eggnog后kegg结果提取和注释

首先进入KEGG BRITE: KEGG Orthology (KO)

下载json文件

用python处理一下

import json
import re
import osos.chdir("C:/Users/fordata/Downloads/")
with open("ko00001.json","r") as f:fj = f.read()kojson = json.loads(fj)with open("newKegg.tsv", "w") as k:for i in kojson['children']:ii = i['name'].replace(" ", "\t", 1)for j in i['children']:jj = j['name'].replace(" ", "\t", 1)for m in j['children']:if re.findall(r"ko\d{5}", m['name']):mm = "ko" + m['name'].replace(" ", "\t", 1)else:mm = m['name'].replace(" ", "\t", 1)try:for n in m['children']:if ";" in n['name']:nn = n['name'].replace(" ", "\t", 1).replace("; ", "\t", 1)else:nn = n['name'].replace(" ", "\t \t", 1)k.write(ii + "\t" + jj + "\t" + mm + "\t" + nn + "\n")except:nn = " \t \t "k.write(ii+"\t"+jj+"\t"+mm+"\t"+nn+"\n")

得到结果 

写个代码看看把keggKO和tpm关联起来

#! /usr/bin/env python
#########################################################
# mix eggnog(kegg) result with tpm
# written by PeiZhong in IFR of CAASimport argparse
import pandas as pd# Parse command-line arguments
parser = argparse.ArgumentParser(description='Mix eggnog(kegg) result with TPM')
parser.add_argument('--result', "-r", required=True, help='Path to eggnog result file')
parser.add_argument('--tpm', "-t", required=True, help='Path to TPM table file')
parser.add_argument('--out', "-o", required=True, help='Path to output file')args = parser.parse_args()# Step 1: Read input files
print("Reading input files")# Read dbcan result
df_result = {}
df_kegg = set()  # Use a set to store unique CAZy families
with open(args.result, "r") as f:for line in f:if "#" not in line:protein_id = line.split("\t")[0]kegg_str = line.split("\t")[11]if "-" != kegg_str:df_result[protein_id] = kegg_str# Extract CAZy families and remove duplicatesfamilies = set(entry.split(":")[1].strip() for entry in kegg_str.split(','))df_kegg.update(families)  # Add unique families to the global set# Read TPM file
df_tpm = pd.read_csv(args.tpm, sep='\t')# Step 2: Process dbcan results and calculate TPM sums for each sample
print("Processing dbcan results and calculating TPM sums for each sample")# Initialize a dictionary to store TPM sums for each CAZy family and sample
kegg_tpm_sums = {ko: {sample: 0.0 for sample in df_tpm.columns[1:]} for ko in df_kegg}# Convert TPM table to a dictionary for faster lookup
tpm_dict = df_tpm.set_index(df_tpm.columns[0]).to_dict(orient='index')# Process each protein in the dbcan result
for protein_id, kegg_str in df_result.items():# Convert protein ID to gene ID by removing trailing "_number"if "_" in protein_id:gene_id = protein_id.rsplit("_", 1)[0]  # Split from right on the last "_"else:print(f"Warning: Protein ID {protein_id} has no underscore, using as gene ID")gene_id = protein_id# Get TPM values for this geneif gene_id not in tpm_dict:print(f"Warning: No TPM values found for {gene_id} (protein {protein_id})")continuetpm_values = tpm_dict[gene_id]# Extract unique CAZy families for this proteinfamilies = set(entry.split(':')[1].strip() for entry in kegg_str.split(','))# Update TPM sums for each unique CAZy familyfor family in families:if family in kegg_tpm_sums:for sample in df_tpm.columns[1:]:kegg_tpm_sums[family][sample] += tpm_values[sample]else:# Dynamically add new CAZy familieskegg_tpm_sums[family] = {sample: tpm_values[sample] for sample in df_tpm.columns[1:]}# Create and save output DataFrame
output_df = pd.DataFrame.from_dict(kegg_tpm_sums, orient='index')
output_df.index.name = 'CAZy_Family'
output_df.to_csv(args.out, sep='\t', float_format='%.2f')  # Round to 2 decimal places
print(f"Results saved to {args.out}")

得到 

kegg的对应level，在excel钟使用vlookup函数对应即可