Target-Decoy-Based False Discovery Rate Estimation for Large-Scale Metabolite Identification.

Area of Science:

• Analytical Chemistry
• Biochemistry
• Computational Biology

Background:

• Metabolite identification is essential for mass spectrometry (MS)-based metabolomics.
• Assessing the confidence of metabolite assignments remains a significant challenge.
• Current methods lack robust statistical validation for high-throughput analyses.

Purpose of the Study:

• To develop and validate a novel method for estimating the false discovery rate (FDR) in metabolite identification.
• To enhance the reliability and confidence of metabolite assignments in large-scale metabolomics studies.
• To integrate this FDR estimation strategy into existing metabolomics pipelines.

Main Methods:

• Implementation of a target-decoy strategy for metabolite identification.
• Decoy generation by violating chemical octet rules (adding hydrogen atoms).
• Integration into the JUMPm pipeline and evaluation with mzMatch and MZmine 2.
• Validation using simulated datasets with altered MS1 and MS2 spectra.

Main Results:

• The target-decoy strategy effectively estimates FDR for metabolite assignments.
• The method demonstrated reliability across different metabolomics tools and simulated data.
• Successful application to unlabeled and stable-isotope-labeled metabolomic datasets.

Conclusions:

• The target-decoy strategy provides a simple and effective approach for confidence assessment in metabolite identification.
• This method significantly improves the reliability of high-throughput metabolomics data.
• The strategy is broadly applicable to various MS-based metabolomics analyses.