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import streamlit as st | |
import requests | |
from io import StringIO | |
from Bio import SeqIO | |
import os | |
import time | |
import pandas as pd | |
from run_domain2go_app import * | |
def convert_df(df): | |
return df.to_csv(index=False).encode('utf-8') | |
st.markdown(""" | |
<div style="background-color:#f9f9f9;padding:10px"> | |
<p style="color:#b22d2a;font-size:15px;">Disclaimer</p> | |
<p style="color:#000000;font-size:14px;">This program is designed to generate predictions for a single protein due to the extended runtime of InterProScan. If you need predictions for multiple UniProtKB/Swiss-Prot proteins, we recommend utilizing our comprehensive protein function prediction dataset available in our <a href="https://github.com/HUBioDataLab/Domain2GO">Github repository</a>.</p> | |
</div> | |
""", unsafe_allow_html=True) | |
domain_tab, pred_tab = st.tabs(['Domains', 'Function predictions']) | |
with domain_tab: | |
st.header('Domains in sequence') | |
with st.sidebar: | |
st.title("Domain2GO: Mutual Annotation-Based Prediction of Protein Domain Functions") | |
st.write("[![arXiv](https://img.shields.io/badge/bioRxiv-2022.11.03.514980-b31b1b.svg)](https://www.biorxiv.org/content/10.1101/2022.11.03.514980v1) [![github-repository](https://img.shields.io/badge/GitHub-black?logo=github)](https://github.com/HUBioDataLab/Domain2GO)") | |
if 'example_seq_button' not in st.session_state: | |
st.session_state.example_seq_button = False | |
def click_button(): | |
st.session_state.example_seq_button = not st.session_state.example_seq_button | |
input_type = st.radio('Select input type', ['Enter sequence', 'Upload FASTA file']) | |
if input_type == 'Enter sequence': | |
if st.session_state.example_seq_button: | |
sequence = st.text_area('Enter protein sequence in FASTA format.', | |
value='>sp|O18783|PLMN_NOTEU\n' | |
'MEYGKVIFLFLLFLKSGQGESLENYIKTEGASLSNSQKKQFVASSTEECEALCEKETEFVCRSFEHYNKEQKCVIMSENSKTSSVERKRDVVLFEKRIYLSDCKSGNGRNYRGTLSKTKSGITCQKWSDLSPHVPNYAPSKYPDAGLEKNYCRNPDDDVKGPWCYTTNPDIRYEYCDVPECEDECMHCSGENYRGTISKTESGIECQPWDSQEPHSHEYIPSKFPSKDLKENYCRNPDGEPRPWCFTSNPEKRWEFCNIPRCSSPPPPPGPMLQCLKGRGENYRGKIAVTKSGHTCQRWNKQTPHKHNRTPENFPCRGLDENYCRNPDGELEPWCYTTNPDVRQEYCAIPSCGTSSPHTDRVEQSPVIQECYEGKGENYRGTTSTTISGKKCQAWSSMTPHQHKKTPDNFPNADLIRNYCRNPDGDKSPWCYTMDPTVRWEFCNLEKCSGTGSTVLNAQTTRVPSVDTTSHPESDCMYGSGKDYRGKRSTTVTGTLCQAWTAQEPHRHTIFTPDTYPRAGLEENYCRNPDGDPNGPWCYTTNPKKLFDYCDIPQCVSPSSFDCGKPRVEPQKCPGRIVGGCYAQPHSWPWQISLRTRFGEHFCGGTLIAPQWVLTAAHCLERSQWPGAYKVILGLHREVNPESYSQEIGVSRLFKGPLAADIALLKLNRPAAINDKVIPACLPSQDFMVPDRTLCHVTGWGDTQGTSPRGLLKQASLPVIDNRVCNRHEYLNGRVKSTELCAGHLVGRGDSCQGDSGGPLICFEDDKYVLQGVTSWGLGCARPNKPGVYVRVSRYISWIEDVMKNN') | |
else: | |
sequence = st.text_input('Enter protein sequence in FASTA format.') | |
name = sequence.split('\n')[0].strip('>') | |
st.button('Use example sequence', on_click=click_button) | |
else: | |
protein_input = st.file_uploader('Choose file') | |
if protein_input: | |
protein_input_stringio = StringIO(protein_input.getvalue().decode("utf-8")) | |
fasta_sequences = SeqIO.parse(protein_input_stringio, 'fasta') | |
for fasta in fasta_sequences: | |
name, sequence = fasta.id, str(fasta.seq) | |
email = st.text_input('Enter your email for InterProScan query: ') | |
# prevent user from clicking 'Find domains' button if email or sequence is empty | |
domains_submitted = False | |
if st.button('Find domains'): | |
if email and sequence: | |
domains_submitted = True | |
st.session_state.disabled = True | |
else: | |
st.warning('Please enter your email and protein sequence first.') | |
else: | |
with domain_tab: | |
st.warning('Please enter your query and click "Find domains" to see domains in sequence.') | |
with domain_tab: | |
no_domains = False | |
error_in_interproscan = False | |
if domains_submitted: | |
with st.spinner('Finding domains in sequence using InterProScan. This may take a while...'): | |
result = find_domains(email, sequence, name) | |
result_text = result[0] | |
if result_text == 'Domains found.': | |
st.success(result_text + 'You can now see function predictions for the sequence in the "Function predictions" tab.') | |
st.session_state['domain_df'] = result[1] | |
elif result_text == 'No domains found.': | |
st.warning(result_text) | |
no_domains = True | |
else: | |
st.error(result_text) | |
st.write(f'InterProScan job id: {result[1]}') | |
st.write(f'InterProScan job response: {result[2]}') | |
error_in_interproscan = True | |
if 'domain_df' in st.session_state: | |
with st.expander('Show domains in sequence'): | |
st.write(st.session_state.domain_df) | |
domains_csv = convert_df(st.session_state.domain_df) | |
st.download_button( | |
label="Download domains in sequence as CSV", | |
data=domains_csv, | |
file_name=f"{name}_domains.csv", | |
mime="text/csv", | |
) | |
with pred_tab: | |
st.header('Function predictions') | |
if 'domain_df' not in st.session_state: | |
if no_domains: | |
st.warning('No domains found. Please find domains in sequence first.') | |
elif error_in_interproscan: | |
st.error('Error in InterProScan. Please check InterProScan job id and response.') | |
else: | |
st.warning('Please find domains in sequence first.') | |
else: | |
with st.spinner('Generating function predictions...'): | |
cwd = os.getcwd() | |
mapping_path = "{}Domain2GO/data".format(cwd.split("Domain2GO")[0]) | |
pred_results = generate_function_predictions(st.session_state.domain_df, mapping_path) | |
pred_result_text = pred_results[0] | |
if pred_result_text == 'Function predictions found.': | |
st.success(pred_result_text) | |
st.session_state['pred_df'] = pred_results[1] | |
elif pred_result_text == 'No function predictions found.': | |
st.warning(pred_result_text) | |
if 'pred_df' in st.session_state: | |
with st.expander('Show function predictions'): | |
st.write(st.session_state.pred_df) | |
pred_csv = convert_df(st.session_state.pred_df) | |
st.download_button( | |
label="Download function predictions as CSV", | |
data=pred_csv, | |
file_name=f"{name}_function_predictions.csv", | |
mime="text/csv", | |
) | |