Strain identifier

BacDive ID: 5410

Type strain: No

Species: Acetobacterium dehalogenans

Strain Designation: MC

Strain history: <- G. Diekert, Universität Stuttgart, Institut für Mikrobiologie, Stuttgart, Germany; MC <- G. Diekert {1990}

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

@ref: 4402

BacDive-ID: 5410

DSM-Number: 11527

keywords: genome sequence, Bacteria, anaerobe

description: Acetobacterium dehalogenans MC is an anaerobe bacterium that was isolated from sewage digester sludge.

NCBI tax id

NCBI tax idMatching level
82116species
1068975strain

strain history

  • @ref: 4402
  • history: <- G. Diekert, Universität Stuttgart, Institut für Mikrobiologie, Stuttgart, Germany; MC <- G. Diekert {1990}

doi: 10.13145/bacdive5410.20250331.9.3

Name and taxonomic classification

LPSN

  • @ref: 20215
  • description: domain/bacteria
  • keyword: phylum/bacillota
  • domain: Bacteria
  • phylum: Bacillota
  • class: Clostridia
  • order: Eubacteriales
  • family: Eubacteriaceae
  • genus: Acetobacterium
  • species: Acetobacterium dehalogenans
  • full scientific name: Acetobacterium dehalogenans Kaufmann et al. 1998

@ref: 4402

domain: Bacteria

phylum: Firmicutes

class: Clostridia

order: Clostridiales

family: Eubacteriaceae

genus: Acetobacterium

species: Acetobacterium dehalogenans

full scientific name: Acetobacterium dehalogenans

strain designation: MC

type strain: no

Physiology and metabolism

oxygen tolerance

  • @ref: 4402
  • oxygen tolerance: anaerobe

Isolation, sampling and environmental information

isolation

  • @ref: 4402
  • sample type: sewage digester sludge
  • geographic location: Stuttgart
  • country: Germany
  • origin.country: DEU
  • continent: Europe

isolation source categories

Cat1Cat2Cat3
#Engineered#Biodegradation
#Engineered#Waste#Sewage sludge

Sequence information

Genome sequences

@refdescriptionaccessionassembly leveldatabaseNCBI tax ID
66792Acetobacterium dehalogenans DSM 11527GCA_000472665scaffoldncbi1068975
66792Acetobacterium dehalogenans DSM 115271068975.11wgspatric1068975
66792Acetobacterium dehalogenans DSM 115272513237154draftimg1068975

GC content

  • @ref: 4402
  • GC-content: 43.8
  • method: sequence analysis

Genome-based predictions

predictions

@refmodeltraitdescriptionpredictionconfidencetraining_data
125439BacteriaNetoxygen_toleranceOxygenic conditions needed for growthmicroaerophile85.8
125439BacteriaNetgram_stainReaction to gram-stainingvariable86.6
125439BacteriaNetmotilityAbility to perform movementyes53.4
125439BacteriaNetspore_formationAbility to form endo- or exosporesyes56
125438gram-positivegram-positivePositive reaction to Gram-stainingyes59.849no
125438anaerobicanaerobicAbility to grow under anoxygenic conditions (including facultative anaerobes)yes89.274yes
125438aerobicaerobicAbility to grow under oxygenic conditions (including facultative aerobes)no93.354yes
125438spore-formingspore-formingAbility to form endo- or exosporesyes53.815no
125438thermophilethermophilicAbility to grow at temperatures above or equal to 45°Cno89.124no
125438motile2+flagellatedAbility to perform flagellated movementyes64.777no

External links

@ref: 4402

culture collection no.: DSM 11527

Reference

@idauthorscataloguedoi/urltitle
4402Curators of the DSMZLeibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH (DSM 11527)https://www.dsmz.de/collection/catalogue/details/culture/DSM-11527
20215Parte, A.C., Sardà Carbasse, J., Meier-Kolthoff, J.P., Reimer, L.C. and Göker, M.10.1099/ijsem.0.004332List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ
66792Julia Koblitz, Joaquim Sardà, Lorenz Christian Reimer, Boyke Bunk, Jörg Overmannhttps://diaspora-project.de/progress.html#genomesAutomatically annotated for the DiASPora project (Digital Approaches for the Synthesis of Poorly Accessible Biodiversity Information)
125438Julia Koblitz, Lorenz Christian Reimer, Rüdiger Pukall, Jörg Overmann10.1101/2024.08.12.607695Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets
125439Philipp Münch, René Mreches, Martin Binder, Hüseyin Anil Gündüz, Xiao-Yin To, Alice McHardyhttps://github.com/GenomeNet/deepGdeepG: Deep Learning for Genome Sequence Data. R package version 0.3.1