app.py

# Install required dependencies if not present
import os
os.system("pip install streamlit pandas xlsxwriter")

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 with file names as separators
def create_excel(sequences_data, homorepeats, filenames):
    output = BytesIO()
    workbook = xlsxwriter.Workbook(output, {'in_memory': True})
    worksheet = workbook.add_worksheet()

    row = 0

    # Iterate through sequences data grouped by filenames
    for file_index, file_data in enumerate(sequences_data):
        filename = filenames[file_index]

        # Write filename as a separator row
        worksheet.write(row, 0, f"File: {filename}")
        row += 1

        # Write the header for the current file
        worksheet.write(row, 0, "Entry ID")
        worksheet.write(row, 1, "Protein Name")
        col = 2
        for repeat in sorted(homorepeats):
            worksheet.write(row, col, repeat)
            col += 1
        row += 1

        # Write data for each sequence in the current file
        for entry_id, protein_name, freq in file_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

        # Add an empty row as a separator between files
        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 = []
    filenames = []

    for file in uploaded_files:
        homorepeats, sequence_data = process_csv(file)
        if homorepeats is not None:
            all_homorepeats.update(homorepeats)
            all_sequences_data.append(sequence_data)
            filenames.append(file.name)

    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, filenames)

        # 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 file_index, file_data in enumerate(all_sequences_data):
                filename = filenames[file_index]
                for entry_id, protein_name, freq in file_data:
                    row = {"Filename": filename, "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)