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PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements

ABSTRACT: Global proteomics datasets to study ash utilization by R. toruloides. PeakDecoder is a workflow for metabolite identification and accurate profiling in multidimensional LC-IM-MS-DIA measurements. It is available at https://github.com/EMSL-Computing/PeakDecoder

INSTRUMENT(S): Q Exactive Plus

ORGANISM(S): Rhodosporidium Toruloides Np11 (ncbitaxon:1130832)

SUBMITTER: Jon K Magnuson

PROVIDER: MSV000091202 | MassIVE | Fri Feb 03 18:22:00 GMT 2023

REPOSITORIES: MassIVE

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PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements.

Bilbao Aivett A Munoz Nathalie N Kim Joonhoon J Orton Daniel J DJ Gao Yuqian Y Poorey Kunal K Pomraning Kyle R KR Weitz Karl K Burnet Meagan M Nicora Carrie D CD Wilton Rosemarie R Deng Shuang S Dai Ziyu Z Oksen Ethan E Gee Aaron A Fasani Rick A RA Tsalenko Anya A Tanjore Deepti D Gardner James J Smith Richard D RD Michener Joshua K JK Gladden John M JM Baker Erin S ES Petzold Christopher J CJ Kim Young-Mo YM Apffel Alex A Magnuson Jon K JK Burnum-Johnson Kristin E KE

Nature communications 20230428 1

Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liq ...[more]

PMID: 37117207

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PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements.

Project description:Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.

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GNPS Synthetic biology microbial samples - LC-IM-MS All Ions

Project description:These data files and results are supplementary material for the publication "PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements". Nature Communications https://doi.org/10.1038/s41467-023-37031-9. PeakDecoder is available at https://github.com/EMSL-Computing/PeakDecoder.

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