Strain identifier

BacDive ID: 147030

Type strain: No

Species: Streptococcus pneumoniae

NCBI tax ID(s): 1313 (species)

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

@ref: 51130

BacDive-ID: 147030

keywords: genome sequence, Bacteria, Gram-positive

description: Streptococcus pneumoniae CCUG 32672 is a Gram-positive bacterium that was isolated from Human cerebrospinal fluid.

NCBI tax id

  • NCBI tax id: 1313
  • Matching level: species

doi: 10.13145/bacdive147030.20250331.9.3

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 pneumoniae
  • full scientific name: Streptococcus pneumoniae (Klein 1884) Chester 1901 (Approved Lists 1980)
  • synonyms

    @refsynonym
    20215Micrococcus pneumoniae
    20215Staphylococcus pneumoniae

@ref: 51130

domain: Bacteria

phylum: Firmicutes

class: Bacilli

order: Lactobacillales

family: Streptococcaceae

genus: Streptococcus

species: Streptococcus pneumoniae

type strain: no

Morphology

cell morphology

@refgram stainconfidence
125439positive90.7
125438positive90.217

Physiology and metabolism

oxygen tolerance

  • @ref: 125439
  • oxygen tolerance: microaerophile
  • confidence: 98.9

spore formation

  • @ref: 125439
  • spore formation: no
  • confidence: 94.7

Isolation, sampling and environmental information

isolation

  • @ref: 51130
  • sample type: Human cerebrospinal fluid
  • sampling date: 1994-03-01
  • geographic location: Gävle
  • country: Sweden
  • origin.country: SWE
  • continent: Europe

isolation source categories

Cat1Cat2Cat3
#Host#Human
#Host Body Product#Fluids#Cerebrospinal fluid
#Infection#Patient

Sequence information

Genome sequences

@refdescriptionaccessionassembly leveldatabaseNCBI tax ID
66792Streptococcus pneumoniae CCUG 32672GCA_001982715scaffoldncbi1313
66792Streptococcus pneumoniae strain CCUG 326721313.13967wgspatric1313

Genome-based predictions

predictions

@refmodeltraitdescriptionpredictionconfidencetraining_data
125438gram-positivegram-positivePositive reaction to Gram-stainingyes90.217no
125438anaerobicanaerobicAbility to grow under anoxygenic conditions (including facultative anaerobes)no89.171no
125438aerobicaerobicAbility to grow under oxygenic conditions (including facultative aerobes)no97.529no
125438spore-formingspore-formingAbility to form endo- or exosporesno83.828no
125438thermophilethermophilicAbility to grow at temperatures above or equal to 45°Cno95.5no
125438motile2+flagellatedAbility to perform flagellated movementno89.479no
125439BacteriaNetspore_formationAbility to form endo- or exosporesno94.7
125439BacteriaNetmotilityAbility to perform movementno83.5
125439BacteriaNetgram_stainReaction to gram-stainingpositive90.7
125439BacteriaNetoxygen_toleranceOxygenic conditions needed for growthmicroaerophile98.9

External links

@ref: 51130

culture collection no.: CCUG 32672

straininfo link

  • @ref: 101806
  • straininfo: 57890

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
51130Curators of the CCUGhttps://www.ccug.se/strain?id=32672Culture Collection University of Gothenburg (CCUG) (CCUG 32672)
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
101806Reimer, 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-ID57890.1
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