Comprehensive investigation of pathway enrichment methods for functional interpretation of LC–MS global metabolomics data

• Published in: Briefings in bioinformatics Vol. 24; no. 1
• Main Authors: Lu, Yao, Pang, Zhiqiang, Xia, Jianguo
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 19.01.2023; Oxford Publishing Limited (England)
• Subjects: Annotations; Benchmarks; Best practice; Chromatography, Liquid - methods; COVID-19; Enrichment; Fundulus heteroclitus; Humans; Inflammatory bowel diseases; Liquid chromatography; Mass spectrometry; Mass spectroscopy; Metabolites; Metabolome; Metabolomics; Metabolomics - methods; Microbial activity; Microorganisms; Review; Tandem Mass Spectrometry; LC-MS; peak annotation; global metabolomics; identifiability index; pathway enrichment analysis
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbac553

Abstract Background: Global or untargeted metabolomics is widely used to comprehensively investigate metabolic profiles under various pathophysiological conditions such as inflammations, infections, responses to exposures or interactions with microbial communities. However, biological interpretation of global metabolomics data remains a daunting task. Recent years have seen growing applications of pathway enrichment analysis based on putative annotations of liquid chromatography coupled with mass spectrometry (LC–MS) peaks for functional interpretation of LC–MS-based global metabolomics data. However, due to intricate peak-metabolite and metabolite-pathway relationships, considerable variations are observed among results obtained using different approaches. There is an urgent need to benchmark these approaches to inform the best practices. Results: We have conducted a benchmark study of common peak annotation approaches and pathway enrichment methods in current metabolomics studies. Representative approaches, including three peak annotation methods and four enrichment methods, were selected and benchmarked under different scenarios. Based on the results, we have provided a set of recommendations regarding peak annotation, ranking metrics and feature selection. The overall better performance was obtained for the mummichog approach. We have observed that a ~30% annotation rate is sufficient to achieve high recall (~90% based on mummichog), and using semi-annotated data improves functional interpretation. Based on the current platforms and enrichment methods, we further propose an identifiability index to indicate the possibility of a pathway being reliably identified. Finally, we evaluated all methods using 11 COVID-19 and 8 inflammatory bowel diseases (IBD) global metabolomics datasets.