TAILOR-MS

TAILOR-MS automates identification and profiling of triacylglycerol (TAG) species from liquid chromatography tandem mass spectrometry (LC/MS) data to resolve ambiguous combinations of three fatty acyl groups.

Key Features:

• Python implementation: Implemented in Python for bioinformatic processing of LC/MS data.
• Automated Structural Determination: Automates structural identification of triacylglycerols from LC/MS data, reducing manual interpretation time.
• Targeted LC/MS Method Generation: Generates targeted LC/MS methods for detecting specific TAG species.
• Comprehensive Profiling: Identified 247 distinct TAG species in bovine milk and predicted an additional 317 potential species.
• Comparative Analysis: Enables comparison of TAG composition between matrices such as bovine milk and infant formulas, identifying over 200 TAG species in infant formula samples.
• Robust Predictive Performance: Produces reliable structural predictions even when some constituent peaks are missing.

Scientific Applications:

• Lipidomics profiling: High-throughput identification and profiling of triacylglycerol species in complex biological samples.
• Nutrition science: Characterization of TAG composition in bovine milk and infant formula for nutritional and compositional studies.
• Metabolic research: Analysis of TAG species distributions to inform metabolic and biochemical investigations.
• Targeted assay development: Support for development of targeted LC/MS assays to detect and quantify specific TAG species.

Methodology:

Implemented in Python; performs automated structural identification of triacylglycerols from LC/MS data, generates targeted LC/MS methods, predicts additional TAG species, and supports structural prediction when constituent peaks are absent.