ArticleNature communications2023
DeepRTAlign: toward accurate retention time alignment for large cohort mass spectrometry data analysis.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 26 citations in OpenAlex.
- A multiplatform chromatography-mass spectrometry dataset for targeted and suspect screening of pollutants.Scientific data · 2026Article
- Progress of metabolomics-centric multi-omics research in medicine.iMetaOmics · 2026Review
- WNetAlign: fast and accurate spectra alignment using truncated Wasserstein distance and network simplex.Briefings in bioinformatics · 2026Article
- Designing a Comparative Proteomics Experiment: Retention-Time Alignment and Imputation Algorithms Affect Statistical Comparisons between Samples.Journal of proteome research · 2025Article
- Targeting infection-specific peptides in immunopeptidomics studies for vaccine target discovery.The Journal of experimental medicine · 2025Article
- Assessing the Impact of Measurement Precision on Metabolite Identification Probability in Multidimensional Mass Spectrometry-Based, Reference-Free Metabolomics.Analytical chemistry · 2025Article
- Overcoming Analytical Challenges in Proximity Labeling Proteomics.Journal of mass spectrometry : JMS · 2025Review
- MS-RT: A Method for Evaluating MS/MS Clustering Performance for Metabolomics Data.Journal of proteome research · 2025Article
- Recent Advances in Mass Spectrometry-Based Bottom-Up Proteomics.Analytical chemistry · 2025Review
- A Comparative Study of Network-Based Machine Learning Approaches for Binary Classification in Metabolomics.Metabolites · 2025Article
- Standard operating procedure combined with comprehensive quality control system for multiple LC-MS platforms urinary proteomics.Nature communications · 2025Article
- Advancements in Mass Spectrometry-Based Targeted Metabolomics and Lipidomics: Implications for Clinical Research.Molecules (Basel, Switzerland) · 2024Review
- The 2024 Report on the Human Proteome from the HUPO Human Proteome Project.Journal of proteome research · 2024Review
- Simple and robust high-throughput serum proteomics workflow with low-microflow LC-MS/MS.Analytical and bioanalytical chemistry · 2024Article
- Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review.Briefings in bioinformatics · 2024Review
- DeepRTAlign: toward accurate retention time alignment for large cohort mass spectrometry data analysis.Nature communications · 2023Article
Corrections and comments
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Authors and funding
10 authors at 6 institutions in 1 country.
Funding
Abstract
Retention time (RT) alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based proteomic and metabolomic experiments, especially for large cohort studies. The most popular alignment tools are based on warping function method and direct matching method. However, existing tools can hardly handle monotonic and non-monotonic RT shifts simultaneously. Here, we develop a deep learning-based RT alignment tool, DeepRTAlign, for large cohort LC-MS data analysis. DeepRTAlign has been demonstrated to have improved performances by benchmarking it against current state-of-the-art approaches on multiple real-world and simulated proteomic and metabolomic datasets. The results also show that DeepRTAlign can improve identification sensitivity without compromising quantitative accuracy. Furthermore, using the MS features aligned by DeepRTAlign, we trained and validated a robust classifier to predict the early recurrence of hepatocellular carcinoma. DeepRTAlign provides an advanced solution to RT alignment in large cohort LC-MS studies, which is currently a major bottleneck in proteomics and metabolomics research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.