Strain identifier

BacDive ID: 150165

Type strain: No

Species: Haemophilus haemolyticus

NCBI tax ID(s): 726 (species)

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

@ref: 54671

BacDive-ID: 150165

keywords: genome sequence, Bacteria

description: Haemophilus haemolyticus CCUG 39154 is a bacterium that was isolated from Human eye.

NCBI tax id

  • NCBI tax id: 726
  • Matching level: species

doi: 10.13145/bacdive150165.20250331.9.3

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 haemolyticus
  • full scientific name: Haemophilus haemolyticus Bergey et al. 1923 (Approved Lists 1980)

@ref: 54671

domain: Bacteria

phylum: Proteobacteria

class: Gammaproteobacteria

order: Pasteurellales

family: Pasteurellaceae

genus: Haemophilus

species: Haemophilus haemolyticus

type strain: no

Morphology

cell morphology

@refgram stainconfidencemotility
125439negative99.5
12543895.655no
125438negative96.978

Physiology and metabolism

oxygen tolerance

  • @ref: 125439
  • oxygen tolerance: obligate aerobe
  • confidence: 96

spore formation

@refspore formationconfidence
125439no99.2
125438no94.671

Isolation, sampling and environmental information

isolation

  • @ref: 54671
  • sample type: Human eye
  • sampling date: 1998-03-19
  • geographic location: Göteborg
  • country: Sweden
  • origin.country: SWE
  • continent: Europe

isolation source categories

Cat1Cat2Cat3
#Host#Human
#Host Body-Site#Organ#Eye

Sequence information

Genome sequences

@refdescriptionaccessionassembly leveldatabaseNCBI tax ID
66792Haemophilus haemolyticus CCUG 39154GCA_001679445scaffoldncbi726
66792Haemophilus haemolyticus strain CCUG 39154726.60wgspatric726

Genome-based predictions

predictions

@refmodeltraitdescriptionpredictionconfidencetraining_data
125438gram-positivegram-positivePositive reaction to Gram-stainingno96.978no
125438anaerobicanaerobicAbility to grow under anoxygenic conditions (including facultative anaerobes)no91.258no
125438aerobicaerobicAbility to grow under oxygenic conditions (including facultative aerobes)no90.226no
125438spore-formingspore-formingAbility to form endo- or exosporesno94.671no
125438thermophilethermophilicAbility to grow at temperatures above or equal to 45°Cno96.483no
125438motile2+flagellatedAbility to perform flagellated movementno95.655no
125439BacteriaNetspore_formationAbility to form endo- or exosporesno99.2
125439BacteriaNetmotilityAbility to perform movementno66.7
125439BacteriaNetgram_stainReaction to gram-stainingnegative99.5
125439BacteriaNetoxygen_toleranceOxygenic conditions needed for growthobligate aerobe96

External links

@ref: 54671

culture collection no.: CCUG 39154

straininfo link

  • @ref: 104467
  • straininfo: 54202

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
54671Curators of the CCUGhttps://www.ccug.se/strain?id=39154Culture Collection University of Gothenburg (CCUG) (CCUG 39154)
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
104467Reimer, 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-ID54202.1
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