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supercat666
commited on
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16e89c0
1
Parent(s):
9ad0b46
fix app.py
Browse files
app.py
CHANGED
@@ -4,6 +4,7 @@ import cas9att
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import cas9attvcf
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import cas9off
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import cas12
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import pandas as pd
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import streamlit as st
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import plotly.graph_objs as go
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@@ -184,10 +185,7 @@ if selected_model == 'Cas9':
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if predict_button and gene_symbol:
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model_choice = st.radio("mutation or not:", ('normal', 'mutation'))
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with st.spinner('Predicting... Please wait'):
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predictions, gene_sequence, exons = cas9attvcf.process_gene(gene_symbol, cas9att_path)
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else:
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predictions, gene_sequence, exons = cas9att.process_gene(gene_symbol, cas9att_path)
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sorted_predictions = sorted(predictions)[:10]
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st.session_state['on_target_results'] = sorted_predictions
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@@ -437,83 +435,98 @@ elif selected_model == 'Cas12':
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# Process predictions
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if predict_button and gene_symbol:
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# Update the current gene symbol
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st.session_state['current_gene_symbol'] = gene_symbol
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# Run the prediction process
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with st.spinner('Predicting... Please wait'):
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predictions, gene_sequence, exons =
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st.session_state['on_target_results'] = sorted_predictions
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st.session_state['gene_sequence'] = gene_sequence # Save gene sequence in session state
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st.session_state['exons'] = exons # Store exon data
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st.success('Prediction completed!')
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# Visualization and file generation
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if 'on_target_results' in st.session_state and st.session_state['on_target_results']:
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fig = go.Figure()
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fig.add_trace(go.Scatter(
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x=[midpoint],
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y=[y_value],
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mode='markers+text',
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marker=dict(symbol=
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text=f"Rank: {i}", #
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hoverinfo='text',
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hovertext=f"Rank: {i}<br>Chromosome: {chrom}<br>Target Sequence: {target}<br>gRNA: {gRNA}<br>Start: {start}<br>End: {end}<br>Strand: {'+' if strand == 1 else '-'}<br>Prediction
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))
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# Update
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fig.update_layout(
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title='Top
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xaxis_title='Genomic Position',
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zerolinecolor='Black',
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zerolinewidth=2,
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tickvals=[positive_strand_y, negative_strand_y],
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ticktext=['+ Strand', '- Strand']
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),
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showlegend=False # Hide the legend if it's not necessary
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)
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# Display the plot
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st.plotly_chart(fig)
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# Ensure gene_sequence is not empty before generating files
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if 'gene_sequence' in st.session_state and st.session_state['gene_sequence']:
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gene_symbol = st.session_state['current_gene_symbol']
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gene_sequence = st.session_state['gene_sequence']
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@@ -522,26 +535,38 @@ elif selected_model == 'Cas12':
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genbank_file_path = f"{gene_symbol}_crispr_targets.gb"
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bed_file_path = f"{gene_symbol}_crispr_targets.bed"
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csv_file_path = f"{gene_symbol}_crispr_predictions.csv"
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# Generate files
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# Prepare an in-memory buffer for the ZIP file
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zip_buffer = io.BytesIO()
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with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file:
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# For each file, add it to the ZIP file
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zip_file.write(genbank_file_path
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zip_file.write(bed_file_path
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zip_file.write(csv_file_path
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# Important: move the cursor to the beginning of the BytesIO buffer before reading it
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zip_buffer.seek(0)
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# Display the download button for the ZIP file
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st.download_button(
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label="Download
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data=zip_buffer.getvalue(),
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file_name=f"{gene_symbol}_files.zip",
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mime="application/zip"
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import cas9attvcf
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import cas9off
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import cas12
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import cas12lstm
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import pandas as pd
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import streamlit as st
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import plotly.graph_objs as go
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if predict_button and gene_symbol:
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model_choice = st.radio("mutation or not:", ('normal', 'mutation'))
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with st.spinner('Predicting... Please wait'):
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predictions, gene_sequence, exons = cas9att.process_gene(gene_symbol, cas9att_path)
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sorted_predictions = sorted(predictions)[:10]
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st.session_state['on_target_results'] = sorted_predictions
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# Process predictions
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if predict_button and gene_symbol:
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with st.spinner('Predicting... Please wait'):
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predictions, gene_sequence, exons = cas12lstm.process_gene(gene_symbol, cas9att_path)
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sorted_predictions = sorted(predictions)[:10]
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st.session_state['on_target_results'] = sorted_predictions
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st.session_state['gene_sequence'] = gene_sequence # Save gene sequence in session state
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st.session_state['exons'] = exons # Store exon data
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# Notify the user once the process is completed successfully.
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st.success('Prediction completed!')
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st.session_state['prediction_made'] = True
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if 'on_target_results' in st.session_state and st.session_state['on_target_results']:
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ensembl_id = gene_annotations.get(gene_symbol, 'Unknown') # Get Ensembl ID or default to 'Unknown'
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col1, col2, col3 = st.columns(3)
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with col1:
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st.markdown("**Genome**")
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st.markdown("Homo sapiens")
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with col2:
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st.markdown("**Gene**")
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st.markdown(f"{gene_symbol} : {ensembl_id} (primary)")
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with col3:
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st.markdown("**Nuclease**")
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st.markdown("SpCas9")
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# Include "Target" in the DataFrame's columns
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try:
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df = pd.DataFrame(st.session_state['on_target_results'],
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columns=["Chr", "Start Pos", "End Pos", "Strand", "Transcript", "Exon", "Target",
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"gRNA", "Prediction"])
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st.dataframe(df)
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except ValueError as e:
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st.error(f"DataFrame creation error: {e}")
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# Optionally print or log the problematic data for debugging:
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print(st.session_state['on_target_results'])
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# Initialize Plotly figure
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fig = go.Figure()
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EXON_BASE = 0 # Base position for exons and CDS on the Y axis
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EXON_HEIGHT = 0.02 # How 'tall' the exon markers should appear
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# Plot Exons as small markers on the X-axis
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for exon in st.session_state['exons']:
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exon_start, exon_end = exon['start'], exon['end']
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fig.add_trace(go.Bar(
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x=[(exon_start + exon_end) / 2],
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y=[EXON_HEIGHT],
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width=[exon_end - exon_start],
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base=EXON_BASE,
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marker_color='rgba(128, 0, 128, 0.5)',
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name='Exon'
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))
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VERTICAL_GAP = 0.2 # Gap between different ranks
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# Define max and min Y values based on strand and rank
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MAX_STRAND_Y = 0.1 # Maximum Y value for positive strand results
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MIN_STRAND_Y = -0.1 # Minimum Y value for negative strand results
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# Iterate over top 5 sorted predictions to create the plot
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for i, prediction in enumerate(st.session_state['on_target_results'][:5], start=1): # Only top 5
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chrom, start, end, strand, transcript, exon, target, gRNA, prediction_score = prediction
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midpoint = (int(start) + int(end)) / 2
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# Vertical position based on rank, modified by strand
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y_value = (MAX_STRAND_Y - (i - 1) * VERTICAL_GAP) if strand == '1' or strand == '+' else (
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MIN_STRAND_Y + (i - 1) * VERTICAL_GAP)
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fig.add_trace(go.Scatter(
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x=[midpoint],
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y=[y_value],
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mode='markers+text',
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marker=dict(symbol='triangle-up' if strand == '1' or strand == '+' else 'triangle-down',
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size=12),
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text=f"Rank: {i}", # Text label
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hoverinfo='text',
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hovertext=f"Rank: {i}<br>Chromosome: {chrom}<br>Target Sequence: {target}<br>gRNA: {gRNA}<br>Start: {start}<br>End: {end}<br>Strand: {'+' if strand == '1' or strand == '+' else '-'}<br>Transcript: {transcript}<br>Prediction: {prediction_score:.4f}",
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))
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# Update layout for clarity and interaction
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fig.update_layout(
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title='Top 5 gRNA Sequences by Prediction Score',
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xaxis_title='Genomic Position',
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yaxis_title='Strand',
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yaxis=dict(tickvals=[MAX_STRAND_Y, MIN_STRAND_Y], ticktext=['+', '-']),
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showlegend=False,
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hovermode='x unified',
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)
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# Display the plot
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st.plotly_chart(fig)
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if 'gene_sequence' in st.session_state and st.session_state['gene_sequence']:
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gene_symbol = st.session_state['current_gene_symbol']
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gene_sequence = st.session_state['gene_sequence']
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genbank_file_path = f"{gene_symbol}_crispr_targets.gb"
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bed_file_path = f"{gene_symbol}_crispr_targets.bed"
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csv_file_path = f"{gene_symbol}_crispr_predictions.csv"
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plot_image_path = f"{gene_symbol}_gtracks_plot.png"
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# Generate files
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cas12lstm.generate_genbank_file_from_df(df, gene_sequence, gene_symbol, genbank_file_path)
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cas12lstm.create_bed_file_from_df(df, bed_file_path)
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cas12lstm.create_csv_from_df(df, csv_file_path)
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# Prepare an in-memory buffer for the ZIP file
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zip_buffer = io.BytesIO()
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with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file:
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# For each file, add it to the ZIP file
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zip_file.write(genbank_file_path)
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zip_file.write(bed_file_path)
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zip_file.write(csv_file_path)
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# Important: move the cursor to the beginning of the BytesIO buffer before reading it
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zip_buffer.seek(0)
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# Specify the region you want to visualize
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min_start = df['Start Pos'].min()
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max_end = df['End Pos'].max()
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chromosome = df['Chr'].mode()[0] # Assumes most common chromosome is the target
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region = f"{chromosome}:{min_start}-{max_end}"
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# Generate the pyGenomeTracks plot
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gtracks_command = f"gtracks {region} {bed_file_path} {plot_image_path}"
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subprocess.run(gtracks_command, shell=True)
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st.image(plot_image_path)
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# Display the download button for the ZIP file
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st.download_button(
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label="Download GenBank, BED, CSV files as ZIP",
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data=zip_buffer.getvalue(),
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file_name=f"{gene_symbol}_files.zip",
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mime="application/zip"
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