{
  "annotation_id": "f5f0ff58-f23f-40b3-a443-eec79a349a89",
  "date_created": "2026-03-02T18:01:34.397000Z",
  "date_modified": "2026-03-02T18:01:34.397000Z",
  "file_hash": "17f0fe171d014e0388c750e7da2b2054b841efd5ffe3f891e12faeabc04fc4f7",
  "private": false,
  "record": {
    "abstract": "Recombination is an important event in the evolution of HIV. It affects the\nglobal spread of the pandemic as well as evolutionary escape from host immune\nresponse and from drug therapy within single patients. Comprehensive\ncomputational methods are needed for detecting recombinant sequences in large\ndatabases, and for inferring the parental sequences.\n  We present a hidden Markov model to annotate a query sequence as a\nrecombinant of a given set of aligned sequences. Parametric inference is used\nto determine all optimal annotations for all parameters of the model. We show\nthat the inferred annotations recover most features of established hand-curated\nannotations. Thus, parametric analysis of the hidden Markov model is feasible\nfor HIV full-length genomes, and it improves the detection and annotation of\nrecombinant forms.\n  All computational results, reference alignments, and C++ source code are\navailable at http://bio.math.berkeley.edu/recombination/.",
    "arxiv_id": "q-bio/0512019",
    "authors": [
      "Niko Beerenwinkel",
      "Colin N. Dewey",
      "Kevin M. Woods"
    ],
    "categories": [
      "q-bio.GN",
      "q-bio.QM"
    ],
    "title": "Parametric inference of recombination in HIV genomes",
    "url": "https://arxiv.org/abs/q-bio/0512019"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "30918ae7-c07c-4638-9ef4-984d284c1820",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}