diff --git a/assets/mash_blast_correlation.py b/assets/mash_blast_correlation.py
index 8663805..a6e7485 100644
--- a/assets/mash_blast_correlation.py
+++ b/assets/mash_blast_correlation.py
@@ -138,7 +138,9 @@ def mantel_tester(blast_paths, mash_paths, pval=0.01):
 plt.suptitle("")  # Elimina el título por defecto
 plt.xlabel("Dataset")  # Etiqueta para el eje x
 plt.ylabel("Mantel correlation value")  # Etiqueta para el eje y
-ax.set_xticklabels([ticklabel.get_text().capitalize() for ticklabel in ax.get_xticklabels()])
+ax.set_xticklabels(
+    [ticklabel.get_text().capitalize() for ticklabel in ax.get_xticklabels()]
+)
 
 # Guarda el boxplot como PNG
 plt.savefig(
diff --git a/taranis/distance.py b/taranis/distance.py
index 87c3b31..b42ee34 100644
--- a/taranis/distance.py
+++ b/taranis/distance.py
@@ -108,14 +108,14 @@ def create_matrix(self, mask_values: list) -> pd.DataFrame:
             pd.DataFrame: Hamming distance matrix as panda DataFrame
         """
         # Mask unwanted values directly in the DataFrame
-        regex_pattern = '|'.join([f".*{value}.*" for value in mask_values])
+        regex_pattern = "|".join([f".*{value}.*" for value in mask_values])
         self.allele_matrix.replace(regex_pattern, np.nan, regex=True, inplace=True)
 
         # Get unique values excluding NaN
-        unique_values = pd.unique(
-            self.allele_matrix.values.ravel("K")
-        )
-        unique_values = unique_values[~pd.isna(unique_values)]  # Exclude NaNs from unique values
+        unique_values = pd.unique(self.allele_matrix.values.ravel("K"))
+        unique_values = unique_values[
+            ~pd.isna(unique_values)
+        ]  # Exclude NaNs from unique values
 
         # Create binary matrix ('1' or '0' ) matching the input matrix vs the unique_values[0]
         # astype(int) is used to transform the boolean matrix into integer
@@ -141,4 +141,8 @@ def create_matrix(self, mask_values: list) -> pd.DataFrame:
         pairwise_valid_counts = pairwise_valid.sum(axis=2)
         distance_matrix = pairwise_valid_counts - H
 
-        return pd.DataFrame(distance_matrix, index=self.allele_matrix.index, columns=self.allele_matrix.index)
+        return pd.DataFrame(
+            distance_matrix,
+            index=self.allele_matrix.index,
+            columns=self.allele_matrix.index,
+        )
diff --git a/taranis/utils.py b/taranis/utils.py
index 0875833..77f6748 100644
--- a/taranis/utils.py
+++ b/taranis/utils.py
@@ -334,7 +334,7 @@ def filter_df(
     row_thr /= 100
 
     # Identify filter values and create a mask for the DataFrame
-    regex_pattern = '|'.join(filter_values)  # This creates 'ASM|LNF|EXC'
+    regex_pattern = "|".join(filter_values)  # This creates 'ASM|LNF|EXC'
 
     # Apply regex across the DataFrame to create a mask
     mask = df.applymap(lambda x: bool(re.search(regex_pattern, str(x))))