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Auto Characteristic Data

The Auto Characteristic Data module is designed to help users automatically retrieve characteristic ions and neutral losses that are commonly present in selected molecules. This data provides valuable insights into the core structure of compounds and their fragmentation patterns.

Steps to Use the Module

Follow these steps to retrieve characteristic data for the selected molecules:

1. Select Molecules for Analysis

  • Users can select "all" molecules or "select" specific molecules by inputting their IDs.

2. Choose Energy Level

  • Select the energy level for the analysis:
  • 10 eV
  • 20 eV
  • 40 eV
  • For experimental data, users should select "expt" in the energy level option.

3. Define Tolerance Range

  • This parameter defines the mass difference within which fragment ions or neutral losses are considered identical.
  • For in-silico MS/MS data, the tolerance can typically be set around 10E-5.
  • For experimental MS/MS data, the tolerance should be set to 0.02 or lower for higher precision.

Result Table Overview

The results will be displayed in a table with the following columns:

  • Characteristic Ion:
    Lists the fragment ions commonly found in the selected molecules.

  • Characteristic NL:
    Displays the neutral losses that are shared among the selected molecules.

  • Average Intensity:
    Shows the sum of the intensities of the characteristic ions or neutral losses across the selected molecules.

Interacting with the Data

Once the characteristic data is generated:

  • Users can interact with the result table to view and analyze the data.
  • Download the Data:
    By clicking the Download button, users can download the characteristic data in a convenient format for further analysis.

Quick Tips:

  • Use a lower tolerance range for experimental data to ensure higher precision.
  • Select the appropriate energy level to match the experimental conditions or simulation parameters.

The Auto Characteristic Data module makes it easy to automatically identify key fragmentation patterns and neutral losses, offering deeper insights into molecular structures.