ArticleAnalytical chemistry2020
AutoTuner: High Fidelity and Robust Parameter Selection for Metabolomics Data Processing.
Article in Analytical chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Challenges and Good Practices in Preprocessing and Normalization of Untargeted DNA Adductomics Data in Exposomics Research.Analytical chemistry · 2026Article
- mzLearn as a data-driven LC/MS signal detection algorithm that enables pre-trained generative models for untargeted metabolomics.Communications chemistry · 2025Article
- Spatial isotope deep tracing deciphers inter-tissue metabolic crosstalk.Nature communications · 2025Article
- [A review and research prospects on the application of the XCMS mass-spectrometry data-processing software in the environmental science field].Se pu = Chinese journal of chromatography · 2025Review
- Trimethylamine-N-oxide accelerates osteoporosis by PERK activation of ATF5 unfolding.Cellular and molecular life sciences : CMLS · 2024Article
- Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review.Briefings in bioinformatics · 2024Review
- Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline.PLoS computational biology · 2024Article
- Computational methods for processing and interpreting mass spectrometry-based metabolomics.Essays in biochemistry · 2024Review
- Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline.bioRxiv : the preprint server for biology · 2024Article
- Identification of biological signatures of cruciferous vegetable consumption utilizing machine learning-based global untargeted stable isotope traced metabolomics.Frontiers in nutrition · 2024Article
- Trackable and scalable LC-MS metabolomics data processing using asari.Nature communications · 2023Article
- Critical review on data processing algorithms in non-target screening: challenges and opportunities to improve result comparability.Analytical and bioanalytical chemistry · 2023Review
- Optimizing XCMS parameters for GC-MS metabolomics data processing: a case study.Metabolomics : Official journal of the Metabolomic Society · 2023Article
- Benthic exometabolites and their ecological significance on threatened Caribbean coral reefs.ISME communications · 2022Article
- Innovative Application of Metabolomics on Bioactive Ingredients of Foods.Foods (Basel, Switzerland) · 2022Review
- Using MetaboAnalyst 5.0 for LC-HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data.Nature protocols · 2022Review
- Power of mzRAPP-Based Performance Assessments in MS1-Based Nontargeted Feature Detection.Analytical chemistry · 2022Article
- Optimization of metabolomic data processing using NOREVA.Nature protocols · 2022Review
- New software tools, databases, and resources in metabolomics: updates from 2020.Metabolomics : Official journal of the Metabolomic Society · 2021Review
- Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics-Standardization, Coverage, and Throughput.Analytical chemistry · 2021Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Untargeted metabolomics experiments provide a snapshot of cellular metabolism but remain challenging to interpret due to the computational complexity involved in data processing and analysis. Prior to any interpretation, raw data must be processed to remove noise and to align mass-spectral peaks across samples. This step requires selection of dataset-specific parameters, as erroneous parameters can result in noise inflation. While several algorithms exist to automate parameter selection, each depends on gradient descent optimization functions. In contrast, our new parameter optimization algorithm, AutoTuner, obtains parameter estimates from raw data in a single step as opposed to many iterations. Here, we tested the accuracy and the run-time of AutoTuner in comparison to isotopologue parameter optimization (IPO), the most commonly used parameter selection tool, and compared the resulting parameters' influence on the properties of feature tables after processing. We performed a Monte Carlo experiment to test the robustness of AutoTuner parameter selection and found that AutoTuner generated similar parameter estimates from random subsets of samples. We conclude that AutoTuner is a desirable alternative to existing tools, because it is scalable, highly robust, and very fast (∼100-1000× speed improvement from other algorithms going from days to minutes). AutoTuner is freely available as an R package through BioConductor.
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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.