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import os
os.system("pip install streamlit pandas xlsxwriter openpyxl pymongo")
import streamlit as st
import pandas as pd
import xlsxwriter
from io import BytesIO
from collections import defaultdict
import hashlib
# Optional for Repeats Functionality
try:
from pymongo import MongoClient
client = MongoClient("mongodb+srv://dhruvmangroliya:[email protected]/BTP_DB?retryWrites=true&w=majority")
db = client['BTP_DB']
results_collection = db['protein_results']
except:
results_collection = None
st.set_page_config(page_title="Protein Tool", layout="wide")
st.title("🧬 Protein Analysis Toolkit")
app_choice = st.radio("Choose an option", ["🔁 Protein Repeat Finder", "📊 Protein Comparator"])
# ------------------- REPEATS FUNCTIONALITY -------------------
if app_choice == "🔁 Protein Repeat Finder":
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 not results_collection:
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: Sheet '{sheet_name}' must have at least 3 columns: ID, 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
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_repeat_results.xlsx",
mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
)
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)
# ------------------- COMPARATOR FUNCTIONALITY -------------------
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, header=0)
df2 = pd.read_excel(file2, header=0)
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:]
differences = []
for i in range(len(df1)):
row1 = df1.iloc[i]
row2 = df2.iloc[i] if i < len(df2) else None
if row2 is not None:
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)
diff_row[repeat] = abs(val1 - val2)
differences.append(diff_row)
result_df = pd.DataFrame(differences)
st.dataframe(result_df)
def to_excel(df):
output = BytesIO()
writer = pd.ExcelWriter(output, engine='xlsxwriter')
df.to_excel(writer, index=False, sheet_name='Comparison')
writer.close()
output.seek(0)
return output
excel_file = to_excel(result_df)
st.download_button(
label="Download Comparison Excel",
data=excel_file,
file_name="comparison_result.xlsx",
mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
)
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