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9260769
1
Parent(s):
9f2ad27
fix
Browse files
app.py
CHANGED
@@ -97,13 +97,13 @@ def parse_gene_annotations(file_path):
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gene_dict = {}
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with open(file_path, 'r') as file:
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headers = file.readline().strip().split('\t') # Assuming tab-delimited file
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for line in file:
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values = line.strip().split('\t')
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#
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if approved_symbol != 'Unknown': # Only add if there is an approved symbol
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gene_dict[approved_symbol] = gene_info
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return gene_dict
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# Replace 'your_annotation_file.txt' with the path to your actual gene annotation file
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@@ -167,6 +167,17 @@ if selected_model == 'Cas9':
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if 'on_target_results' in st.session_state and st.session_state['on_target_results']:
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# Include "Target" in the DataFrame's columns
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df = pd.DataFrame(st.session_state['on_target_results'],
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columns=["Gene ID", "Start Pos", "End Pos", "Strand", "Target", "gRNA", "Prediction"])
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gene_dict = {}
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with open(file_path, 'r') as file:
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headers = file.readline().strip().split('\t') # Assuming tab-delimited file
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symbol_idx = headers.index('Approved symbol') # Find index of 'Approved symbol'
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ensembl_idx = headers.index('Ensembl gene ID') # Find index of 'Ensembl gene ID'
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for line in file:
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values = line.strip().split('\t')
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# Ensure we have enough values and add mapping from symbol to Ensembl ID
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if len(values) > max(symbol_idx, ensembl_idx):
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gene_dict[values[symbol_idx]] = values[ensembl_idx]
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return gene_dict
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# Replace 'your_annotation_file.txt' with the path to your actual gene annotation file
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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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df = pd.DataFrame(st.session_state['on_target_results'],
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columns=["Gene ID", "Start Pos", "End Pos", "Strand", "Target", "gRNA", "Prediction"])
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