update result table columns

pages/User_Guide.py

@@ -57,7 +57,7 @@ st.markdown(
     | protein_name | Protein name you provided in the input FASTA. |
     | GO_ID | Gene Ontology term ID. |
     | GO_term | Gene Ontology term name. |
-    | GO_aspect | Gene Ontology term aspect. |
+    | GO_category | Gene Ontology term aspect (molecular_function, biological_process or cellular_component). |
     | domain_locations | List of locations of the domain in the protein sequence. |
     | probability | Probability of the domain being associated with the GO term. You can find more information about calculation of this score in our [pre-print article](https://www.biorxiv.org/content/10.1101/2022.11.03.514980v1). |
     | domain_accession | InterPro domain accession. |

run_domain2go_app.py

@@ -109,8 +109,8 @@ def generate_function_predictions(domains_df, mapping_path):
     
     else:
         merged_df['protein_name'] = domains_df['protein_name'].iloc[0]
-        merged_df = merged_df[['protein_name', 'GO', 'GO_aspect', 'GO_name', 'domain_locations', 's', 'domain_accession', 'domain_name',]]
-        merged_df.columns = ['protein_name', 'GO_ID', 'GO_aspect', 'GO_name', 'domain_locations', 'probability', 'domain_accession', 'domain_name',]
+        merged_df = merged_df[['protein_name', 'GO', 'GO_name', 'GO_aspect', 'domain_locations', 's', 'domain_accession', 'domain_name',]]
+        merged_df.columns = ['protein_name', 'GO_ID', 'GO_term', 'GO_category', 'domain_locations', 'probability', 'domain_accession', 'domain_name',]
 
         # save protein function predictions
         protein_name = domains_df['protein_name'].iloc[0]