Generate protein fasta

 python3 getProteinSequences_1.py > Proximity_labelling.fasta
 python3 getProteinSequences_2.py > ALFA_pulldown.fasta
 python3 getProteinSequences_3.py > schnittmenge.fasta

Using eggnog_env to generate the intial annotations

 mamba activate eggnog_env
 emapper.py -i Proximity_labelling.fasta -o eggnog_out_Proximity_labelling --cpu 60  #--resume
 emapper.py -i ALFA_pulldown.fasta -o eggnog_out_ALFA_pulldown --cpu 60
 emapper.py -i schnittmenge.fasta -o eggnog_out_schnittmenge --cpu 60

Using BLAST2GO to generate GO terms and EC terms

Using R to merge the two files blast2goannot.annot2 and eggnog_out.emapper.annotations.txt

 #mamba activate r_env
 # PREPARING go_terms and ec_terms: annot_* file: cut -f1-2 -d$'\t' blast2go_annot.annot2 > blast2go_annot.annot2_
 # PREPARING cp eggnog_out.emapper.annotations eggnog_out.emapper.annotations.txt. Note that the first last 3 lines starting with "#" should be removed; '#query' to query

 # Load required libraries
 library(openxlsx)  # For Excel file handling
 library(dplyr)     # For data manipulation
 library(clusterProfiler)  # For KEGG and GO enrichment analysis
 #library(org.Hs.eg.db)  # Replace with appropriate organism database
 #BiocManager::install("GO.db")
 #BiocManager::install("AnnotationDbi")
 library(GO.db)
 library(AnnotationDbi)

 # Step 1: Load the blast2go annotation file with a check for missing columns
 annot_df <- read.table("/home/jhuang/b2gWorkspace_schnittmenge/blast2go_annot.annot2_",
                     header = FALSE, sep = "\t", stringsAsFactors = FALSE, fill = TRUE)
 # If the structure is inconsistent, we can make sure there are exactly 3 columns:
 colnames(annot_df) <- c("GeneID", "Term")
 # Step 2: Filter and aggregate GO and EC terms as before
 go_terms <- annot_df %>%
 filter(grepl("^GO:", Term)) %>%
 group_by(GeneID) %>%
 summarize(GOs = paste(Term, collapse = ","), .groups = "drop")
 ec_terms <- annot_df %>%
 filter(grepl("^EC:", Term)) %>%
 group_by(GeneID) %>%
 summarize(EC = paste(Term, collapse = ","), .groups = "drop")

 eggnog_data <- read.delim("~/DATA/Data_Michelle_ProteinClustering/eggnog_out_schnittmenge.emapper.annotations.txt", header = TRUE, sep = "\t")

 # Perform the left joins and rename columns
 eggnog_data_updated <- eggnog_data %>%
 left_join(go_terms, by = "GeneID") %>%
 left_join(ec_terms, by = "GeneID")
 #%>% select(-EC.x, -GOs.x)
 #%>% rename(EC = EC.y, GOs = GOs.y)

 # Create a new workbook
 wb <- createWorkbook()
 # Add the complete dataset as the first sheet (with annotations)
 addWorksheet(wb, "Complete_Data")
 writeData(wb, "Complete_Data", eggnog_data_updated)
 # Save the workbook to a file
 saveWorkbook(wb, "Gene_with_Annotations_schnittmenge.xlsx", overwrite = TRUE)

 #Manually DELETE GOs.x and EC.x and rename GOs.y to GOs, EC.y to EC.

Draw group bar plot for relative occurrence

 import matplotlib.pyplot as plt
 import pandas as pd
 import numpy as np

 # COG annotations for the two gene sets
 proximity_labelling = [
     'D', 'S', 'K', 'S', 'T', 'S', 'S', 'F', 'E', 'J', 'G', 'C', 'C', 'S', 'I', 'L', 'I', 'S', 'S', 'G', 'O', 'H', 'IQ', 'L', 'C', 'H', 'C', 'NU', 'O', 'C',
     'M', 'S', 'IQ', 'G', 'S', 'M', 'M', '-', 'M', 'S', 'J', 'S', 'IQ', 'K', 'E', 'J', 'K', 'E', 'J', 'S', 'K', 'E', 'S', 'J', 'S', 'V', 'C', 'I', 'K', 'IQ',
     'M', 'S', 'T', 'P', 'C', 'J', 'S', 'E', 'C', 'K', 'H', 'S', 'H', 'H'
 ]

 alfa_pulldown = [
     'K', 'S', 'P', 'K', 'C', 'K', 'I', 'O', '-', 'F', 'C', 'P', 'HQ', '-', 'L', 'L', 'S', 'G', 'IQ', 'S'
 ]

 # Define all possible COG categories (including the combinations)
 all_cog_categories_ = ['J','A', 'K', 'L', 'B', 'D', 'Y', 'V', 'T', 'M', 'N', 'Z', 'W', 'U', 'O', 'C','G', 'E', 'F', 'H', 'I', 'P', 'Q', 'R', 'S']
 all_cog_categories = all_cog_categories_[::-1]

 # Define functional groups for COG categories
 functional_groups = {
     "INFORMATION STORAGE AND PROCESSING": ['J', 'A', 'K', 'L', 'B'],
     "CELLULAR PROCESSES AND SIGNALING": ['D', 'Y', 'V', 'T', 'M', 'N', 'Z', 'W', 'U', 'O'],
     "METABOLISM": ['C', 'G', 'E', 'F', 'H', 'I', 'P', 'Q'],
     "POORLY CHARACTERIZED": ['R', 'S']
 }

 # Function to count occurrences of each COG category in a gene set, handling combinations
 def count_cog_categories(gene_set):
     counts = {cog: gene_set.count(cog) for cog in all_cog_categories}

     # Treat combinations (e.g., IQ, HQ, NU) as belonging to both respective categories
     combination_categories = {
         'IQ': ['I', 'Q'],
         'HQ': ['H', 'Q'],
         'NU': ['N', 'U']
     }

     for comb, categories in combination_categories.items():
         comb_count = gene_set.count(comb)
         for category in categories:
             counts[category] += comb_count
         counts[comb] = 0  # Remove the combination itself as it is distributed

     return counts

 # Count COG categories for each gene set
 proximity_counts = count_cog_categories(proximity_labelling)
 alfa_counts = count_cog_categories(alfa_pulldown)

 # Calculate relative occurrence (percentage) of each COG category for both gene sets
 total_proximity = len(proximity_labelling)
 total_alfa = len(alfa_pulldown)

 # Calculate percentages for each COG category in both gene sets
 proximity_percentage = {cog: count / total_proximity * 100 for cog, count in proximity_counts.items()}
 alfa_percentage = {cog: count / total_alfa * 100 for cog, count in alfa_counts.items()}

 # Create a DataFrame to store the percentages for both gene sets
 df_percentage = pd.DataFrame({
     'Proximity Labelling (%)': [proximity_percentage.get(cog, 0) for cog in all_cog_categories],
     'ALFA Pulldown (%)': [alfa_percentage.get(cog, 0) for cog in all_cog_categories]
 }, index=all_cog_categories)

 ## Reverse the order of the COG categories
 #df_percentage = df_percentage[::-1]
 # Round the values to 1 decimal place
 df_percentage = df_percentage.round(1)

 # Save the relative occurrence data in a text file
 file_path = "COG_relative_occurrence.txt"
 df_percentage.to_csv(file_path, sep='\t', header=True, index=True)

 # Save the relative occurrence data in an Excel file
 file_path = "COG_relative_occurrence.xlsx"
 df_percentage.to_excel(file_path, header=True, index=True)

 # Display file path where the data was saved
 print(f"Relative occurrence data has been saved to {file_path}")

 # --------------------
 # Horizontal Grouped Bar Plot
 # --------------------

 # Plot a horizontal grouped bar chart
 fig, ax = plt.subplots(figsize=(12, 8))

 # Create the positions for each group of bars
 bar_height = 0.35
 index = np.arange(len(df_percentage))

 # Plot bars for both gene sets
 bar1 = ax.barh(index, df_percentage['Proximity Labelling (%)'], bar_height, label='Proximity Labelling', color='skyblue')
 bar2 = ax.barh(index + bar_height, df_percentage['ALFA Pulldown (%)'], bar_height, label='ALFA Pulldown', color='lightgreen')

 # Customize the plot
 ax.set_ylabel('COG Categories')
 ax.set_xlabel('Relative Occurrence (%)')
 ax.set_title('Relative COG Category Distribution')
 ax.set_yticks(index + bar_height / 2)
 ax.set_yticklabels(all_cog_categories)

 # Annotate functional groups
 #, fontweight='bold'
 for group, categories in functional_groups.items():
     # Find indices of the categories in this group
     group_indices = [all_cog_categories.index(cog) for cog in categories]
     # Add a label for the functional group
     ax.annotate(group, xy=(1, group_indices[0] + 0.5), xycoords='data', fontsize=9, color='black')

 # Add legend
 ax.legend()

 # Show the plot
 plt.tight_layout()
 plt.show()