Update app.py

app.py

@@ -12,7 +12,6 @@ app = Flask(__name__)
 ssl._create_default_https_context = ssl._create_unverified_context
 
 def get_ready_for_primers(gene_symbol, species="human"):
-    """获取基因序列和外显子交界点信息（增强报错处理版）"""
     Entrez.email = "your_email@example.com"
     
     try:
@@ -21,32 +20,30 @@ def get_ready_for_primers(gene_symbol, species="human"):
         handle = Entrez.esearch(db="gene", term=search_term)
         record = Entrez.read(handle)
         if not record["IdList"]: 
-            return {"error": f"在 {species} 中未找到基因符号: {gene_symbol}"}
+            return {"error": f"未找到基因: {gene_symbol}"}
         gene_id = record["IdList"][0]
 
-        # 2. 尝试获取关联的 RefSeq mRNA (NM_)
-        # 增加多种筛选条件以提高匹配率
-        link_handle = Entrez.elink(
-            dbfrom="gene", 
-            db="nucleotide", 
-            id=gene_id, 
-            term="srcdb_refseq[prop] AND (mRNA[filter] OR RNA[filter])"
-        )
+        # 2. 改进的 Link 获取逻辑：兼容 NM_, NR_, XM_, XR_ 等所有转录本
+        # 不再使用复杂的 term 过滤，直接获取所有关联的核苷酸序列
+        link_handle = Entrez.elink(dbfrom="gene", db="nucleotide", id=gene_id)
         link_record = Entrez.read(link_handle)
         
-        # 检查是否有跳转结果
-        if not link_record[0]["LinkSetDb"]:
-            return {"error": f"基因 {gene_symbol} (ID: {gene_id}) 暂无已证实的 RefSeq mRNA 序列 (NM_)，无法进行 qPCR 引物设计。"}
-
-        # 3. 提取核苷酸 ID
-        nucl_id = link_record[0]["LinkSetDb"][0]["Link"][0]["Id"]
+        nucl_id = None
+        # 深度遍历：寻找存储在 'gene_nucleotide' 链接中的 ID
+        for link_set in link_record[0].get("LinkSetDb", []):
+            # 通常 RefSeq 序列存储在名为 'gene_nucleotide' 或 'gene_nucleotide_refseq' 的链接中
+            if "nucleotide" in link_set["DbTo"]:
+                nucl_id = link_set["Link"][0]["Id"]
+                break
         
-        # 4. 下载 GenBank 数据
+        if not nucl_id:
+            return {"error": f"基因 {gene_symbol} 找不到关联的核苷酸序列 ID"}
+
+        # 3. 下载数据
         handle = Entrez.efetch(db="nucleotide", id=nucl_id, rettype="gb", retmode="text")
         seq_record = SeqIO.read(handle, "genbank")
-        seq_type = "mRNA (编码)" if seq_record.id.startswith("NM_") else "ncRNA (非编码)"
         
-        # 提取外显子（此处逻辑保持不变）
+        # 4. 提取外显子（逻辑同前）
         junctions = []
         current_pos = 0
         for feature in seq_record.features:
@@ -55,13 +52,11 @@ def get_ready_for_primers(gene_symbol, species="human"):
                 current_pos += (end - start)
                 junctions.append(int(current_pos))
         
-        if junctions: 
-            junctions.pop() 
+        if junctions: junctions.pop() 
 
         return {
             "symbol": gene_symbol,
             "nm_id": seq_record.id,
-            "type": seq_type, # 传给前端展示
             "sequence": str(seq_record.seq),
             "junctions": junctions
         }