HeuSMA: A Multigradient LC-MS Strategy for Improving Peak Identification in Untargeted Metabolomics

HeuSMA: A Multigradient LC-MS Strategy for Improving Peak Identification in Untargeted Metabolomics

Metabolomics, which involves the comprehensive analysis of small molecules within biological systems, plays a crucial role in elucidating the biochemical underpinnings of physiological processes and disease conditions. However, current coverage of the metabolome remains limited. In this study, we pr...

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Bibliographic Details
Main Author: Yao-Yu Chen (11083566) (author)
Other Authors: Na An (299087) (author), Yan-Zhen Wang (11249236) (author), Peng-Cheng Mei (12010472) (author), Jun-Di Hao (3117213) (author), Song-Mei Liu (278770) (author), Quan-Fei Zhu (4852795) (author), Yu-Qi Feng (438357) (author)
Published: 2025
Subjects:
Biochemistry
Genetics
Biotechnology
Marine Biology
Space Science
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Chemical Sciences not elsewhere classified
Information Systems not elsewhere classified
validation results demonstrate
retention index system
peak identification accuracy
multiple chromatographic gradients
improving peak identification
human hepatocellular carcinoma
https :// github
derive meaningful conclusions
efficiently manage data
quantitative metabolomics data
multiple gradient conditions
metabolome remains limited
heuristic peak picking
untargeted metabolomics metabolomics
quality control sample
serum metabolomics analysis
untargeted metabolomics analysis
untargeted metabolomics
metabolome coverage
heuristic strategy
disease conditions
ms data
comprehensive analysis
analysis process
pooled sample
given sample
ms analysis
thereby facilitating
physiological processes
multigradient lc
metabolite coverage
metabolite annotation
hp ),
hcc ).
g .,
enabling researchers
current coverage
crucial role
biochemical underpinnings
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