fix

app.py

@@ -287,8 +287,8 @@ if selected_model == 'Cas9':
                     # cas9on.create_bed_file_from_df(df, bed_file_path)
                     # cas9on.create_csv_from_df(df, csv_file_path)
 
-                    # Assuming create_bigwig_from_df is a function that generates a BigWig file from the DataFrame
-                    cas9on.create_bigwig_from_df(df, bigwig_file_path)
+                    # Assuming create_bigwig is a function that generates a BigWig file from the DataFrame
+                    cas9on.create_bigwig(df, bigwig_file_path)
 
                     # Prepare an in-memory buffer for the ZIP file
                     zip_buffer = io.BytesIO()

cas9on.py

@@ -203,24 +203,28 @@ def process_gene(gene_symbol, model_path):
 #     df.to_csv(output_path, index=False)
 
 
-def create_bigwig(df, bigwig_path, chrom_sizes_path):
-    bw = pyBigWig.open(bigwig_path, "w")
-
-    # Load chromosome sizes
-    chrom_sizes = {}
-    with open(chrom_sizes_path, 'r') as f:
-        for line in f:
-            chrom, size = line.strip().split()
-            chrom_sizes[chrom] = int(size)
-
-    bw.addHeader(list(chrom_sizes.items()))
-
-    for chrom in df['Chr'].unique():
-        chrom_df = df[df['Chr'] == chrom]
-        bw.addEntries(
-            chrom,
-            chrom_df['Start Pos'].astype(int).tolist(),
-            ends=chrom_df['End Pos'].astype(int).tolist(),
-            values=chrom_df['Prediction'].astype(float).tolist()
-        )
-    bw.close()
+def create_bigwig(predictions, bigwig_path):
+    # Convert predictions to DataFrame if it's a list of lists
+    if isinstance(predictions, list):
+        import pandas as pd
+        df = pd.DataFrame(predictions, columns=["Chr", "Start Pos", "End Pos", "Strand", "Transcript", "Exon", "Target", "gRNA", "Prediction"])
+    else:
+        df = predictions  # Assuming predictions is already a DataFrame
+
+    # Calculate chromosome sizes as the maximum end position per chromosome
+    chrom_sizes = df.groupby('Chr')['End Pos'].max().to_dict()
+
+    # Create a BigWig file
+    with pyBigWig.open(bigwig_path, "w") as bw:
+        # Add chromosome sizes to the header
+        bw.addHeader(list(chrom_sizes.items()))
+
+        # Add entries for each prediction
+        for index, row in df.iterrows():
+            chrom = row['Chr']
+            start = int(row['Start Pos']) - 1  # BigWig positions are 0-based
+            end = int(row['End Pos'])
+            score = float(row['Prediction'])
+
+            # Add the entry to the BigWig file
+            bw.addEntries([chrom], [start], ends=[end], values=[score])