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February 2, 2026Analytical Chemistry0 citations

Trend-Aligner: A New Method for Aligning Features in Untargeted LC-MS Data

Trend-Aligner: A Retention Time Modeling-Based Feature Alignment Method for Untargeted LC–MS Data Analysis

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Authors

YLYuqi LiuSRShouyang RenÉCÉtienne Caron

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Overview

Trend-Aligner demonstrates improved feature alignment accuracy in untargeted LC-MS data, suggesting better quantification capabilities.

Key Points

  • To develop an advanced feature alignment algorithm for untargeted LC-MS data that models retention time shifts based on chromatographic principles.
  • Introduced Trend-Aligner for feature alignment based on retention time modeling.
  • Decomposed retention time shifts into global and local components.
  • Developed a reference-based accuracy benchmarking strategy.
  • Annotated metabolomic and proteomic datasets with consensus features.
  • Evaluated performance against 11 widely used alignment algorithms.
  • Trend-Aligner achieved the highest accuracy across various datasets.
  • Demonstrated an 82.5% increase in identified peptides post-MBR compared to MaxQuant.
  • Exhibited high sensitivity and specificity in aligning features.

Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/6980fd18c1c9540dea80ed34https://doi.org/10.1021/acs.analchem.5c05354
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