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app (2).py

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+import os
+from dotenv import load_dotenv
+from pymongo import MongoClient
+# os.system("pip install streamlit pandas xlsxwriter openpyxl pymongo matplotlib seaborn")
+
+import streamlit as st
+import pandas as pd
+import xlsxwriter
+from io import BytesIO
+from collections import defaultdict
+import hashlib
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# load_dotenv()
+
+# Get MongoDB URI from environment
+mongo_uri = os.environ.get("MONGODB_URI")
+
+# MongoDB Setup
+try:
+    from pymongo import MongoClient
+    client = MongoClient(mongo_uri)
+    db = client['BTP_DB']
+    results_collection = db['protein_results']
+except:
+    results_collection = None
+
+# Utility Functions
+def is_homo_repeat(s):
+    return all(c == s[0] for c in s)
+
+def hash_sequence(sequence):
+    return hashlib.md5(sequence.encode()).hexdigest()
+
+@st.cache_data(show_spinner=False)
+def fragment_protein_sequence(sequence, max_length=1000):
+    return [sequence[i:i+max_length] for i in range(0, len(sequence), max_length)]
+
+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
+        if len(repeat) > 1:
+            freq[repeat] += 1
+    return freq
+
+def find_hetero_amino_acid_repeats(sequence):
+    repeat_counts = defaultdict(int)
+    for length in range(2, len(sequence) + 1):
+        for i in range(len(sequence) - length + 1):
+            substring = sequence[i:i+length]
+            repeat_counts[substring] += 1
+    return {k: v for k, v in repeat_counts.items() if v > 1}
+
+def check_boundary_repeats(fragments, final_repeats, overlap=50):
+    for i in range(len(fragments) - 1):
+        left_overlap = fragments[i][-overlap:]
+        right_overlap = fragments[i + 1][:overlap]
+        overlap_region = left_overlap + right_overlap
+        boundary_repeats = find_hetero_amino_acid_repeats(overlap_region)
+        for substring, count in boundary_repeats.items():
+            if any(aa in left_overlap for aa in substring) and any(aa in right_overlap for aa in substring):
+                final_repeats[substring] += count
+    return final_repeats
+
+def find_new_boundary_repeats(fragments, final_repeats, overlap=50):
+    new_repeats = defaultdict(int)
+    for i in range(len(fragments) - 1):
+        left_overlap = fragments[i][-overlap:]
+        right_overlap = fragments[i + 1][:overlap]
+        overlap_region = left_overlap + right_overlap
+        boundary_repeats = find_hetero_amino_acid_repeats(overlap_region)
+        for substring, count in boundary_repeats.items():
+            if any(aa in left_overlap for aa in substring) and any(aa in right_overlap for aa in substring):
+                if substring not in final_repeats:
+                    new_repeats[substring] += count
+    return new_repeats
+
+def get_or_process_sequence(sequence, analysis_type, overlap=50):
+    if results_collection is None:
+        return {}
+    hash_input = f"{sequence}_{analysis_type}"
+    sequence_hash = hash_sequence(hash_input)
+    cached = results_collection.find_one({"_id": sequence_hash})
+    if cached:
+        return cached["repeats"]
+
+    fragments = fragment_protein_sequence(sequence)
+    final_repeats = defaultdict(int)
+
+    if analysis_type == "Hetero":
+        for fragment in fragments:
+            fragment_repeats = find_hetero_amino_acid_repeats(fragment)
+            for k, v in fragment_repeats.items():
+                final_repeats[k] += v
+        final_repeats = check_boundary_repeats(fragments, final_repeats, overlap)
+        new_repeats = find_new_boundary_repeats(fragments, final_repeats, overlap)
+        for k, v in new_repeats.items():
+            final_repeats[k] += v
+        final_repeats = {k: v for k, v in final_repeats.items() if not is_homo_repeat(k)}
+
+    elif analysis_type == "Homo":
+        final_repeats = find_homorepeats(sequence)
+
+    elif analysis_type == "Both":
+        hetero_repeats = defaultdict(int)
+        for fragment in fragments:
+            fragment_repeats = find_hetero_amino_acid_repeats(fragment)
+            for k, v in fragment_repeats.items():
+                hetero_repeats[k] += v
+        hetero_repeats = check_boundary_repeats(fragments, hetero_repeats, overlap)
+        new_repeats = find_new_boundary_repeats(fragments, hetero_repeats, overlap)
+        for k, v in new_repeats.items():
+            hetero_repeats[k] += v
+        hetero_repeats = {k: v for k, v in hetero_repeats.items() if not is_homo_repeat(k)}
+        homo_repeats = find_homorepeats(sequence)
+        final_repeats = homo_repeats.copy()
+        for k, v in hetero_repeats.items():
+            final_repeats[k] += v
+
+    results_collection.insert_one({
+        "_id": sequence_hash,
+        "sequence": sequence,
+        "analysis_type": analysis_type,
+        "repeats": dict(final_repeats)
+    })
+    return final_repeats
+
+def process_excel(excel_data, analysis_type):
+    repeats = set()
+    sequence_data = []
+    count = 0
+    for sheet_name in excel_data.sheet_names:
+        df = excel_data.parse(sheet_name)
+        if len(df.columns) < 3:
+            st.error(f"Error: The sheet '{sheet_name}' must have at least three columns: ID, Protein Name, Sequence")
+            return None, None
+        for _, row in df.iterrows():
+            entry_id = str(row[0])
+            protein_name = str(row[1])
+            sequence = str(row[2]).replace('"', '').replace(' ', '').strip()
+            if not sequence:
+                continue
+            count += 1
+            freq = get_or_process_sequence(sequence, analysis_type)
+            sequence_data.append((entry_id, protein_name, freq))
+            repeats.update(freq.keys())
+    st.toast(f"{count} sequences processed.")
+    return repeats, sequence_data
+
+def create_excel(sequences_data, repeats, filenames):
+    output = BytesIO()
+    workbook = xlsxwriter.Workbook(output, {'in_memory': True})
+    for file_index, file_data in enumerate(sequences_data):
+        filename = filenames[file_index]
+        worksheet = workbook.add_worksheet(filename[:31])
+        worksheet.write(0, 0, "Entry")
+        worksheet.write(0, 1, "Protein Name")
+        col = 2
+        for repeat in sorted(repeats):
+            worksheet.write(0, col, repeat)
+            col += 1
+        row = 1
+        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(repeats):
+                worksheet.write(row, col, freq.get(repeat, 0))
+                col += 1
+            row += 1
+    workbook.close()
+    output.seek(0)
+    return output
+
+# Streamlit UI
+st.set_page_config(page_title="Protein Tool", layout="wide")
+st.title("🧬 Protein Analysis Toolkit by SCBL, IITG")
+
+app_choice = st.radio("Choose an option", ["🔁 Protein Repeat Finder", "📊 Protein Comparator", "🧪 Amino Acid Percentage Analyzer"])
+
+if app_choice == "🔁 Protein Repeat Finder":
+    analysis_type = st.radio("Select analysis type:", ["Homo", "Hetero", "Both"], index=2)
+    uploaded_files = st.file_uploader("Upload Excel files", accept_multiple_files=True, type=["xlsx"])
+
+    if 'all_sequences_data' not in st.session_state:
+        st.session_state.all_sequences_data = []
+        st.session_state.all_repeats = set()
+        st.session_state.filenames = []
+        st.session_state.excel_file = None
+
+    if uploaded_files and st.button("Process Files"):
+        st.session_state.all_repeats = set()
+        st.session_state.all_sequences_data = []
+        st.session_state.filenames = []
+        for file in uploaded_files:
+            excel_data = pd.ExcelFile(file)
+            repeats, sequence_data = process_excel(excel_data, analysis_type)
+            if repeats is not None:
+                st.session_state.all_repeats.update(repeats)
+                st.session_state.all_sequences_data.append(sequence_data)
+                st.session_state.filenames.append(file.name)
+        if st.session_state.all_sequences_data:
+            st.toast(f"Processed {len(uploaded_files)} file(s) successfully.")
+            st.session_state.excel_file = create_excel(
+                st.session_state.all_sequences_data,
+                st.session_state.all_repeats,
+                st.session_state.filenames
+            )
+
+    if st.session_state.excel_file:
+        st.download_button(
+            label="Download Excel file",
+            data=st.session_state.excel_file,
+            file_name="Protein_Repeats_Analysis.xlsx",
+            mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+        )
+
+    # Display results table and repeat cluster visualization
+    if st.checkbox("Show Results Table"):
+        rows = []
+        for file_index, file_data in enumerate(st.session_state.all_sequences_data):
+            filename = st.session_state.filenames[file_index]
+            for entry_id, protein_name, freq in file_data:
+                row = {"Filename": filename, "Entry": entry_id, "Protein Name": protein_name}
+                row.update({repeat: freq.get(repeat, 0) for repeat in sorted(st.session_state.all_repeats)})
+                rows.append(row)
+        result_df = pd.DataFrame(rows)
+        st.dataframe(result_df)
+
+        # Repeat Cluster Visualization
+        repeat_counts = defaultdict(int)
+        for seq_data in st.session_state.all_sequences_data:
+            for _, _, freq_dict in seq_data:
+                for repeat, count in freq_dict.items():
+                    repeat_counts[repeat] += count
+
+        if repeat_counts:
+            sorted_repeats = sorted(repeat_counts.items(), key=lambda x: x[1], reverse=True)
+            top_n = st.slider("Select number of top repeats to visualize", min_value=5, max_value=50, value=20)
+            top_repeats = sorted_repeats[:top_n]
+            repeats, counts = zip(*top_repeats)
+
+            plt.figure(figsize=(12, 6))
+            sns.barplot(x=list(repeats), y=list(counts), palette="viridis")
+            plt.xticks(rotation=45, ha='right')
+            plt.xlabel("Repeats")
+            plt.ylabel("Total Frequency")
+            plt.title("Top Repeat Clusters Across All Sequences")
+            st.pyplot(plt.gcf())
+        else:
+            st.warning("No repeat data available to visualize. Please upload files first.")
+
+
+
+elif app_choice == "📊 Protein Comparator":
+    st.write("Upload two Excel files with protein data to compare repeat frequencies.")
+
+    file1 = st.file_uploader("Upload First Excel File", type=["xlsx"], key="comp1")
+    file2 = st.file_uploader("Upload Second Excel File", type=["xlsx"], key="comp2")
+
+    if file1 and file2:
+        df1 = pd.read_excel(file1)
+        df2 = pd.read_excel(file2)
+
+        df1.columns = df1.columns.astype(str)
+        df2.columns = df2.columns.astype(str)
+
+        id_col = df1.columns[0]
+        name_col = df1.columns[1]
+        repeat_columns = df1.columns[2:]
+
+        diff_data = []
+        for i in range(min(len(df1), len(df2))):
+            row1 = df1.iloc[i]
+            row2 = df2.iloc[i]
+            diff_row = {"Entry": row1[id_col], "Protein Name": row1[name_col]}
+            for repeat in repeat_columns:
+                val1 = row1.get(repeat, 0)
+                val2 = row2.get(repeat, 0)
+                change = ((val2 - val1) / val1 * 100) if val1 != 0 else (100 if val2 > 0 else 0)
+                diff_row[repeat] = change
+            diff_data.append(diff_row)
+
+        result_df = pd.DataFrame(diff_data)
+        percent_cols = result_df.select_dtypes(include='number').columns
+        st.dataframe(result_df.style.format({col: "{:.2f}%" for col in percent_cols}))
+
+        def to_excel_with_colors(df):
+            output = BytesIO()
+            workbook = xlsxwriter.Workbook(output, {'in_memory': True})
+            worksheet = workbook.add_worksheet('Comparison')
+
+            green_format = workbook.add_format({'font_color': 'green'})
+            red_format = workbook.add_format({'font_color': 'red'})
+            header_format = workbook.add_format({'bold': True, 'bg_color': '#D7E4BC'})
+
+            for col_num, col_name in enumerate(df.columns):
+                worksheet.write(0, col_num, col_name, header_format)
+
+            for row_num, row in enumerate(df.itertuples(index=False), start=1):
+                for col_num, value in enumerate(row):
+                    if col_num < 2:
+                        worksheet.write(row_num, col_num, value)
+                    else:
+                        fmt = green_format if value > 0 else red_format if value < 0 else None
+                        worksheet.write(row_num, col_num, f"{value:.2f}%", fmt)
+
+            workbook.close()
+            output.seek(0)
+            return output
+
+        excel_file = to_excel_with_colors(result_df)
+
+        st.download_button(
+            label="Download Colored Comparison Excel",
+            data=excel_file,
+            file_name="comparison_result_colored.xlsx",
+            mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+        )
+
+elif app_choice == "🧪 Amino Acid Percentage Analyzer":
+    import matplotlib.pyplot as plt  # Needed for pie chart
+
+    AMINO_ACIDS = set("ACDEFGHIKLMNPQRSTVWY")
+
+    uploaded_file = st.file_uploader("Upload Excel file (with Entry, Protein Name, Sequence)", type=["xlsx"])
+
+    if uploaded_file and st.button("Analyze File"):
+        df = pd.read_excel(uploaded_file)
+
+        if len(df.columns) < 3:
+            st.error("The file must have at least three columns: Entry, Protein Name, Sequence")
+        else:
+            entry_col = df.columns[0]
+            name_col = df.columns[1]
+            seq_col = df.columns[2]
+
+            from collections import Counter
+            all_counts = Counter()
+            all_length = 0
+            result_rows = []
+
+            for _, row in df.iterrows():
+                entry = str(row[entry_col])
+                name = str(row[name_col])
+                sequence = str(row[seq_col]).replace(" ", "").replace("\"", "").strip().upper()
+                sequence = ''.join(filter(lambda c: c in AMINO_ACIDS, sequence))
+                length = len(sequence)
+
+                if length == 0:
+                    continue
+
+                count = Counter(sequence)
+                all_counts.update(count)
+                all_length += length
+                percentage = {aa: round(count[aa] / length * 100, 2) for aa in AMINO_ACIDS}
+                result_rows.append({"Entry": entry, "Protein Name": name, **percentage})
+
+            overall_percentage = {aa: round(all_counts[aa] / all_length * 100, 2) for aa in AMINO_ACIDS}
+            overall_row = {"Entry": "OVERALL", "Protein Name": "ALL SEQUENCES", **overall_percentage}
+            df_result = pd.concat([pd.DataFrame([overall_row]), pd.DataFrame(result_rows)], ignore_index=True)
+
+            st.dataframe(df_result)
+
+            # 🔵 Pie Chart
+            st.subheader("🧁 Overall Amino Acid Composition (Pie Chart)")
+            fig, ax = plt.subplots(figsize=(9, 9))
+            labels = list(overall_percentage.keys())
+            sizes = list(overall_percentage.values())
+            filtered = [(label, size) for label, size in zip(labels, sizes) if size > 0]
+
+            if filtered:
+                labels, sizes = zip(*filtered)
+                ax.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90, counterclock=False)
+                ax.axis('equal')
+                st.pyplot(fig)
+            else:
+                st.info("No valid amino acids found to display in pie chart.")
+
+            # Excel Export
+            def to_excel(df):
+                output = BytesIO()
+                workbook = xlsxwriter.Workbook(output, {'in_memory': True})
+                worksheet = workbook.add_worksheet("Amino Acid %")
+                header_format = workbook.add_format({'bold': True, 'bg_color': '#CDEDF6'})
+                for col_num, col_name in enumerate(df.columns):
+                    worksheet.write(0, col_num, col_name, header_format)
+                for row_num, row in enumerate(df.itertuples(index=False), start=1):
+                    for col_num, value in enumerate(row):
+                        worksheet.write(row_num, col_num, value)
+                workbook.close()
+                output.seek(0)
+                return output
+
+            excel_file = to_excel(df_result)
+
+            st.download_button(
+                label="Download Excel Report",
+                data=excel_file,
+                file_name="amino_acid_percentage.xlsx",
+                mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+            )
+

requirements.txt

@@ -0,0 +1,8 @@
+streamlit
+pandas
+xlsxwriter
+openpyxl
+pymongo
+matplotlib
+seaborn
+python-dotenv