Data processing in proteomics and metabolomics: liquid chromatography-mass spectrometry-based omics with divergent computational paradigms.
- 1Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, British Columbia V6T 1Z1, Canada.
- 2Department of Biomedical Sciences, College of Biomedicine, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, SAR, China.
- 3Tianfu Jincheng Laboratory, Chengdu, Sichuan, China.
Abstract
Proteomics and metabolomics are two foundational pillars of omics research. Proteomics focuses on the large-scale, comprehensive studies of the entire set of proteins encoded by the genome. Metabolomics targets the systematic study of chemically diverse small molecules that represent the integrated biochemical output of cellular processes and organismal physiology. Although both fields rely heavily on liquid chromatographic separation coupled with mass spectrometry (LC-MS), their fundamental chemical and biological differences shape every stage of data processing and interpretation. In this review, we move beyond a descriptive comparison of existing pipelines and develop a task-level framework to understand why proteomics and metabolomics have evolved different bioinformatic paradigms despite sharing the same analytical platform. We examine how the degree of prior molecular knowledge shapes feature extraction, identification, confidence assessment, and quantification. We show that the constrained, sequence-defined nature of the proteome enables database-driven peptide identification, rigorous false discovery rate control, and increasingly standardized quantitative workflows. In contrast, we highlight how the open, chemically heterogeneous, and incompletely enumerated metabolome has required peak-shape-based feature detection, retention-time and mass-to-charge ratio-based alignment, extensive signal correction, and confidence-tiered structural annotation. By examining these workflows side by side, we identify shared challenges and field-specific bottlenecks and discuss opportunities for cross-fertilization between proteomics and metabolomics. Ultimately, this review provides a framework for an in-depth understanding of why two LC-MS-based omics fields have developed divergent computational solutions and how future bioinformatic advances may benefit both communities.
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BibTeX
@article{zheng2026data,
title = {{Data processing in proteomics and metabolomics: liquid chromatography-mass spectrometry-based omics with divergent computational paradigms.}},
author = {Amy Zheng and Bin Liao and Liang Zhang and Tao Huan},
journal = {Briefings in bioinformatics},
year = {2026},
month = sep,
doi = {10.1093/bib/bbag591},
url = {https://europepmc.org/article/MED/42932843},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Data processing in proteomics and metabolomics: liquid chromatography-mass spectrometry-based omics with divergent computational paradigms.
AU - Amy Zheng
AU - Bin Liao
AU - Liang Zhang
AU - Tao Huan
PY - 2026
DA - 2026/09/01
JO - Briefings in bioinformatics
DO - 10.1093/bib/bbag591
UR - https://europepmc.org/article/MED/42932843
AB - Proteomics and metabolomics are two foundational pillars of omics research. Proteomics focuses on the large-scale, comprehensive studies of the entire set of proteins encoded by the genome. Metabolomics targets the systematic study of chemically diverse small molecules that represent the integrated biochemical output of cellular processes and organismal physiology. Although both fields rely heavily on liquid chromatographic separation coupled with mass spectrometry (LC-MS), their fundamental chemical and biological differences shape every stage of data processing and interpretation. In this review, we move beyond a descriptive comparison of existing pipelines and develop a task-level framework to understand why proteomics and metabolomics have evolved different bioinformatic paradigms despite sharing the same analytical platform. We examine how the degree of prior molecular knowledge shapes feature extraction, identification, confidence assessment, and quantification. We show that the constrained, sequence-defined nature of the proteome enables database-driven peptide identification, rigorous false discovery rate control, and increasingly standardized quantitative workflows. In contrast, we highlight how the open, chemically heterogeneous, and incompletely enumerated metabolome has required peak-shape-based feature detection, retention-time and mass-to-charge ratio-based alignment, extensive signal correction, and confidence-tiered structural annotation. By examining these workflows side by side, we identify shared challenges and field-specific bottlenecks and discuss opportunities for cross-fertilization between proteomics and metabolomics. Ultimately, this review provides a framework for an in-depth understanding of why two LC-MS-based omics fields have developed divergent computational solutions and how future bioinformatic advances may benefit both communities.
SN - 1467-5463
SN - 1477-4054
ER -