MolNetEnhancerMod

MolNetEnhancerMod is an alternative to the non-working MolNetEnhancer. It uses a modified approximation to retrieve SMILES classification, supporting both NPClassifier and ClassyFire for robust molecular networking and detailed structural insights in metabolomics data.

Select what you need

A. For Map chemical class information using NPClassifier to mass spectral molecular networks

B. For Map chemical class information using Classyfire to mass spectral molecular networks

To map chemical class information to a mass spectral molecular network, you need to:

• Create a molecular network using the classical or feature-based workflow through the Global Natural Products Social Molecular Networking (GNPS) platform
• Perform in silico structure annotation using Network Annotation Propagation (NAP), DEREPLICATOR, or another tool of preference for in silico structure annotation

Then execute the code in MolNenEnhancerMod_NPC.R line by line.

Disclaimer: Currently, the code only works with Feature Base Molecular Networking and NAP. DEREPLICATOR will be added soon.

A. For Map chemical class information using NPClassifier to mass spectral molecular networks

This script is a modification of the original MolNetEnhancer workflow published by Madeleine Ernst. This version assigns chemical class annotations using NPClassifier.

The only things that you need to replace are:

1. Your GNPS Job ID

2. Your NAP Job ID

3. Replace the aforementioned IDs in the R script (To replicate results storage in Madeleine GitHub I will use the same data)

4. Download the MolNenEnhancerMod_NPC.R

5. Open it in your preferred R development environment, in this case, I use RStudio

6. Replace the IDs from your work in the corresponding place

7. Run the code as usual

8. After finishing, it will generate a CSV file called Molnetenhancer in your working directory

9. Open Cytoscape and import the Molecular Network File (You can find the graphML file inside of the folder called GNPS_output_graphML that was generated after run the previous code)

   The graphML file is located inside of gnps_molecular_network_graphml

   

   

10. After loaded, the graphML file will look like this:

    

11. Import the Molnethancer CSV file into the network

    

    

    cluster.index need to have the key

    

    Then press Ok

12. Navigate to Style -> Fill Color

    

    In Column, select the Classification level that you need (Pathway, Superclass, or Class) For the example, I chose NPC_Pathway_Consensus, in Mapping Type select Discrete Mapping The NPC Pathways will be displayed

    

13. Select the colors that you want. For example, I right-clicked in the row next to the Alkaloids, and inside Mapping Values Generator, I selected Rainbow

    

14. Your network enhanced with NPClassifier is ready!

    

B. For Map chemical class information using Classyfire to mass spectral molecular networks

This script is a modification of the original MolNetEnhancer workflow published by Madeleine Ernst. This version assigns chemical class annotations using Classyfire.

The only things that you need to replace are:

1. Your GNPS Job ID

2. Your NAP Job ID

3. Replace the aforementioned IDs in the R script (To replicate results storage in Madeleine GitHub I will use the same data)

4. Download the MolNenEnhancerMod_Classyfire.R

5. Open it in your preferred R development environment; in this case, I use RStudio

6. Replace the IDs from your work in the corresponding place

7. Run the code as usual until line #113

8. Line 113 will generate TSV files in your working directory. Each run in the Classyfire web app needs fewer than 1000 features, so if you have more than 1000 features, the code will divide the features into multiple TSV files containing fewer than 1000 features per file

9. After running line 113. You must stop and find the TSV files. For this example, two TSV files were generated

10. You need to upload each TSV file individually to the [Classyfire web app] (http://classyfire.wishartlab.com/)

11. After running each file individually on the Classyfire web. You need to download the output files in SDF format and save them in the working directory.

12. Continue running the script (line 125).

13. After finishing, it will generate a CSV file called Molnetenhancer in your working directory

14. Open Cytoscape and import the Molecular Network File (You can find the graphML file inside of the folder called GNPS_output_graphML that was generated after run the previous code)

The graphML file is located inside of gnps_molecular_network_graphml

15. After loaded, the graphML file will look like this:

    

16. Import the Molnethancer CSV file into the network

    

    

    cluster.index need to have the key

    

    Then press Ok

17. Navigate to Style -> Fill Color

    

    In Column, select the Classification level that you need (Superslass, Class, or Subclass) For the example, I chose Superclass_Consensus, in Mapping Type select Discrete Mapping The Superclasses will be displayed

    

18. Select the colors that you want. For example, I right-clicked in the row next to the Alkaloids and derivatives, and inside Mapping Values Generator, I selected Rainbow

    

19. Your network enhanced with NPClassifier is ready!