Metabolomics Data Preprocessing Using ADAP and MZmine 2.

Area of Science:

• Metabolomics
• Computational Biology
• Analytical Chemistry

Background:

• Untargeted metabolomics using LC-MS or GC-MS generates complex raw data.
• Effective preprocessing is crucial for accurate statistical analysis and biological interpretation.
• Existing tools may lack user-friendliness or specific functionalities for LC-MS/GC-MS data.

Purpose of the Study:

• To introduce the ADAP (Analysis Data Auto Processing) software suite.
• To describe the computational algorithms and workflows for preprocessing LC-MS and GC-MS data.
• To demonstrate the application of ADAP within the MZmine 2 framework.

Main Methods:

• ADAP employs distinct computational workflows for LC-MS and GC-MS data.
• Key steps include extracted ion chromatogram construction, peak detection, spectral deconvolution, and alignment.
• Workflows are integrated into the MZmine 2 framework with dedicated graphical user interfaces.

Main Results:

• ADAP provides efficient and automated preprocessing of untargeted metabolomics data.
• The software facilitates the extraction of compound-relevant information from raw mass spectrometry data.
• User-friendly interfaces enhance the accessibility of advanced computational methods.

Conclusions:

• ADAP offers a robust solution for the critical preprocessing step in untargeted metabolomics.
• The integration with MZmine 2 and intuitive GUIs promote wider adoption and ease of use.
• This facilitates the subsequent statistical analysis and pathway mapping for biological insights.