import os import tiger import cas9on import cas9off import pandas as pd import streamlit as st from pygenomeviz import Genbank, GenomeViz import numpy as np from pathlib import Path # title and documentation st.markdown(Path('crisprTool.md').read_text(), unsafe_allow_html=True) st.divider() CRISPR_MODELS = ['Cas9', 'Cas12', 'Cas13d'] selected_model = st.selectbox('Select CRISPR model:', CRISPR_MODELS, key='selected_model') cas9on_path = 'cas9_model/on-cla.h5' @st.cache_data def convert_df(df): # IMPORTANT: Cache the conversion to prevent computation on every rerun return df.to_csv().encode('utf-8') def mode_change_callback(): if st.session_state.mode in {tiger.RUN_MODES['all'], tiger.RUN_MODES['titration']}: # TODO: support titration st.session_state.check_off_targets = False st.session_state.disable_off_target_checkbox = True else: st.session_state.disable_off_target_checkbox = False def progress_update(update_text, percent_complete): with progress.container(): st.write(update_text) st.progress(percent_complete / 100) def initiate_run(): # initialize state variables st.session_state.transcripts = None st.session_state.input_error = None st.session_state.on_target = None st.session_state.titration = None st.session_state.off_target = None # initialize transcript DataFrame transcripts = pd.DataFrame(columns=[tiger.ID_COL, tiger.SEQ_COL]) # manual entry if st.session_state.entry_method == ENTRY_METHODS['manual']: transcripts = pd.DataFrame({ tiger.ID_COL: ['ManualEntry'], tiger.SEQ_COL: [st.session_state.manual_entry] }).set_index(tiger.ID_COL) # fasta file upload elif st.session_state.entry_method == ENTRY_METHODS['fasta']: if st.session_state.fasta_entry is not None: fasta_path = st.session_state.fasta_entry.name with open(fasta_path, 'w') as f: f.write(st.session_state.fasta_entry.getvalue().decode('utf-8')) transcripts = tiger.load_transcripts([fasta_path], enforce_unique_ids=False) os.remove(fasta_path) # convert to upper case as used by tokenizer transcripts[tiger.SEQ_COL] = transcripts[tiger.SEQ_COL].apply(lambda s: s.upper().replace('U', 'T')) # ensure all transcripts have unique identifiers if transcripts.index.has_duplicates: st.session_state.input_error = "Duplicate transcript ID's detected in fasta file" # ensure all transcripts only contain nucleotides A, C, G, T, and wildcard N elif not all(transcripts[tiger.SEQ_COL].apply(lambda s: set(s).issubset(tiger.NUCLEOTIDE_TOKENS.keys()))): st.session_state.input_error = 'Transcript(s) must only contain upper or lower case A, C, G, and Ts or Us' # ensure all transcripts satisfy length requirements elif any(transcripts[tiger.SEQ_COL].apply(lambda s: len(s) < tiger.TARGET_LEN)): st.session_state.input_error = 'Transcript(s) must be at least {:d} bases.'.format(tiger.TARGET_LEN) # run model if we have any transcripts elif len(transcripts) > 0: st.session_state.transcripts = transcripts # Check if the selected model is Cas9 if selected_model == 'Cas9': # Use a radio button to select enzymes, making sure only one can be selected at a time target_selection = st.radio( "Select either on-target or off-target:", ('on-target', 'off-target'), key='target_selection' ) if target_selection == 'on-target': # Gene symbol entry gene_symbol = st.text_input('Enter a Gene Symbol:', key='gene_symbol') # Prediction button predict_button = st.button('Predict on-target') # Process predictions if predict_button and gene_symbol: predictions, gene_sequence = cas9on.process_gene(gene_symbol, cas9on_path) sorted_predictions = sorted(predictions, key=lambda x: x[-1], reverse=True)[:10] st.session_state['on_target_results'] = sorted_predictions if 'on_target_results' in st.session_state and st.session_state['on_target_results']: df = pd.DataFrame(st.session_state['on_target_results'], columns=["Gene ID", "Start Pos", "End Pos", "Strand", "gRNA", "Prediction"]) if gene_sequence: # Ensure gene_sequence is not empty genbank_file_path = f"{gene_symbol}_crispr_targets.gb" cas9on.generate_genbank_file_from_df(df, gene_sequence, gene_symbol, genbank_file_path) st.write('Top on-target predictions:') st.dataframe(df) # Add a download button for the GenBank file with open(genbank_file_path, "rb") as file: st.download_button( label="Download GenBank File", data=file, file_name=genbank_file_path, mime="text/x-genbank" ) # Visualize the GenBank file using pyGenomeViz gv = GenomeViz( feature_track_ratio=0.3, tick_track_ratio=0.5, tick_style="axis", ) gbk = Genbank(genbank_file_path) track = gv.add_feature_track(gbk.name, gbk.range_size) # Make sure you are adding features of the type that are present in your GenBank file track.add_genbank_features(gbk, feature_types=["misc_feature"]) # Specify feature types if needed fig = gv.plotfig() st.pyplot(fig) # Clean up the GenBank file after visualization os.remove(genbank_file_path) elif target_selection == 'off-target': ENTRY_METHODS = dict( manual='Manual entry of target sequence', txt="txt file upload" ) if __name__ == '__main__': # app initialization for Cas9 off-target if 'target_sequence' not in st.session_state: st.session_state.target_sequence = None if 'input_error' not in st.session_state: st.session_state.input_error = None if 'off_target_results' not in st.session_state: st.session_state.off_target_results = None # target sequence entry st.selectbox( label='How would you like to provide target sequences?', options=ENTRY_METHODS.values(), key='entry_method', disabled=st.session_state.target_sequence is not None ) if st.session_state.entry_method == ENTRY_METHODS['manual']: st.text_input( label='Enter on/off sequences:', key='manual_entry', placeholder='Enter on/off sequences like:GGGTGGGGGGAGTTTGCTCCAGG,AGGTGGGGTGA_TTTGCTCCAGG', disabled=st.session_state.target_sequence is not None ) elif st.session_state.entry_method == ENTRY_METHODS['txt']: st.file_uploader( label='Upload a txt file:', key='txt_entry', disabled=st.session_state.target_sequence is not None ) # prediction button if st.button('Predict off-target'): if st.session_state.entry_method == ENTRY_METHODS['manual']: user_input = st.session_state.manual_entry if user_input: # Check if user_input is not empty predictions = cas9off.process_input_and_predict(user_input, input_type='manual') elif st.session_state.entry_method == ENTRY_METHODS['txt']: uploaded_file = st.session_state.txt_entry if uploaded_file is not None: # Read the uploaded file content file_content = uploaded_file.getvalue().decode("utf-8") predictions = cas9off.process_input_and_predict(file_content, input_type='manual') st.session_state.off_target_results = predictions else: predictions = None progress = st.empty() # input error display error = st.empty() if st.session_state.input_error is not None: error.error(st.session_state.input_error, icon="🚨") else: error.empty() # off-target results display off_target_results = st.empty() if st.session_state.off_target_results is not None: with off_target_results.container(): if len(st.session_state.off_target_results) > 0: st.write('Off-target predictions:', st.session_state.off_target_results) st.download_button( label='Download off-target predictions', data=convert_df(st.session_state.off_target_results), file_name='off_target_results.csv', mime='text/csv' ) else: st.write('No significant off-target effects detected!') else: off_target_results.empty() # running the CRISPR-Net model for off-target predictions if st.session_state.target_sequence is not None: st.session_state.off_target_results = cas9off.predict_off_targets( target_sequence=st.session_state.target_sequence, status_update_fn=progress_update ) st.session_state.target_sequence = None st.experimental_rerun() elif selected_model == 'Cas12': # Placeholder for Cas12 model loading # TODO: Implement Cas12 model loading logic raise NotImplementedError("Cas12 model loading not implemented yet.") elif selected_model == 'Cas13d': ENTRY_METHODS = dict( manual='Manual entry of single transcript', fasta="Fasta file upload (supports multiple transcripts if they have unique ID's)" ) if __name__ == '__main__': # app initialization if 'mode' not in st.session_state: st.session_state.mode = tiger.RUN_MODES['all'] st.session_state.disable_off_target_checkbox = True if 'entry_method' not in st.session_state: st.session_state.entry_method = ENTRY_METHODS['manual'] if 'transcripts' not in st.session_state: st.session_state.transcripts = None if 'input_error' not in st.session_state: st.session_state.input_error = None if 'on_target' not in st.session_state: st.session_state.on_target = None if 'titration' not in st.session_state: st.session_state.titration = None if 'off_target' not in st.session_state: st.session_state.off_target = None # mode selection col1, col2 = st.columns([0.65, 0.35]) with col1: st.radio( label='What do you want to predict?', options=tuple(tiger.RUN_MODES.values()), key='mode', on_change=mode_change_callback, disabled=st.session_state.transcripts is not None, ) with col2: st.checkbox( label='Find off-target effects (slow)', key='check_off_targets', disabled=st.session_state.disable_off_target_checkbox or st.session_state.transcripts is not None ) # transcript entry st.selectbox( label='How would you like to provide transcript(s) of interest?', options=ENTRY_METHODS.values(), key='entry_method', disabled=st.session_state.transcripts is not None ) if st.session_state.entry_method == ENTRY_METHODS['manual']: st.text_input( label='Enter a target transcript:', key='manual_entry', placeholder='Upper or lower case', disabled=st.session_state.transcripts is not None ) elif st.session_state.entry_method == ENTRY_METHODS['fasta']: st.file_uploader( label='Upload a fasta file:', key='fasta_entry', disabled=st.session_state.transcripts is not None ) # let's go! st.button(label='Get predictions!', on_click=initiate_run, disabled=st.session_state.transcripts is not None) progress = st.empty() # input error error = st.empty() if st.session_state.input_error is not None: error.error(st.session_state.input_error, icon="🚨") else: error.empty() # on-target results on_target_results = st.empty() if st.session_state.on_target is not None: with on_target_results.container(): st.write('On-target predictions:', st.session_state.on_target) st.download_button( label='Download on-target predictions', data=convert_df(st.session_state.on_target), file_name='on_target.csv', mime='text/csv' ) else: on_target_results.empty() # titration results titration_results = st.empty() if st.session_state.titration is not None: with titration_results.container(): st.write('Titration predictions:', st.session_state.titration) st.download_button( label='Download titration predictions', data=convert_df(st.session_state.titration), file_name='titration.csv', mime='text/csv' ) else: titration_results.empty() # off-target results off_target_results = st.empty() if st.session_state.off_target is not None: with off_target_results.container(): if len(st.session_state.off_target) > 0: st.write('Off-target predictions:', st.session_state.off_target) st.download_button( label='Download off-target predictions', data=convert_df(st.session_state.off_target), file_name='off_target.csv', mime='text/csv' ) else: st.write('We did not find any off-target effects!') else: off_target_results.empty() # keep trying to run model until we clear inputs (streamlit UI changes can induce race-condition reruns) if st.session_state.transcripts is not None: st.session_state.on_target, st.session_state.titration, st.session_state.off_target = tiger.tiger_exhibit( transcripts=st.session_state.transcripts, mode={v: k for k, v in tiger.RUN_MODES.items()}[st.session_state.mode], check_off_targets=st.session_state.check_off_targets, status_update_fn=progress_update ) st.session_state.transcripts = None st.experimental_rerun()