Focus: a robust workflow for one-dimensional NMR spectral analysis.

Area of Science:

• Analytical Chemistry
• Metabolomics
• Bioinformatics

Background:

• One-dimensional (1D) nuclear magnetic resonance (NMR) is a key technique in metabolomics.
• High-throughput metabolomic studies require efficient and accurate data processing workflows.
• Existing methods face challenges in peak alignment and metabolite identification.

Purpose of the Study:

• To present FOCUS, an integrated methodology for 1D NMR-based metabolomics data analysis.
• To provide a user-friendly workflow for peak feature matrix generation and metabolite identification.
• To address critical data processing difficulties for improved accuracy and efficiency.

Main Methods:

• Development of an integrated data analysis workflow named FOCUS.
• Implementation of a novel spectral alignment algorithm, RUNAS, which does not require a reference spectrum.
• Utilizing positional and correlation peak patterns for enhanced metabolite identification against a reference panel.

Main Results:

• FOCUS provides a complete workflow for 1D NMR metabolomics, generating peak feature matrices and metabolite identification scores.
• The RUNAS algorithm achieves substantial improvements in spectral alignment, especially for unaligned spectra.
• Successful identification of 42 metabolites from human urine and liver extract datasets.

Conclusions:

• FOCUS offers an accurate, efficient, and user-friendly solution for 1D NMR metabolomics data processing.
• The methodology simplifies biological interpretation by providing metabolite identification scores.
• The open-source software facilitates broader adoption and advancement in metabolomic research.