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Andrzej Zielezinski edited this page Oct 9, 2024 · 2 revisions

💎 Accurate ANI calculations

Vclust uses a Lempel-Ziv-based pairwise sequence aligner (LZ-ANI) for ANI calculation. LZ-ANI achieves high sensitivity in detecting matched and mismatched nucleotides, ensuring accurate ANI determination. Its efficiency comes from a simplified indel handling model, making LZ-ANI magnitudes faster than alignment-based tools (e.g., BLASTn, MegaBLAST) while maintaining comparable accuracy to the most sensitive BLASTn searches.

📐 Multiple similarity measures

Vclust offers multiple similarity measures between two genome sequences:

  • ANI: The number of identical nucleotides across local alignments divided by the total length of the alignments.
  • Global ANI (gANI): The number of identical nucleotides across local alignments divided by the length of the query/reference genome.
  • Total ANI (tANI): The number of identical nucleotides between query-reference and reference-query genomes divided by the sum length of both genomes. tANI is equivalent to the VIRIDIC's intergenomic similarity.
  • Coverage (alignment fraction): The proportion of the query/reference sequence aligned with the reference/query sequence.
  • Number of local alignments: The number of local alignments between the two genome sequences.
  • Ratio between genome lengths: The length of the shorter genome divided by the longer one.

🌟 Multiple clustering algorithms

Vclust provides six clustering algorithms tailored to various scenarios, including taxonomic classification and dereplication of viral genomes.

  • Single-linkage
  • Complete-linkage
  • UCLUST
  • CD-HIT (Greedy incremental)
  • Greedy set cover (adopted from MMseqs2)
  • Leiden algorithm [optional]

🔥 Speed and efficiency

Vclust uses three efficient C++ tools - Kmer-db, LZ-ANI, Clusty - for prefiltering, aligning, calculating ANI, and clustering viral genomes. This combination enables the processing of millions of virus genomes within a few hours on a mid-range workstation.

🌎 Web service

For datasets containing up to 1000 viral genomes, Vclust is available at http://www.vclust.org.

Table of contents

Vclust User Guide

  1. Features
  2. Installation
  3. Quick Start
  4. Usage
    1. Input data
    2. Prefilter
    3. Align
    4. Cluster
    5. Deduplicate
  5. Optimizing sensitivity and resource usage
    1. Prefilter
  6. Use cases
    1. Classify viruses into species and genera following ICTV standards
    2. Assign viral contigs into vOTUs following MIUViG standards
    3. Dereplicate viral contigs into representative genomes
    4. Calculate pairwise similarities between all-versus-all genomes
    5. Deduplicate (remove identical sequences) across multiple datasets
    6. Process large dataset of diverse virus genomes (IMG/VR)
    7. Process large dataset of highly redundant virus genomes
    8. Cluster plasmid genomes into pOTUs
  7. FAQ: Frequently Asked Questions
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