download go terms from Gene Ontology https://geneontology.org/docs/download-ontology/

 import obonet

 # Load the OBO file
 url = 'http://purl.obolibrary.org/obo/go/go-basic.obo'
 graph = obonet.read_obo(url)

 # Open a file to write to
 with open('go_terms.csv', 'w') as f:
     f.write("id,name,namespace\n")  # Write header
     for node_id, data in graph.nodes(data=True):
         name = data.get('name', '')
         namespace = data.get('namespace', '')
         f.write(f"{node_id}\t{name}\t{namespace}\n")  # Write data

prepare the blastp output

 python3 filter_uniref_by_taxid.py uniref100.fasta uniref100_taxid1282.fasta

 makeblastdb -in uniref90_taxid1282.fasta -dbtype prot
 makeblastdb -in uniref50_taxid1282.fasta -dbtype prot;
 makeblastdb -in uniref100_taxid1282.fasta -dbtype prot;

 # -strand both -max_target_seqs 1 -taxidlist taxid_list.txt
 blastp -query 1585_CDS.fasta -db /home/jhuang/REFs/uniref100_taxid1282.fasta -out 1585_CDS_on_uniref100.blastp -num_threads 100 -outfmt 6 -evalue 1.0e-30
 grep -v "UniRef100_UPI" 1585_CDS_on_uniref100.blastp > 1585_CDS_on_uniref100_.blastp

 python3 add_RefSeq-ID.py 1585_genes_annotated_with_GO.csv 1585.csv 1585_genes_annotated_with_GO_RefSeq-ID.csv
 python3 add_UniProt-ID.py #Input are 1585_CDS_on_uniref100_.blastp and 1585_genes_annotated_with_GO_RefSeq-ID.csv; Output is 1585_genes_annotated_with_GO_RefSeq-ID_UniProt-ID.csv.
 python3 add_Translation.py 1585_genes_annotated_with_GO_RefSeq-ID_UniProt-ID.csv 1585.csv 1585_genes_annotated_with_GO_RefSeq-ID_UniProt-ID_Translation.csv

 # check if they are running correctly
 cut -f1 -d',' 1585_genes_annotated_with_GO_NCBI-ID.csv > temp1
 cut -f1 -d',' 1585_genes_annotated_with_GO_NCBI-ID_UniProt-ID_Translation.csv > temp2
 diff temp1 temp2

code of filter_uniref_by_taxid.py

 import sys

 def filter_uniref50(input_file, output_file):
 with open(input_file, 'r') as in_handle:
         with open(output_file, 'w') as out_handle:
         record_started = False
         for line in in_handle:
                 if line.startswith(">"):
                 if "TaxID=1282 " in line:
                         record_started = True
                         out_handle.write(line)
                 else:
                         record_started = False
                 elif record_started:
                 out_handle.write(line)

 if __name__ == "__main__":
 # Check command-line arguments
 if len(sys.argv) != 3:
         print("Usage: python3 filter_uniref_by_taxid.py 

code of add_RefSeq-ID.py

 import pandas as pd
 import sys

 def main(input_file1, input_file2, output_file):
 # Load the first table
 table1 = pd.read_csv(input_file1)

 # Load the second table
 table2 = pd.read_csv(input_file2)

 # Merge the tables on SeqName from table1 and Locus Tag from table2
 merged_table = pd.merge(table1, table2[['Locus Tag', 'inference']], left_on='SeqName', right_on='Locus Tag', how='left')

 # Drop the extra 'Locus Tag' column that is redundant after merge
 merged_table.drop(columns=['Locus Tag'], inplace=True)

 # Save the merged table to a new CSV file
 merged_table.to_csv(output_file, index=False)

 if __name__ == "__main__":
 if len(sys.argv) != 4:
         print("Usage: python script.py 

code of add_UniProt-ID.py

 import csv

 # Read blastp results and create a dictionary mapping CDS IDs to lists of UniRef100 IDs
 def read_blastp_results(blastp_file):
 cds_to_uniref100 = {}
 with open(blastp_file, 'r') as blastp_handle:
         blastp_reader = csv.reader(blastp_handle, delimiter='\t')
         for row in blastp_reader:
         cds_id, uniref100_id = row[0], row[1]
         if cds_id not in cds_to_uniref100:
                 cds_to_uniref100[cds_id] = []
         cds_to_uniref100[cds_id].append(uniref100_id)
 return cds_to_uniref100

 # Add UniRef100 IDs as the last column in the input CSV file
 def add_uniref100_column(input_file, output_file, cds_to_uniref100):
 with open(input_file, 'r') as input_handle:
         with open(output_file, 'w', newline='') as output_handle:
         reader = csv.reader(input_handle)
         writer = csv.writer(output_handle)
         for row in reader:
                 cds_id = row[0]
                 if cds_id in cds_to_uniref100:
                 uniref100_ids = '|'.join(cds_to_uniref100[cds_id])
                 row.append(uniref100_ids)
                 else:
                 row.append("")  # If no UniRef100 IDs found, add empty string
                 writer.writerow(row)

 if __name__ == "__main__":
 blastp_file = "1585_CDS_on_uniref100_.blastp"  # Replace with your blastp results file
 input_file = "1585_genes_annotated_with_GO_NCBI-ID.csv"
 output_file = "1585_genes_annotated_with_GO_NCBI-ID_UniProt-ID.csv"

 # Read blastp results and create dictionary mapping CDS IDs to lists of UniRef100 IDs
 cds_to_uniref100 = read_blastp_results(blastp_file)

 # Add UniRef100 IDs as the last column in the input CSV file
 add_uniref100_column(input_file, output_file, cds_to_uniref100)

 print("UniRef100 IDs added to the input CSV file:", output_file)

code of add_Translation.py

 import pandas as pd
 import sys

 def main(input_file1, input_file2, output_file):
 # Load the first table
 table1 = pd.read_csv(input_file1)

 # Load the second table
 table2 = pd.read_csv(input_file2)

 # Merge the tables on SeqName from table1 and Locus Tag from table2
 merged_table = pd.merge(table1, table2[['Locus Tag', 'Translation']], left_on='SeqName', right_on='Locus Tag', how='left')

 # Drop the extra 'Locus Tag' column that is redundant after merge
 merged_table.drop(columns=['Locus Tag'], inplace=True)

 # Save the merged table to a new CSV file
 merged_table.to_csv(output_file, index=False)

 if __name__ == "__main__":
 if len(sys.argv) != 4:
         print("Usage: python script.py