Strain identifier

BacDive ID: 144379

Type strain: No

Species: Streptococcus agalactiae

NCBI tax ID(s): 1154808 (strain), 1311 (species)

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

@ref: 48049

BacDive-ID: 144379

keywords: genome sequence, Bacteria

description: Streptococcus agalactiae CCUG 24810 is a bacterium that was isolated from Human ear,newborn.

NCBI tax id

NCBI tax idMatching level
1154808strain
1311species

doi: 10.13145/bacdive144379.20250331.9.3

Name and taxonomic classification

LPSN

  • @ref: 20215
  • description: domain/bacteria
  • keyword: phylum/bacillota
  • domain: Bacteria
  • phylum: Bacillota
  • class: Bacilli
  • order: Lactobacillales
  • family: Streptococcaceae
  • genus: Streptococcus
  • species: Streptococcus agalactiae
  • full scientific name: Streptococcus agalactiae Lehmann and Neumann 1896 (Approved Lists 1980)
  • synonyms

    @refsynonym
    20215Streptococcus difficile
    20215Streptococcus difficilis

@ref: 48049

domain: Bacteria

phylum: Firmicutes

class: Bacilli

order: Lactobacillales

family: Streptococcaceae

genus: Streptococcus

species: Streptococcus agalactiae

type strain: no

Physiology and metabolism

oxygen tolerance

  • @ref: 125439
  • oxygen tolerance: microaerophile
  • confidence: 96.6

spore formation

  • @ref: 125439
  • spore formation: no
  • confidence: 97.1

Isolation, sampling and environmental information

isolation

  • @ref: 48049
  • sample type: Human ear,newborn
  • sampling date: 1989-06-11
  • geographic location: Göteborg
  • country: Sweden
  • origin.country: SWE
  • continent: Europe

isolation source categories

Cat1Cat2Cat3
#Host#Human#Child
#Host Body-Site#Organ#Ear
#Infection#Patient

Sequence information

Genome sequences

@refdescriptionaccessionassembly leveldatabaseNCBI tax ID
66792Streptococcus agalactiae CCUG 24810GCA_000288695contigncbi1154808
66792Streptococcus agalactiae CCUG 248101154808.3wgspatric1154808
66792Streptococcus agalactiae CCUG 248102534682322draftimg1154808

Genome-based predictions

predictions

@refmodeltraitdescriptionpredictionconfidencetraining_data
125439BacteriaNetoxygen_toleranceOxygenic conditions needed for growthmicroaerophile96.6
125439BacteriaNetgram_stainReaction to gram-stainingpositive86.7
125439BacteriaNetmotilityAbility to perform movementno76.1
125439BacteriaNetspore_formationAbility to form endo- or exosporesno97.1
125438gram-positivegram-positivePositive reaction to Gram-stainingyes84.799no
125438anaerobicanaerobicAbility to grow under anoxygenic conditions (including facultative anaerobes)no90.356no
125438spore-formingspore-formingAbility to form endo- or exosporesno85.51no
125438aerobicaerobicAbility to grow under oxygenic conditions (including facultative aerobes)no96.62no
125438thermophilethermophilicAbility to grow at temperatures above or equal to 45°Cno96.442no
125438motile2+flagellatedAbility to perform flagellated movementno89no

External links

@ref: 48049

culture collection no.: CCUG 24810

straininfo link

  • @ref: 99586
  • straininfo: 57443

Reference

@idauthorstitledoi/urlcatalogue
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
48049Curators of the CCUGhttps://www.ccug.se/strain?id=24810Culture Collection University of Gothenburg (CCUG) (CCUG 24810)
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
99586Reimer, L.C., Lissin, A.,Schober, I., Witte,J.F., Podstawka, A., Lüken, H., Bunk, B.,Overmann, J.StrainInfo: A central database for resolving microbial strain identifiers10.60712/SI-ID57443.1
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