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  "Title": "Chaos Game Representation for Phylogenetic Analysis",
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  "Date": "2026-06-12",
  "Authors@R": "person(\"Amarinder Singh\", \"Thind\", \nemail = \"thind.amarinder@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-4592-0380\"))",
  "Description": "An alignment-free phylogenetic analysis method for viral\ngenomes using Chaos Game Representation (CGR), a technique\nbased on statistical physics concepts. Viruses exhibit high\nmutation rates, facilitating rapid evolution and emergence of\nnew species, subspecies, strains, and recombinant forms.\nAccurate classification is crucial for understanding viral\nevolution and therapeutic development. Traditional phylogenetic\nmethods require sequence alignment, which is computationally\nintensive. CGRphylo2 addresses this by implementing CGR-based\nwhole-genome comparison that is fast, accurate, and\ncomputationally efficient. The package successfully classifies\nclosely related viral lineages (demonstrated on SARS-CoV-2\nlineages A and B), identifies recombinants (such as the XBB\nvariant), and distinguishes multiple strains simultaneously. It\nprocesses sequences 5-13.7x faster than alignment-based methods\n(Clustal-Omega) with linear computational scaling. As a k-mer\nbased approach, it enables simultaneous comparison of numerous\nclosely-related sequences of different lengths. The package\ncreates frequency matrices for distance calculations and\nphylogenetic tree construction, with outputs compatible with\nstandard formats (MEGA, PHYLIP, Newick). Methods are based on\nThind and Sinha (2023)\n<doi:10.2174/0113892029264990231013112156>.",
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      "page": "calculateDistanceMatrix",
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      "title": "Introduction to CGRphylo2: Chaos Game Representation for Phylogenetic Analysis",
      "author": "Amarinder Singh Thind",
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        "About CGRphylo2",
        "Why CGRphylo?",
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        "References"
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      "created": "2026-02-13 05:55:21",
      "modified": "2026-06-07 01:41:18",
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