VICTOR

VICTOR infers phylogenetic relationships and classifies prokaryotic viruses (bacterial and archaeal viruses) from genome sequence data to support viral taxonomy and evolutionary analyses.


Key Features:

  • In Silico Framework: Employs a computational framework aligned with phylogenetic systematics and leverages a comprehensive reference dataset of officially classified viruses.
  • Phylogenetic Analysis: Generates phylogenetic trees that exhibit high concordance with existing classifications, with most taxa supported as monophyletic but with reduced resolution at the family level.
  • Distance Threshold Optimization: Applies distance thresholds optimized to maximize agreement with established taxonomy, identifying phylogenetically coherent clusters for taxon delineation.
  • Identification of Novel Taxa: Detects novel species, genera, subfamilies, and families through analysis of an expanded dataset of over 4,000 genomes from public databases.

Scientific Applications:

  • Biogeochemical Cycle Research: Supports investigation of prokaryotic virus roles in biogeochemical processes by clarifying evolutionary relationships and taxonomy.
  • Therapeutic Development: Informs development of phage-based therapeutics against multi-resistant pathogens by identifying and classifying novel viral taxa.
  • Viral Evolution Studies: Enables studies of viral evolution and diversity by providing phylogenetic context and a taxonomic framework for prokaryotic viruses.

Methodology:

Generates phylogenetic trees, applies distance thresholds optimized for maximal taxonomic agreement, and compares results to a reference dataset of officially classified viruses; analyses include an expanded dataset of over 4,000 genomes from public databases.

Topics

Details

Tool Type:
web application
Operating Systems:
Linux, Windows, Mac
Added:
6/14/2018
Last Updated:
11/25/2024

Operations

Publications

Meier-Kolthoff JP, Göker M. VICTOR: genome-based phylogeny and classification of prokaryotic viruses. Bioinformatics. 2017;33(21):3396-3404. doi:10.1093/bioinformatics/btx440. PMID:29036289. PMCID:PMC5860169.

PMID: 29036289
PMCID: PMC5860169
Funding: - Deutsche Forschungsgemeinschaft: TRR 51

Documentation