{
  "annotation_id": "ef5d38c0-b8ad-4c9a-bfe2-d919301dfc50",
  "date_created": "2026-03-02T18:01:31.488000Z",
  "date_modified": "2026-03-02T18:01:31.488000Z",
  "file_hash": "2b20e702b12f2c2688576ec31b121921fe830daff88e74401e0b25bb101e4eb5",
  "private": false,
  "record": {
    "abstract": "We showed in this paper that similarity network can be used as an powerful\ntools to study the relationship of tRNA genes. We constructed a network of 3719\ntRNA gene sequences using simplest alignment and studied its topology, degree\ndistribution and clustering coefficient. It is found that the behavior of the\nnetwork shift from fluctuated distribution to scale-free distribution when the\nsimilarity degree of the tRNA gene sequences increase. tRNA gene sequences with\nthe same anticodon identity are more self-organized than the tRNA gene\nsequences with different anticodon identities and form local clusters in the\nnetwork. An interesting finding in our studied is some vertices of the local\ncluster have a high connection with other local clusters, the probable reason\nis given. Moreover, a network constructed by the same number of random tRNA\nsequences is used to make comparisons. The relationships between properties of\nthe tRNA similarity network and the characters of tRNA evolutionary history are\ndiscussed.",
    "arxiv_id": "q-bio/0410023",
    "authors": [
      "Fangping Wei",
      "Sheng Li",
      "Hongru Ma"
    ],
    "categories": [
      "q-bio.MN",
      "cond-mat.stat-mech",
      "q-bio.PE"
    ],
    "title": "Network of tRNA Gene Sequences",
    "url": "https://arxiv.org/abs/q-bio/0410023"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "cf83a8e5-abbf-4ad6-9906-7fbfb24b14a1",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}