Name: Polymorphobacter megasporae PAMC 29362
Creator: BacDive
License: https://creativecommons.org/licenses/by/4.0/

Strain identifier

BacDive ID: 170133

Type strain:

Species: Polymorphobacter megasporae

Strain Designation: PAMC 29362

Culture col. no.: JCM 34545, KCTC 82578

NCBI tax ID(s): 2835787 (species)

Special Collections

JCM
KCTC

When using BacDive for research please cite our paper

Polymorphobacter megasporae PAMC 29362 is a bacterium of the family Sphingosinicellaceae.

16S sequence
Bacteria
genome sequence

Information on the name and the taxonomic classification. Name and taxonomic classification

	Last LPSN update	09-12-2024 (DD-MM-YYYY)
	Domain	*Bacteria*
	Phylum	*Pseudomonadota*
	Class	*Alphaproteobacteria*
	Order	*Sphingomonadales*
	Family	*Sphingosinicellaceae*
	Genus	*Polymorphobacter*
	Species	**Polymorphobacter megasporae**
	Full Scientific Name (LPSN)	Polymorphobacter megasporae Noh et al. 2022

Information on morphological and physiological properties Morphology

[Ref.: #125439]	Gram stain	negative Confidence in %95.2
[Ref.: #125438]	Gram stain	negative Confidence in %93.485

Information on physiology and metabolism Physiology and metabolism

[Ref.: #125439]

Oxygen tolerance

obligate aerobe Confidence in %99.5

[Ref.: #125439]

Ability of spore formation

no Confidence in %95.8

Information on genomic background e.g. entries in nucleic sequence databass Sequence information

16S Sequence information:

	Sequence accession description	Seq. accession number	Sequence length (bp)	Sequence database	Associated NCBI tax ID
[Ref.: #20215]	Polymorphobacter sp. strain PAMC 29362 16S ribosomal RNA gene, partial sequence	MW507628	1487	GenBank ENA	2835787 tax ID	*

Genome sequence information:

	Sequence accession description	Seq. accession number	Assembly level	Sequence database	Associated NCBI tax ID
[Ref.: #66792]	Polymorphobacter megasporae PAMC 29362	GCA_018982885	complete	GenBank ENA	2835787 tax ID	*

Data predicted using genome information as a basis Genome-based predictions

	Model	Trait	Prediction	Confidence in %	In training data
	Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Polymorphobacter megasporae PAMC 29362 NCBI: GCA_018982885.2
[Ref.: #125438]	gram-positive	gram-positiveⓘ	no	93.485	no
[Ref.: #125438]	anaerobic	anaerobicⓘ	no	95.1	no
[Ref.: #125438]	aerobic	aerobicⓘ	yes	87.102	no
[Ref.: #125438]	spore-forming	spore-formingⓘ	no	87.346	no
[Ref.: #125438]	thermophile	thermophilicⓘ	no	96.347	no
[Ref.: #125438]	motile2+	flagellatedⓘ	yes	65.905	no

	Model	Trait	Prediction	Confidence in %
	Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Polymorphobacter megasporae PAMC 29362 NCBI: GCA_018982885
[Ref.: #125439]	BacteriaNet	spore_formationⓘ	no	95.8
[Ref.: #125439]	BacteriaNet	motilityⓘ	yes	61
[Ref.: #125439]	BacteriaNet	gram_stainⓘ	negative	95.2
[Ref.: #125439]	BacteriaNet	oxygen_toleranceⓘ	obligate aerobe	99.5

Availability in culture collections External links

[Ref.: #20215]

Culture collection no.

JCM 34545, KCTC 82578

Literature:

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Polymorphobacter megasporae sp. nov., isolated from an Antarctic lichen.	Noh HJ, Park Y, Yang J, Jang S, Lee H, Lee YM	Int J Syst Evol Microbiol	10.1099/ijsem.0.005535	2022	*

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) .

#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 .

* These data were automatically processed and therefore are not curated

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