	no	98.99	no
125438	flagellated	motile2+ⓘ	yes	73.98	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 )
#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 .
#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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