import os
import tiger
import cas9on
import cas9off
import cas12
import pandas as pd
import streamlit as st
import plotly.graph_objs as go
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'
cas12_path = 'cas12_model/Seq_deepCpf1_weights.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 'current_gene_symbol' not in st.session_state:
st.session_state['current_gene_symbol'] = ""
# Define a function to clean up old files
def clean_up_old_files(gene_symbol):
genbank_file_path = f"{gene_symbol}_crispr_targets.gb"
bed_file_path = f"{gene_symbol}_crispr_targets.bed"
csv_file_path = f"{gene_symbol}_crispr_predictions.csv"
for path in [genbank_file_path, bed_file_path, csv_file_path]:
if os.path.exists(path):
os.remove(path)
# Gene symbol entry
gene_symbol = st.text_input('Enter a Gene Symbol:', placeholder="e.g., FOXA1", key='gene_symbol')
# Handle gene symbol change and file cleanup
if gene_symbol != st.session_state['current_gene_symbol'] and gene_symbol:
if st.session_state['current_gene_symbol']:
# Clean up files only if a different gene symbol is entered and a previous symbol exists
clean_up_old_files(st.session_state['current_gene_symbol'])
# Update the session state with the new gene symbol
st.session_state['current_gene_symbol'] = gene_symbol
if target_selection == 'on-target':
# Prediction button
predict_button = st.button('Predict on-target')
# Process predictions
if predict_button and gene_symbol:
with st.spinner('Predicting... Please wait'):
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
st.session_state['gene_sequence'] = gene_sequence # Save gene sequence in session state
# Notify the user once the process is completed successfully.
st.success('Prediction completed!')
st.session_state['prediction_made'] = True
# Process predictions
if 'prediction_made' in st.session_state and st.session_state['prediction_made']:
with st.spinner('Predicting... Please wait'):
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
# notify the user once the process is completed successfully.
st.success('Prediction completed!')
if 'on_target_results' in st.session_state and st.session_state['on_target_results']:
# Include "Target" in the DataFrame's columns
df = pd.DataFrame(st.session_state['on_target_results'],
columns=["Gene ID", "Start Pos", "End Pos", "Strand", "Target", "gRNA", "Prediction"])
st.dataframe(df)
# Now create a Plotly plot with the sorted_predictions
fig = go.Figure()
# Initialize the y position for the positive and negative strands
positive_strand_y = 0.1
negative_strand_y = -0.1
# Use an offset to spread gRNA sequences vertically
offset = 0.05
# Iterate over the sorted predictions to create the plot
for i, prediction in enumerate(sorted_predictions, start=1):
# Extract data for plotting and convert start and end to integers
chrom, start, end, strand, target, gRNA, pred_score = prediction
start, end = int(start), int(end)
midpoint = (start + end) / 2
# Set the y-value and arrow symbol based on the strand
if strand == '1':
y_value = positive_strand_y
arrow_symbol = 'triangle-right'
# Increment the y-value for the next positive strand gRNA
positive_strand_y += offset
else:
y_value = negative_strand_y
arrow_symbol = 'triangle-left'
# Decrement the y-value for the next negative strand gRNA
negative_strand_y -= offset
fig.add_trace(go.Scatter(
x=[midpoint],
y=[y_value], # Use the y_value set above for the strand
mode='markers+text',
marker=dict(symbol=arrow_symbol, size=10),
name=f"gRNA: {gRNA}",
text=f"Rank: {i}", # Place text at the marker
hoverinfo='text',
hovertext=f"Rank: {i}
Chromosome: {chrom}
Target Sequence: {target}
gRNA: {gRNA}
Start: {start}
End: {end}
Strand: {'+' if strand == 1 else '-'}
Prediction Score: {pred_score:.4f}",
))
# Update the layout of the plot
fig.update_layout(
title='Top 10 gRNA Sequences by Prediction Score',
xaxis_title='Genomic Position',
yaxis=dict(
title='Strand',
showgrid=True, # Show horizontal gridlines for clarity
zeroline=True, # Show a line at y=0 to represent the axis
zerolinecolor='Black',
zerolinewidth=2,
tickvals=[positive_strand_y, negative_strand_y],
ticktext=['+ Strand', '- Strand']
),
showlegend=False # Hide the legend if it's not necessary
)
# Display the plot
st.plotly_chart(fig)
if 'gene_sequence' in st.session_state and st.session_state['gene_sequence']:
gene_symbol = st.session_state['current_gene_symbol']
gene_sequence = st.session_state['gene_sequence']
# Define file paths
genbank_file_path = f"{gene_symbol}_crispr_targets.gb"
bed_file_path = f"{gene_symbol}_crispr_targets.bed"
csv_file_path = f"{gene_symbol}_crispr_predictions.csv"
# Generate files
cas9on.generate_genbank_file_from_df(df, gene_sequence, gene_symbol, genbank_file_path)
cas9on.create_bed_file_from_df(df, bed_file_path)
cas9on.create_csv_from_df(df, csv_file_path)
# Layout for download buttons
col1, col2, col3 = st.columns(3)
with col1:
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")
with col2:
with open(bed_file_path, "rb") as file:
st.download_button(label="Download BED File", data=file, file_name=bed_file_path,
mime="text/plain")
with col3:
with open(csv_file_path, "rb") as file:
st.download_button(label="Download CSV File", data=file, file_name=csv_file_path,
mime="text/csv")
# Links for user guidance on using the downloaded files
st.markdown(
"GenBank files can be visualized using [PyGenomeViz](https://pygenomeviz.streamlit.app/). "
"BED files can be used with the [UCSC Genome Browser](https://genome.ucsc.edu/cgi-bin/hgCustom).")
# # Visualize the GenBank file using pyGenomeViz
# gv = GenomeViz(
# feature_track_ratio=0.3,
# tick_track_ratio=0.5,
# tick_style="axis",
# )
#
# # Load the GenBank file
# gbk = Genbank(genbank_file_path)
#
# # Add a feature track to the GenomeViz object
# track = gv.add_feature_track(gbk.name, gbk.range_size)
#
# # Add all features from the GenBank file to the track
# track.add_genbank_features(gbk)
#
# # Plot the figure and display it in Streamlit
# fig = gv.plotfig()
# st.pyplot(fig)
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':
# Gene symbol entry
gene_symbol = st.text_input('Enter a Gene Symbol:', placeholder="e.g., FOXA1", key='gene_symbol')
# Initialize the current_gene_symbol in the session state if it doesn't exist
if 'current_gene_symbol' not in st.session_state:
st.session_state['current_gene_symbol'] = ""
# Prediction button
predict_button = st.button('Predict on-target')
# Function to clean up old files
def clean_up_old_files(gene_symbol):
genbank_file_path = f"{gene_symbol}_crispr_targets.gb"
bed_file_path = f"{gene_symbol}_crispr_targets.bed"
csv_file_path = f"{gene_symbol}_crispr_predictions.csv"
for path in [genbank_file_path, bed_file_path, csv_file_path]:
if os.path.exists(path):
os.remove(path)
# Clean up files if a new gene symbol is entered
if st.session_state['current_gene_symbol'] and gene_symbol != st.session_state['current_gene_symbol']:
clean_up_old_files(st.session_state['current_gene_symbol'])
# Process predictions
if predict_button and gene_symbol:
# Update the current gene symbol
st.session_state['current_gene_symbol'] = gene_symbol
# Run the prediction process
with st.spinner('Predicting... Please wait'):
predictions, gene_sequence = cas12.process_gene(gene_symbol,cas12_path)
sorted_predictions = sorted(predictions, key=lambda x: x[-1], reverse=True)[:10]
st.session_state['on_target_results'] = sorted_predictions
st.success('Prediction completed!')
# Visualization and file generation
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", "Target", "gRNA", "Prediction"])
st.dataframe(df)
# Now create a Plotly plot with the sorted_predictions
fig = go.Figure()
# Initialize the y position for the positive and negative strands
positive_strand_y = 0.1
negative_strand_y = -0.1
# Use an offset to spread gRNA sequences vertically
offset = 0.05
# Iterate over the sorted predictions to create the plot
for i, prediction in enumerate(sorted_predictions, start=1):
# Extract data for plotting and convert start and end to integers
chrom, start, end, strand, target, gRNA, pred_score = prediction
start, end = int(start), int(end)
midpoint = (start + end) / 2
# Set the y-value and arrow symbol based on the strand
if strand == '1':
y_value = positive_strand_y
arrow_symbol = 'triangle-right'
# Increment the y-value for the next positive strand gRNA
positive_strand_y += offset
else:
y_value = negative_strand_y
arrow_symbol = 'triangle-left'
# Decrement the y-value for the next negative strand gRNA
negative_strand_y -= offset
fig.add_trace(go.Scatter(
x=[midpoint],
y=[y_value], # Use the y_value set above for the strand
mode='markers+text',
marker=dict(symbol=arrow_symbol, size=10),
name=f"gRNA: {gRNA}",
text=f"Rank: {i}", # Place text at the marker
hoverinfo='text',
hovertext=f"Rank: {i}
Chromosome: {chrom}
Target Sequence: {target}
gRNA: {gRNA}
Start: {start}
End: {end}
Strand: {'+' if strand == 1 else '-'}
Prediction Score: {pred_score:.4f}",
))
# Update the layout of the plot
fig.update_layout(
title='Top 10 gRNA Sequences by Prediction Score',
xaxis_title='Genomic Position',
yaxis=dict(
title='Strand',
showgrid=True, # Show horizontal gridlines for clarity
zeroline=True, # Show a line at y=0 to represent the axis
zerolinecolor='Black',
zerolinewidth=2,
tickvals=[positive_strand_y, negative_strand_y],
ticktext=['+ Strand', '- Strand']
),
showlegend=False # Hide the legend if it's not necessary
)
# Display the plot
st.plotly_chart(fig)
# Ensure gene_sequence is not empty before generating files
if gene_sequence:
# Define file paths
genbank_file_path = f"{gene_symbol}_crispr_targets.gb"
bed_file_path = f"{gene_symbol}_crispr_targets.bed"
csv_file_path = f"{gene_symbol}_crispr_predictions.csv"
# Generate files
cas9on.generate_genbank_file_from_df(df, gene_sequence, gene_symbol, genbank_file_path)
cas9on.create_bed_file_from_df(df, bed_file_path)
cas9on.create_csv_from_df(df, csv_file_path)
# Layout for download buttons
col1, col2, col3 = st.columns(3)
with col1:
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")
with col2:
with open(bed_file_path, "rb") as file:
st.download_button(label="Download BED File", data=file, file_name=bed_file_path,
mime="text/plain")
with col3:
with open(csv_file_path, "rb") as file:
st.download_button(label="Download CSV File", data=file, file_name=csv_file_path,
mime="text/csv")
# Links for user guidance on using the downloaded files
st.markdown(
"GenBank files can be visualized using [PyGenomeViz](https://pygenomeviz.streamlit.app/). "
"BED files can be used with the [UCSC Genome Browser](https://genome.ucsc.edu/cgi-bin/hgCustom)."
)
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()