diff --git a/lusSTR/wrappers/snps_convert.py b/lusSTR/wrappers/snps_convert.py
index 4342936..ad73276 100644
--- a/lusSTR/wrappers/snps_convert.py
+++ b/lusSTR/wrappers/snps_convert.py
@@ -30,7 +30,6 @@ def kintelligence_filtering(input):
 
 
 def create_output_table(sample_df, orientation, separate, output_type, software):
-    allele_des = {"A": "1", "C": "2", "G": "3", "T": "4"}
     if orientation == "uas":
         allele_col = "UAS_Allele"
     else:
@@ -41,7 +40,6 @@ def create_output_table(sample_df, orientation, separate, output_type, software)
         compiled_table = create_sample_df(indiv_df, output_type, allele_col)
         if software != "uas":
             compiled_table = check_allele_calls(compiled_table, output_type)
-        compiled_table = compiled_table.replace(allele_des)
         compiled_table.insert(0, "Sample.Name", sample)
         all_samples_df = pd.concat([all_samples_df, compiled_table])
         if separate:
@@ -187,11 +185,11 @@ def main(input, output, kit, strand, separate, refs, software, thresh):
         ref_samples = results[results["SampleID"].isin([refs])]
     if len(ref_samples) > 0:
         ref_table = create_output_table(ref_samples, strand, separate, "reference", software)
-        ref_table.to_csv(f"{output_name}_snp_reference.csv", index=False, sep="\t")
+        ref_table.to_csv(f"{output_name}_snp_reference.tsv", index=False, sep="\t")
     evid_samples = results[~results.SampleID.isin(ref_ids)]
     if len(evid_samples) > 0:
         evid_table = create_output_table(evid_samples, strand, separate, "evidence", software)
-        evid_table.to_csv(f"{output}_snp_evidence.csv", index=False, sep="\t")
+        evid_table.to_csv(f"{output}_snp_evidence.tsv", index=False, sep="\t")
 
 
 if __name__ == "__main__":