Create homorepeat_app.py

homorepeat_app.py

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+import streamlit as st
+import pandas as pd
+import xlsxwriter
+from io import BytesIO
+from collections import defaultdict
+
+# Function to find repeated amino acids in the protein sequence
+def find_homorepeats(protein):
+    n = len(protein)
+    freq = defaultdict(int)
+    i = 0
+
+    while i < n:
+        curr = protein[i]
+        repeat = ""
+        while i < n and curr == protein[i]:
+            repeat += protein[i]
+            i += 1
+
+        # Only consider repeats of length > 1
+        if len(repeat) > 1:
+            freq[repeat] += 1
+
+    return freq
+
+# Function to process a single CSV file and return its analysis
+def process_csv(file):
+    df = pd.read_csv(file)
+    if len(df.columns) < 3:
+        st.error(f"Error: The file must have at least three columns: ID, Protein Name, Sequence")
+        return None
+
+    # Storing entry ID, protein name, and sequence
+    sequences = []
+    for _, row in df.iterrows():
+        entry_id = str(row[0])
+        protein_name = str(row[1])
+        sequence = str(row[2]).replace('"', '').replace(' ', '')
+        sequences.append((entry_id, protein_name, sequence))
+
+    # Analyzing homorepeats in the sequences
+    homorepeats = set()
+    sequence_data = []
+    for entry_id, protein_name, sequence in sequences:
+        freq = find_homorepeats(sequence)
+        homorepeats.update(freq.keys())  # Collect unique homorepeats
+        sequence_data.append((entry_id, protein_name, freq))
+
+    return homorepeats, sequence_data
+
+# Function to generate and download Excel workbook
+def create_excel(sequences_data, homorepeats):
+    output = BytesIO()
+    workbook = xlsxwriter.Workbook(output, {'in_memory': True})
+    worksheet = workbook.add_worksheet()
+
+    # Write the header
+    worksheet.write(0, 0, "Entry ID")
+    worksheet.write(0, 1, "Protein Name")
+    col = 2
+    for repeat in sorted(homorepeats):
+        worksheet.write(0, col, repeat)
+        col += 1
+
+    # Write data for each sequence
+    row = 1
+    for entry_id, protein_name, freq in sequences_data:
+        worksheet.write(row, 0, entry_id)
+        worksheet.write(row, 1, protein_name)
+        col = 2
+        for repeat in sorted(homorepeats):
+            worksheet.write(row, col, freq.get(repeat, 0))
+            col += 1
+        row += 1
+
+    workbook.close()
+    output.seek(0)
+    return output
+
+# Streamlit UI components
+st.title("Protein Homorepeat Analysis")
+
+# Step 1: Upload CSV Files
+uploaded_files = st.file_uploader("Upload CSV files", accept_multiple_files=True, type=["csv"])
+
+# Step 2: Process files and display results
+if uploaded_files:
+    all_homorepeats = set()
+    all_sequences_data = []
+
+    for file in uploaded_files:
+        homorepeats, sequence_data = process_csv(file)
+        if homorepeats is not None:
+            all_homorepeats.update(homorepeats)
+            all_sequences_data.extend(sequence_data)
+
+    if all_sequences_data:
+        st.success(f"Processed {len(uploaded_files)} files successfully!")
+
+        # Step 3: Generate and download the Excel report
+        excel_file = create_excel(all_sequences_data, all_homorepeats)
+
+        # Download the Excel file
+        st.download_button(
+            label="Download Excel file",
+            data=excel_file,
+            file_name="protein_homorepeat_results.xlsx",
+            mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+        )
+
+        # Step 4: Display summary table
+        if st.checkbox("Show Results Table"):
+            # Convert the sequences data into a DataFrame for easy display
+            rows = []
+            for entry_id, protein_name, freq in all_sequences_data:
+                row = {"Entry ID": entry_id, "Protein Name": protein_name}
+                row.update({repeat: freq.get(repeat, 0) for repeat in sorted(all_homorepeats)})
+                rows.append(row)
+
+            result_df = pd.DataFrame(rows)
+            st.dataframe(result_df)