Strain identifier

BacDive ID: 153316

Type strain: No

Species: Streptococcus agalactiae

NCBI tax ID(s): 1105271 (strain), 1311 (species)

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

@ref: 58466

BacDive-ID: 153316

keywords: genome sequence, Bacteria, microaerophile, mesophilic

description: Streptococcus agalactiae CCUG 49100 is a microaerophile, mesophilic bacterium that was isolated from Human synovial fluid,48-yr-old male.

NCBI tax id

NCBI tax idMatching level
1105271strain
1311species

doi: 10.13145/bacdive153316.20250331.9.3

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: 58466

domain: Bacteria

phylum: Firmicutes

class: Bacilli

order: Lactobacillales

family: Streptococcaceae

genus: Streptococcus

species: Streptococcus agalactiae

type strain: no

Culture and growth conditions

culture temp

  • @ref: 58466
  • growth: positive
  • type: growth
  • temperature: 37

Physiology and metabolism

oxygen tolerance

@refoxygen toleranceconfidence
58466microaerophile
125439microaerophile94.5

Isolation, sampling and environmental information

isolation

  • @ref: 58466
  • sample type: Human synovial fluid,48-yr-old male
  • sampling date: 1998-01-23
  • geographic location: Lidköping
  • country: Sweden
  • origin.country: SWE
  • continent: Europe

isolation source categories

Cat1Cat2Cat3
#Host#Human#Male
#Host Body Product#Fluids#Synovial fluid
#Infection#Patient

Sequence information

Genome sequences

@refdescriptionaccessionassembly leveldatabaseNCBI tax ID
66792Streptococcus agalactiae CCUG 49100GCA_000310785contigncbi1105271
66792Streptococcus agalactiae CCUG 491001105271.3wgspatric1105271
66792Streptococcus agalactiae CCUG 491002706794506draftimg1105271

Genome-based predictions

predictions

@refmodeltraitdescriptionpredictionconfidencetraining_data
125438gram-positivegram-positivePositive reaction to Gram-stainingyes86.251no
125438anaerobicanaerobicAbility to grow under anoxygenic conditions (including facultative anaerobes)no91.257yes
125438spore-formingspore-formingAbility to form endo- or exosporesno85.528no
125438aerobicaerobicAbility to grow under oxygenic conditions (including facultative aerobes)no95.771yes
125438thermophilethermophilicAbility to grow at temperatures above or equal to 45°Cno95.913no
125438motile2+flagellatedAbility to perform flagellated movementno88.5no
125439BacteriaNetspore_formationAbility to form endo- or exosporesno81.5
125439BacteriaNetmotilityAbility to perform movementno68.4
125439BacteriaNetgram_stainReaction to gram-stainingpositive87.8
125439BacteriaNetoxygen_toleranceOxygenic conditions needed for growthmicroaerophile94.5

External links

@ref: 58466

culture collection no.: CCUG 49100

straininfo link

  • @ref: 107139
  • straininfo: 215253

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
58466Curators of the CCUGhttps://www.ccug.se/strain?id=49100Culture Collection University of Gothenburg (CCUG) (CCUG 49100)
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
107139Reimer, 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-ID215253.1
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