Name: Lysobacter luteus AS29M
Creator: BacDive
License: https://creativecommons.org/licenses/by/4.0/

Strain identifier

BacDive ID: 170014

Type strain:

Species: Lysobacter luteus

Strain Designation: AS29M

Culture col. no.: LMG 32343, CECT 30171

NCBI tax ID(s): 2822368 (species)

When using BacDive for research please cite our paper

Lysobacter luteus AS29M is a bacterium of the family Lysobacteraceae.

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	*Gammaproteobacteria*
	Order	*Lysobacterales*
	Family	*Lysobacteraceae*
	Genus	*Lysobacter*
	Species	**Lysobacter luteus**
	Full Scientific Name (LPSN)	Lysobacter luteus Lucena et al. 2022

Synonym

Novilysobacter luteus

Information on morphological and physiological properties Morphology

[Ref.: #125438]	Gram stain	negative Confidence in %97.244
[Ref.: #125439]	Gram stain	negative Confidence in %93.1

Information on physiology and metabolism Physiology and metabolism

[Ref.: #125439]

Oxygen tolerance

obligate aerobe Confidence in %99.8

[Ref.: #125439]	Ability of spore formation	no Confidence in %92.2
[Ref.: #125438]	Ability of spore formation	no Confidence in %90.359

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]	Lysobacter luteus strain AS29M 16S ribosomal RNA gene, partial sequence	MZ047093	1535	GenBank ENA	2822368 tax ID	*

Genome sequence information:

	Sequence accession description	Seq. accession number	Assembly level	Sequence database	Associated NCBI tax ID
[Ref.: #66792]	Lysobacter luteus CECT 30171	GCA_907164845	complete	GenBank ENA	2822368 tax ID	*
[Ref.: #66792]	Lysobacter sp. CECT 30171	2822368.3	complete	PATRIC	2822368 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: Lysobacter luteus CECT 30171 NCBI: GCA_907164845.1
[Ref.: #125438]	gram-positive	gram-positiveⓘ	no	97.244	no
[Ref.: #125438]	anaerobic	anaerobicⓘ	no	98.385	no
[Ref.: #125438]	aerobic	aerobicⓘ	yes	81.052	no
[Ref.: #125438]	spore-forming	spore-formingⓘ	no	90.359	no
[Ref.: #125438]	thermophile	thermophilicⓘ	no	90.583	no
[Ref.: #125438]	motile2+	flagellatedⓘ	no	55.286	no

	Model	Trait	Prediction	Confidence in %
	Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Lysobacter luteus CECT 30171 NCBI: GCA_907164845
[Ref.: #125439]	BacteriaNet	spore_formationⓘ	no	92.2
[Ref.: #125439]	BacteriaNet	motilityⓘ	yes	69.9
[Ref.: #125439]	BacteriaNet	gram_stainⓘ	negative	93.1
[Ref.: #125439]	BacteriaNet	oxygen_toleranceⓘ	obligate aerobe	99.8

Availability in culture collections External links

[Ref.: #20215]

Culture collection no.

LMG 32343, CECT 30171

Literature:

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Parvicella tangerina gen. nov., sp. nov. (Parvicellaceae fam. nov., Flavobacteriales), first cultured representative of the marine clade UBA10066, and Lysobacter luteus sp. nov., from activated sludge of a seawater-processing wastewater treatment plant.	Lucena T, Sanchez O, Sanz-Saez I, Acinas SG, Garrido L, Mas J, Macian MC, Ruvira MA, Arahal DR, Pujalte MJ	Int J Syst Evol Microbiol	10.1099/ijsem.0.005498	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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Lysobacter luteus CECT 30171

Lysobacter luteus CECT 30171

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