237 lines
8.4 KiB
Python
Executable File
237 lines
8.4 KiB
Python
Executable File
"""
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PlantCARE API 客户端
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PlantCARE 是一个植物顺式作用调控元件数据库
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URL: http://bioinformatics.psb.ugent.be/webtools/plantcare/html/
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"""
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import requests
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import time
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from typing import Dict, List, Optional, Any
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from dataclasses import dataclass
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from pathlib import Path
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@dataclass
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class CisElement:
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"""顺式作用元件"""
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name: str
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sequence: str
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function: str
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position: Optional[int] = None
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strand: Optional[str] = None
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class PlantCAREClient:
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"""
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PlantCARE 数据库客户端
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用于查询启动子区域的顺式作用调控元件
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"""
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BASE_URL = "http://bioinformatics.psb.ugent.be/webtools/plantcare/html/"
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def __init__(self, rate_limit: float = 1.0):
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"""
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初始化客户端
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Args:
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rate_limit: 请求间隔（秒），避免过载服务器
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"""
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self.rate_limit = rate_limit
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self.last_request_time = 0
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self.session = requests.Session()
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self.session.headers.update({
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'User-Agent': 'bbav20311/1.0 (Research Tool)'
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})
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def _rate_limited_request(self, method: str, url: str, **kwargs) -> requests.Response:
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"""带速率限制的请求"""
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elapsed = time.time() - self.last_request_time
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if elapsed < self.rate_limit:
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time.sleep(self.rate_limit - elapsed)
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response = self.session.request(method, url, **kwargs)
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self.last_request_time = time.time()
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return response
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def search_elements(self, sequence: str, element_type: Optional[str] = None) -> List[CisElement]:
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"""
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搜索顺式作用元件
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Args:
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sequence: DNA 序列（启动子区域）
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element_type: 元件类型过滤（如 "light", "hormone", "stress"）
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Returns:
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顺式作用元件列表
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"""
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# PlantCARE 网页版没有公开 REST API
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# 这里实现基于本地数据库的查询或网页解析
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# 实际使用时可能需要本地部署 PlantCARE 数据库
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# 模拟返回（实际应替换为真实查询）
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elements = self._parse_sequence_for_elements(sequence)
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if element_type:
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elements = [e for e in elements if element_type.lower() in e.function.lower()]
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return elements
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def _parse_sequence_for_elements(self, sequence: str) -> List[CisElement]:
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"""
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解析序列中的顺式作用元件
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基于 PlantCARE 已知元件模式进行匹配
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"""
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# PlantCARE 常见元件数据库（简化版）
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element_patterns = {
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'CAAT-box': {'pattern': 'CCAAT', 'function': 'core promoter element'},
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'TATA-box': {'pattern': 'TATA', 'function': 'core promoter element'},
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'G-box': {'pattern': 'CACGTG', 'function': 'light responsive'},
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'ABRE': {'pattern': 'ACGTGG', 'function': 'abscisic acid responsive'},
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'W-box': {'pattern': 'TTGAC', 'function': 'WRKY binding site'},
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'WUN-motif': {'pattern': 'AAATTTCCT', 'function': 'wound responsive'},
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'MYB-binding': {'pattern': 'CNGTT', 'function': 'MYB transcription factor binding'},
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'MYC-binding': {'pattern': 'CACATG', 'function': 'MYC transcription factor binding'},
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'GT-1 motif': {'pattern': 'GGTTAA', 'function': 'light responsive'},
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'I-box': {'pattern': 'GATAA', 'function': 'light responsive'},
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}
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elements = []
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sequence_upper = sequence.upper()
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for name, info in element_patterns.items():
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pattern = info['pattern']
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# 支持简并碱基
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import re
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pattern_regex = pattern.replace('N', '[ATCG]')
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for match in re.finditer(pattern_regex, sequence_upper):
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elements.append(CisElement(
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name=name,
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sequence=sequence[match.start():match.end()],
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function=info['function'],
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position=match.start(),
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strand='+'
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))
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return elements
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def get_element_details(self, element_name: str) -> Optional[Dict[str, Any]]:
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"""
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获取元件详细信息
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Args:
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element_name: 元件名称（如 "G-box"）
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Returns:
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元件详细信息字典
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"""
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# 本地元件数据库
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element_db = {
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'CAAT-box': {
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'name': 'CAAT-box',
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'consensus': 'CCAAT',
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'function': 'Core promoter element, enhances transcription efficiency',
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'factors': ['CTF/NF1', 'CCAAT-binding factors'],
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'references': ['Mantovani, 1999'],
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},
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'TATA-box': {
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'name': 'TATA-box',
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'consensus': 'TATA(A/T)A(A/T)',
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'function': 'Core promoter element, transcription start site selection',
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'factors': ['TBP (TATA-binding protein)'],
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'references': ['Smale & Kadonaga, 2003'],
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},
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'G-box': {
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'name': 'G-box',
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'consensus': 'CACGTG',
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'function': 'Light-responsive element, also involved in ABA signaling',
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'factors': ['bZIP transcription factors', 'HY5'],
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'references': ['Menkens et al., 1995'],
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},
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'ABRE': {
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'name': 'ABRE',
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'consensus': 'ACGTGG/TC',
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'function': 'Abscisic acid responsive element',
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'factors': ['bZIP/ABF transcription factors'],
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'references': ['Nakashima & Yamaguchi-Shinozaki, 2013'],
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},
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'W-box': {
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'name': 'W-box',
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'consensus': 'TTGAC(C/T)',
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'function': 'WRKY transcription factor binding site, defense response',
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'factors': ['WRKY transcription factors'],
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'references': ['Eulgem et al., 2000'],
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},
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}
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return element_db.get(element_name)
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def analyze_promoter(self, promoter_sequence: str, upstream_length: int = 1500) -> Dict[str, Any]:
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"""
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全面分析启动子区域
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Args:
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promoter_sequence: 启动子序列
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upstream_length: 上游长度（默认1500bp）
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Returns:
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分析结果字典
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"""
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elements = self.search_elements(promoter_sequence)
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# 分类统计
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categories = {}
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for elem in elements:
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category = self._categorize_element(elem)
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if category not in categories:
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categories[category] = []
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categories[category].append(elem)
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return {
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'total_elements': len(elements),
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'categories': {k: len(v) for k, v in categories.items()},
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'elements_by_category': categories,
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'core_promoter_elements': [e for e in elements if 'core promoter' in e.function],
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'regulatory_elements': [e for e in elements if 'responsive' in e.function or 'binding' in e.function],
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'sequence_length': len(promoter_sequence),
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}
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def _categorize_element(self, element: CisElement) -> str:
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"""对元件进行分类"""
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function_lower = element.function.lower()
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if 'core promoter' in function_lower:
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return 'core_promoter'
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elif 'light' in function_lower:
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return 'light_responsive'
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elif 'hormone' in function_lower or 'acid' in function_lower:
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return 'hormone_responsive'
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elif 'stress' in function_lower or 'wound' in function_lower:
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return 'stress_responsive'
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elif 'binding' in function_lower:
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return 'tf_binding'
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else:
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return 'other'
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# 使用示例
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if __name__ == "__main__":
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client = PlantCAREClient()
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# 测试序列
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# 测试序列
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test_seq = "ATGCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCG"
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# 搜索元件
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elements = client.search_elements(test_seq)
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print(f"Found {len(elements)} cis-elements")
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for elem in elements:
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print(f" - {elem.name}: {elem.sequence} ({elem.function})")
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