Strain identifier

BacDive ID: 148839

Type strain: No

Species: Haemophilus parainfluenzae

NCBI tax ID(s): 729 (species)

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

@ref: 53169

BacDive-ID: 148839

keywords: genome sequence, Bacteria, Gram-negative

description: Haemophilus parainfluenzae CCUG 36129 is a Gram-negative bacterium that was isolated from Human throat,child with asthma.

NCBI tax id

  • NCBI tax id: 729
  • Matching level: species

doi: 10.13145/bacdive148839.20250331.9.3

Name and taxonomic classification

LPSN

  • @ref: 20215
  • description: domain/bacteria
  • keyword: phylum/pseudomonadota
  • domain: Bacteria
  • phylum: Pseudomonadota
  • class: Gammaproteobacteria
  • order: Pasteurellales
  • family: Pasteurellaceae
  • genus: Haemophilus
  • species: Haemophilus parainfluenzae
  • full scientific name: Haemophilus parainfluenzae Rivers 1922 (Approved Lists 1980)

@ref: 53169

domain: Bacteria

phylum: Proteobacteria

class: Gammaproteobacteria

order: Pasteurellales

family: Pasteurellaceae

genus: Haemophilus

species: Haemophilus parainfluenzae

type strain: no

Morphology

cell morphology

@refmotilityconfidencegram stain
125438no95.75
12543896.915negative

Physiology and metabolism

oxygen tolerance

  • @ref: 125439
  • oxygen tolerance: obligate aerobe
  • confidence: 93.4

spore formation

@refspore formationconfidence
125439no98.5
125438no92.383

Isolation, sampling and environmental information

isolation

  • @ref: 53169
  • sample type: Human throat,child with asthma
  • sampling date: 1970
  • geographic location: Birmingham
  • country: United Kingdom
  • origin.country: GBR
  • continent: Europe

isolation source categories

Cat1Cat2Cat3
#Host#Human#Child
#Host Body-Site#Oral cavity and airways#Throat
#Infection#Patient

Sequence information

Genome sequences

@refdescriptionaccessionassembly leveldatabaseNCBI tax ID
66792Haemophilus parainfluenzae NCTC10672GCA_900450995contigncbi729
66792Haemophilus parainfluenzae strain NCTC10672729.341wgspatric729

Genome-based predictions

predictions

@refmodeltraitdescriptionpredictionconfidencetraining_data
125438gram-positivegram-positivePositive reaction to Gram-stainingno96.915no
125438anaerobicanaerobicAbility to grow under anoxygenic conditions (including facultative anaerobes)no86.373no
125438spore-formingspore-formingAbility to form endo- or exosporesno92.383no
125438aerobicaerobicAbility to grow under oxygenic conditions (including facultative aerobes)no88.952no
125438thermophilethermophilicAbility to grow at temperatures above or equal to 45°Cno98no
125438motile2+flagellatedAbility to perform flagellated movementno95.75no
125439BacteriaNetspore_formationAbility to form endo- or exosporesno98.5
125439BacteriaNetmotilityAbility to perform movementno75.4
125439BacteriaNetgram_stainReaction to gram-stainingpositive79.2
125439BacteriaNetoxygen_toleranceOxygenic conditions needed for growthobligate aerobe93.4

External links

@ref: 53169

culture collection no.: CCUG 36129, MCCM 02683, NCTC 10672

straininfo link

  • @ref: 103332
  • straininfo: 39426

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
53169Curators of the CCUGhttps://www.ccug.se/strain?id=36129Culture Collection University of Gothenburg (CCUG) (CCUG 36129)
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
103332Reimer, 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-ID39426.1
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