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@Jessime
Created January 29, 2022 00:00
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Solving the second degree neighbor problem with Python 3.10
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{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"id": "0e928ad0",
"metadata": {
"ExecuteTime": {
"end_time": "2022-01-28T23:33:19.195967Z",
"start_time": "2022-01-28T23:33:19.053420Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
">s1\r\n",
"AAA\r\n",
">s2\r\n",
"AAG\r\n",
">s3\r\n",
"AGG\r\n",
">s4\r\n",
"TTT\r\n"
]
}
],
"source": [
"cat ~/Code/jessime/notebooks/sites.fa"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "b26a63da",
"metadata": {
"ExecuteTime": {
"end_time": "2022-01-28T23:35:00.772046Z",
"start_time": "2022-01-28T23:35:00.764197Z"
}
},
"outputs": [],
"source": [
"from collections import defaultdict\n",
"from pathlib import Path\n",
"from itertools import pairwise\n",
"\n",
"\n",
"def get_sequences(input_fasta):\n",
" \"\"\"Returns sequences from a fasta formatted str.\"\"\"\n",
" return Path(input_fasta).read_text().splitlines()[1::2]\n",
"\n",
"\n",
"def are_similar(seq1, seq2, limit=5):\n",
" \"\"\"Returns True if two sequences are similar above some threshold.\"\"\"\n",
" # prewritten\n",
" return sum(1 for c1, c2 in zip(seq1, seq2) if c1 == c2) >= limit\n",
"\n",
"\n",
"def build_graph(seqs):\n",
" \"\"\"Returns a graph representing which sequences are similar to each other.\"\"\"\n",
" graph = defaultdict(set)\n",
" for seq1, seq2 in pairwise(seqs):\n",
" if are_similar(seq1, seq2, 2):\n",
" graph[seq1].add(seq2)\n",
" graph[seq2].add(seq1)\n",
" return graph\n",
"\n",
"\n",
"def is_second_degree(graph, seq1, seq2):\n",
" \"\"\"Returns True if a sequences is a second degree neighbor to another sequence.\"\"\"\n",
" if any((seq1 not in graph, seq2 not in graph, seq2 in graph[seq1])):\n",
" return False\n",
" return bool(graph[seq1] & graph[seq2])\n",
"\n",
"\n",
"def run(input_fasta, seq1, seq2):\n",
" seqs = get_sequences(input_fasta)\n",
" graph = build_graph(seqs)\n",
" return is_second_degree(graph, seq1, seq2)\n",
"\n",
" \n",
"assert run('sites.fa', \"AAA\", \"AGG\")\n",
"assert run('sites.fa', \"AGG\", \"AAA\")\n",
"assert not run('sites.fa', \"AAA\", \"AAG\")\n",
"assert not run('sites.fa', \"AAG\", \"AGG\")\n",
"assert not run('sites.fa', \"AAA\", \"TTT\")\n",
"assert not run('sites.fa', \"HEY\", \"TTT\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9dea57b3",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "6f561ee6",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.0"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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