File size: 4,022 Bytes
c7c7b6a 5a1e198 c7c7b6a 5e885f1 c7c7b6a 5e885f1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
# 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
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)
|