import pandas as pd
# 读取OTU表
otu_df = pd.read_csv('C:\\Users\\Administrator\\Desktop\\1.txt', header=None)
# 创建一个空的DataFrame来存储结果
df = pd.DataFrame(columns=["OTUID", "Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species"])
for i, otu in enumerate(otu_df[0]):
# 使用"|"拆分每个OTU
split_otu = otu.split("|")
# 初始化一个字典来存储每个分类级别
tax_dict = {"Kingdom": "Unassigned", "Phylum": "Unassigned", "Class": "Unassigned",
"Order": "Unassigned", "Family": "Unassigned", "Genus": "Unassigned",
"Species": "Unassigned"}
# 遍历拆分后的列表,并根据前缀将每个部分分配到相应的分类级别
for part in split_otu:
if part.startswith("k__"):
tax_dict["Kingdom"] = part
elif part.startswith("p__"):
tax_dict["Phylum"] = part
elif part.startswith("c__"):
tax_dict["Class"] = part
elif part.startswith("o__"):
tax_dict["Order"] = part
elif part.startswith("f__"):
tax_dict["Family"] = part
elif part.startswith("g__"):
tax_dict["Genus"] = part
elif part.startswith("s__"):
tax_dict["Species"] = part
# 删除分类级别前缀(如"k__")
tax_dict = {key: value.split("__")[1] if "__" in value else value for key, value in tax_dict.items()}
# 将结果添加到DataFrame中
df.loc[i] = ["ASV_" + str(i+1)] + list(tax_dict.values())
# 将结果保存为新的txt文件
df.to_csv('C:\\Users\\Administrator\\Desktop\\annotated_otu_table.txt', sep='\t', index=False)