{
  "annotation_id": "2ffe9f55-f7b1-44b8-b051-882eaee37a98",
  "date_created": "2026-03-02T18:01:35.799000Z",
  "date_modified": "2026-03-02T18:01:35.799000Z",
  "file_hash": "5a3c6752574c8051e36bee374bae6268a3cc31834445d21d150f09d4de6f288e",
  "private": false,
  "record": {
    "abstract": "Alignment algorithms usually rely on simplified models of gaps for\ncomputational efficiency. Based on an isomorphism between alignments and\nphysical helix-coil models, we show in statistical mechanics that alignments\nwith realistic laws for gaps can be computed with fast algorithms. Improved\nperformances of probabilistic alignments with realistic models of gaps are\nillustrated. Probabilistic and optimization formulations are compared, with\npotential implications in many fields and perspectives for computationally\nefficient extensions to Markov models with realistic long-range interactions.",
    "arxiv_id": "q-bio/0606010",
    "authors": [
      "E. Yeramian",
      "E. Debonneuil"
    ],
    "categories": [
      "q-bio.GN",
      "cond-mat.dis-nn",
      "cond-mat.stat-mech",
      "physics.bio-ph",
      "physics.comp-ph"
    ],
    "doi": "10.1103/PhysRevLett.98.078101",
    "title": "Probabilistic sequence alignments: realistic models with efficient   algorithms",
    "url": "https://arxiv.org/abs/q-bio/0606010"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "0fdea7a1-17e9-4cd6-b409-93a2ccd66a6b",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}