Strain identifier

BacDive ID: 170015

Type strain: Yes

Species: Lysobacter selenitireducens

Strain Designation: 13A

NCBI tax ID(s): 2872639 (species)

For citation purpose refer to the digital object identifier (doi) of the current version.
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General

@ref: 20215

BacDive-ID: 170015

keywords: genome sequence, 16S sequence, Bacteria, Gram-negative

description: Lysobacter selenitireducens 13A is a Gram-negative bacterium of the family Lysobacteraceae.

NCBI tax id

  • NCBI tax id: 2872639
  • Matching level: species

doi: 10.13145/bacdive170015.20250331.9.3

Name and taxonomic classification

LPSN

  • @ref: 20215
  • description: domain/bacteria
  • keyword: phylum/pseudomonadota
  • domain: Bacteria
  • phylum: Pseudomonadota
  • class: Gammaproteobacteria
  • order: Lysobacterales
  • family: Lysobacteraceae
  • genus: Lysobacter
  • species: Lysobacter selenitireducens
  • full scientific name: Lysobacter selenitireducens Mao et al. 2022
  • synonyms

    • @ref: 20215
    • synonym: Novilysobacter selenitireducens

@ref: 20215

domain: Bacteria

phylum: Pseudomonadota

class: Gammaproteobacteria

order: Lysobacterales

family: Lysobacteraceae

genus: Lysobacter

species: Lysobacter selenitireducens

full scientific name: Lysobacter selenitireducens Mao et al. 2022

strain designation: 13A

type strain: yes

Morphology

cell morphology

@refgram stainconfidence
125438negative99.23
125439negative96.5

Physiology and metabolism

oxygen tolerance

  • @ref: 125439
  • oxygen tolerance: obligate aerobe
  • confidence: 99.2

spore formation

@refspore formationconfidence
125439no94.3
125438no91.376

Sequence information

16S sequences

  • @ref: 20215
  • description: Lysobacter selenitireducens strain 13A 16S ribosomal RNA gene, partial sequence
  • accession: MZ643208
  • length: 1467
  • database: nuccore
  • NCBI tax ID: 2872639

Genome sequences

  • @ref: 66792
  • description: Lysobacter selenitireducens 13A
  • accession: GCA_020026895
  • assembly level: contig
  • database: ncbi
  • NCBI tax ID: 2872639

Genome-based predictions

predictions

@refmodeltraitdescriptionpredictionconfidencetraining_data
125438gram-positivegram-positivePositive reaction to Gram-stainingno99.23no
125438anaerobicanaerobicAbility to grow under anoxygenic conditions (including facultative anaerobes)no98.495no
125438aerobicaerobicAbility to grow under oxygenic conditions (including facultative aerobes)yes84.182no
125438spore-formingspore-formingAbility to form endo- or exosporesno91.376no
125438thermophilethermophilicAbility to grow at temperatures above or equal to 45°Cno89.899no
125438motile2+flagellatedAbility to perform flagellated movementno53.231no
125439BacteriaNetspore_formationAbility to form endo- or exosporesno94.3
125439BacteriaNetmotilityAbility to perform movementyes74.8
125439BacteriaNetgram_stainReaction to gram-stainingnegative96.5
125439BacteriaNetoxygen_toleranceOxygenic conditions needed for growthobligate aerobe99.2

External links

@ref: 20215

culture collection no.: JCM 34786, GDMCC 1.2722

literature

  • topic: Phylogeny
  • Pubmed-ID: 36260505
  • title: Lysobacter selenitireducens sp. nov., isolated from river sediment.
  • authors: Mao S, Li S, Guo B, Mu W, Hou X, Liu H, Wei S, Liu A, Kong L, Chen Z
  • journal: Int J Syst Evol Microbiol
  • DOI: 10.1099/ijsem.0.005550
  • year: 2022
  • mesh: *Lysobacter/genetics, RNA, Ribosomal, 16S/genetics, Ubiquinone/chemistry, Phylogeny, Phosphatidylethanolamines/metabolism, Base Composition, Rivers, Sodium Chloride, Cardiolipins, Soil Microbiology, *DNA, Bacterial/genetics, Fatty Acids/chemistry, Bacterial Typing Techniques, Sequence Analysis, DNA, Phospholipids/chemistry, Glycolipids/analysis, Amino Acids/metabolism, Nucleotides
  • topic2: Metabolism

Reference

@idauthorstitledoi/url
20215Parte, 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 DSMZ10.1099/ijsem.0.004332
66792Julia Koblitz, Joaquim Sardà, Lorenz Christian Reimer, Boyke Bunk, Jörg OvermannAutomatically annotated for the DiASPora project (Digital Approaches for the Synthesis of Poorly Accessible Biodiversity Information)https://diaspora-project.de/progress.html#genomes
125438Julia Koblitz, Lorenz Christian Reimer, Rüdiger Pukall, Jörg OvermannPredicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets10.1101/2024.08.12.607695
125439Philipp Münch, René Mreches, Martin Binder, Hüseyin Anil Gündüz, Xiao-Yin To, Alice McHardydeepG: Deep Learning for Genome Sequence Data. R package version 0.3.1https://github.com/GenomeNet/deepG