An Automated Workflow Composition System for Liquid Chromatography-Mass Spectrometry Metabolomics Data Processing

Area of Science:

• Computational Biology
• Bioinformatics
• Analytical Chemistry

Background:

• Liquid chromatography-mass spectrometry (LC-MS) metabolomics generates high-dimensional data requiring complex informatics for analysis.
• Developing customized computational workflows for LC-MS metabolomics data processing presents a significant challenge for researchers.
• Ontology-based automated workflow composition (AWC) offers a promising solution for creating adaptable computational pipelines.

Purpose of the Study:

• To assess the feasibility of using an ontology-based AWC system for LC-MS metabolomics data processing.
• To develop and evaluate computational workflows for high-dimensional metabolomics data using the Automated Pipeline Explorer (APE).

Main Methods:

• Utilized the Automated Pipeline Explorer (APE) to construct an AWC system tailored for LC-MS metabolomics data.
• Applied the AWC system across three distinct use cases to predict data processing workflows.
• Performed manual review and assessed the executability and computational viability of the generated workflows.

Main Results:

• APE predicted a total of 145 data processing workflows across the three use cases.
• Identified six traditional and six novel workflows, with one-third of novel workflows found to be executable.
• Achieved a 45% computational viability rate for predicted workflows when selecting the top six per use case.

Conclusions:

• The study successfully demonstrates the feasibility of developing an AWC system for automating LC-MS metabolomics data processing.
• Ontology-based AWC systems like APE can generate both novel and executable workflows, simplifying complex data analysis.
• This approach has the potential to enhance efficiency and reproducibility in metabolomics research.