Strain identifier

BacDive ID: 156556

Type strain: No

Species: Haemophilus parainfluenzae

Variant: biovar II

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: 62876

BacDive-ID: 156556

keywords: genome sequence, Bacteria, microaerophile, mesophilic, Gram-negative

description: Haemophilus parainfluenzae CCUG 62655 is a microaerophile, mesophilic, Gram-negative bacterium that was isolated from Human.

NCBI tax id

  • NCBI tax id: 729
  • Matching level: species

doi: 10.13145/bacdive156556.20250331.9.3

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: 62876

domain: Bacteria

phylum: Proteobacteria

class: Gammaproteobacteria

order: Pasteurellales

family: Pasteurellaceae

genus: Haemophilus

species: Haemophilus parainfluenzae

variant: biovar II

type strain: no

Morphology

cell morphology

@refgram stainconfidencemotility
125439negative97.8
12543892.768no
125438negative96.978

Culture and growth conditions

culture temp

  • @ref: 62876
  • growth: positive
  • type: growth
  • temperature: 37

Physiology and metabolism

oxygen tolerance

@refoxygen toleranceconfidence
62876microaerophile
125439obligate aerobe90.3

spore formation

@refspore formationconfidence
125439no99.1
125438no93.697

Isolation, sampling and environmental information

isolation

  • @ref: 62876
  • sample type: Human
  • sampling date: 2010
  • geographic location: Århus
  • country: Denmark
  • origin.country: DNK
  • continent: Europe

isolation source categories

  • Cat1: #Host
  • Cat2: #Human

Sequence information

Genome sequences

@refdescriptionaccessionassembly leveldatabaseNCBI tax ID
66792Haemophilus parainfluenzae CCUG 62655GCA_001679455scaffoldncbi729
66792Haemophilus parainfluenzae strain CCUG 62655729.36wgspatric729

Genome-based predictions

predictions

@refmodeltraitdescriptionpredictionconfidencetraining_data
125438gram-positivegram-positivePositive reaction to Gram-stainingno96.978no
125438anaerobicanaerobicAbility to grow under anoxygenic conditions (including facultative anaerobes)no87.399yes
125438aerobicaerobicAbility to grow under oxygenic conditions (including facultative aerobes)no88.456yes
125438spore-formingspore-formingAbility to form endo- or exosporesno93.697no
125438thermophilethermophilicAbility to grow at temperatures above or equal to 45°Cno96.5yes
125438motile2+flagellatedAbility to perform flagellated movementno92.768no
125439BacteriaNetspore_formationAbility to form endo- or exosporesno99.1
125439BacteriaNetmotilityAbility to perform movementno67
125439BacteriaNetgram_stainReaction to gram-stainingnegative97.8
125439BacteriaNetoxygen_toleranceOxygenic conditions needed for growthobligate aerobe90.3

External links

@ref: 62876

culture collection no.: CCUG 62655

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
62876Curators of the CCUGhttps://www.ccug.se/strain?id=62655Culture Collection University of Gothenburg (CCUG) (CCUG 62655)
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
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