primer3有提供批量设计引物的方法，话不多说，直接放脚本！

不过，在这之前要先保证python里有pandas、primer3-py和biopython这3个包

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import primer3 import pandas as pd import sys from Bio.Seq import Seq primer_condition 可以根据实际情况进行条件设置 global_args = { 'PRIMER_NUM_RETURN': 5, # 引物条数 'PRIMER_OPT_SIZE': 21, # 引物**长度 'PRIMER_MIN_SIZE': 18, # 引物最小长度 'PRIMER_MAX_SIZE': 24, # 引物最大长度 'PRIMER_OPT_TM': 60.0, # 引物**Tm值 'PRIMER_MIN_TM': 57.0, # 引物最小Tm值 'PRIMER_MAX_TM': 63.0, # 引物最大Tm值 'PRIMER_MIN_GC': 40.0, # 引物最小GC含量 'PRIMER_MAX_GC': 60.0, # 引物最大GC含量 'PRIMER_THERMODYNAMIC_OLIGO_ALIGNMENT': 1, # 该值设为1，则程序会使用热力学模型来计算oligos形成发夹结构和二聚体的可能性 'PRIMER_MAX_POLY_X': 5, # 所允许的单核苷酸重复的次数 'PRIMER_INTERNAL_MAX_POLY_X': 5, # internal所允许的单核苷酸重复的次数 'PRIMER_SALT_MONOVALENT': 50.0, # 单价盐离子浓度(mM) 'PRIMER_DNA_CONC': 50.0, # DNA产物浓度(mM) 'PRIMER_MAX_NS_ACCEPTED': 0, # 引物中允许的 N 的数目 'PRIMER_MAX_SELF_ANY': 8, # 引物自身进行反向互补 'PRIMER_MAX_SELF_END': 3, # 引物自身进行 3' 端反向互补形成引物二聚体 'PRIMER_PAIR_MAX_COMPL_ANY': 8, # left primer 和 right primer 序列的反向互补 'PRIMER_PAIR_MAX_COMPL_END': 3, # left primer 和 right primer 进行 3' 端反向互补形成引物二聚体 'PRIMER_PRODUCT_SIZE_RANGE': [400,600], # 指定引物产物的长度范围 'PRIMER_GC_CLAMP': 1 #要求 left primer 和 right primer 的 3' 末端序列中有连续指定数目的 Gs 或 Cs 碱基。 } function of read fasta def readfasta(lines): seq = [] index = [] current_seq = [] for line in lines: line = line.strip() if line.startswith(">"): if current_seq: seq.append("".join(current_seq)) current_seq = [] index.append(line[1:]) else: current_seq.append(line) if current_seq: seq.append("".join(current_seq)) return index, seq 读取序列 f = open(sys.argv[1], 'r') lines = f.readlines() (index, seq) = readfasta(lines) f.close() build table primer_df = pd.DataFrame() primer finder, dic -> datafrme for i in range(len(index)): seq_args = { 'SEQUENCE_ID': str(index[i]), 'SEQUENCE_TEMPLATE': str(seq[i]), 'SEQUENCE_INCLUDED_REGION': [0, len(seq[i]) - 1], } GeneID = str(index[i]) primer3_result = primer3.bindings.designPrimers(seq_args, global_args) change dic primer3_result_table_dict = {} for j in range(primer3_result["PRIMER_PAIR_NUM_RETURNED"]): primer_id = str(j) for key in primer3_result: if primer_id in key: info_tag = key.replace("_" + primer_id, "") try: primer3_result_table_dict[info_tag] except: primer3_result_table_dict[info_tag] = [] finally: primer3_result_table_dict[info_tag].append(primer3_result[key]) df_index = [] append dataframe for m in range(primer3_result["PRIMER_PAIR_NUM_RETURNED"]): df_index.append(GeneID + "_" + str(m + 1)) primer3_result_df = pd.DataFrame(primer3_result_table_dict, index=df_index) primer_df = primer_df._append(primer3_result_df) primer_df.to_csv(sys.argv[2], sep='\t') # 引物详细文件路径 target_df = primer_df.loc[:,["PRIMER_LEFT_SEQUENCE","PRIMER_RIGHT_SEQUENCE","PRIMER_LEFT","PRIMER_RIGHT"]] target_df['PRIMER_LEFT'] = target_df['PRIMER_LEFT'].apply(lambda x: x[0]) target_df['PRIMER_RIGHT'] = target_df['PRIMER_RIGHT'].apply(lambda x: x[0]) seq_dict = dict(zip(index, seq)) # 创建空的 DataFrame 来存储截取的序列片段和相关信息 result_df = pd.DataFrame(columns=['F', 'F-6','R', 'R-6', 'seq']) for i, row in target_df.iterrows(): seq_index = i.strip().split('_')[0] if seq_index in seq_dict: # 提取左引物的起始位置和右引物的终止位置 left_start = row['PRIMER_LEFT'] right_end = row['PRIMER_RIGHT'] # 截取序列片段 sequence = seq_dict[seq_index][left_start:right_end+1] # 计算 F, F-6, R-6 位置 F_minus_6_position = len(row['PRIMER_LEFT_SEQUENCE']) + 6 R_minus_6_position = len(row['PRIMER_RIGHT_SEQUENCE']) + 6 # 反向互补序列 R_minus_6_sequence = Seq(sequence[len(sequence)-R_minus_6_position:]).reverse_complement() # 将截取的序列片段和相关信息添加到结果 DataFrame 中 result_df = result_df._append({'F': row['PRIMER_LEFT_SEQUENCE'], 'F-6': sequence[:F_minus_6_position], 'R':row['PRIMER_RIGHT_SEQUENCE'], 'R-6': R_minus_6_sequence, 'seq': sequence},ignore_index=True) result_df.set_index(target_df.index, inplace=True) # 打印结果 DataFrame result_df.to_csv(sys.argv[3],sep = '\t') 

运行命令为：python3 primer.py {详细引物文件} {引物文件}