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

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...

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Published inNature communications Vol. 14; no. 1; pp. 2461 - 18
Main Authors Bilbao, Aivett, Munoz, Nathalie, Kim, Joonhoon, Orton, Daniel J., Gao, Yuqian, Poorey, Kunal, Pomraning, Kyle R., Weitz, Karl, Burnet, Meagan, Nicora, Carrie D., Wilton, Rosemarie, Deng, Shuang, Dai, Ziyu, Oksen, Ethan, Gee, Aaron, Fasani, Rick A., Tsalenko, Anya, Tanjore, Deepti, Gardner, James, Smith, Richard D., Michener, Joshua K., Gladden, John M., Baker, Erin S., Petzold, Christopher J., Kim, Young-Mo, Apffel, Alex, Magnuson, Jon K., Burnum-Johnson, Kristin E.
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 28.04.2023
Nature Publishing Group
Nature Portfolio
Subjects
49/47
631/114/1305
631/114/1314
631/1647/296
631/45/320
82/16
82/58
Algorithms
Annotations
BASIC BIOLOGICAL SCIENCES
Biological activity
Chemical reactions
Chromatography
Chromatography, Liquid - methods
Construction standards
Gas chromatography
Humanities and Social Sciences
Ion Mobility Spectrometry
Ionic mobility
Ions
Learning algorithms
Libraries
Liquid chromatography
Machine learning
Mass spectrometry
Mass Spectrometry - methods
Mass spectroscopy
Metabolites
Metabolomics
Metabolomics - methods
Microorganisms
Mobility
multidisciplinary
Pseudomonas putida
Science
Science (multidisciplinary)
Scientific imaging
Sensitivity analysis
Workflow
Online AccessGet full text

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Abstract 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. Alternative algorithms exploiting advantages of multidimensional mass spectrometry in untargeted metabolomics are needed. Here, the authors develop and demonstrate PeakDecoder for confident and accurate metabolite profiling in 116 microbial sample runs and using a library built from 64 standards.
AbstractList 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.
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.Alternative algorithms exploiting advantages of multidimensional mass spectrometry in untargeted metabolomics are needed. Here, the authors develop and demonstrate PeakDecoder for confident and accurate metabolite profiling in 116 microbial sample runs and using a library built from 64 standards.
Abstract 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.
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. Alternative algorithms exploiting advantages of multidimensional mass spectrometry in untargeted metabolomics are needed. Here, the authors develop and demonstrate PeakDecoder for confident and accurate metabolite profiling in 116 microbial sample runs and using a library built from 64 standards.
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.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.
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.
ArticleNumber 2461
Author Burnum-Johnson, Kristin E.
Oksen, Ethan
Gao, Yuqian
Deng, Shuang
Baker, Erin S.
Michener, Joshua K.
Petzold, Christopher J.
Burnet, Meagan
Kim, Young-Mo
Tsalenko, Anya
Munoz, Nathalie
Smith, Richard D.
Weitz, Karl
Fasani, Rick A.
Tanjore, Deepti
Gladden, John M.
Poorey, Kunal
Kim, Joonhoon
Magnuson, Jon K.
Orton, Daniel J.
Gee, Aaron
Apffel, Alex
Bilbao, Aivett
Pomraning, Kyle R.
Nicora, Carrie D.
Dai, Ziyu
Wilton, Rosemarie
Gardner, James
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/37117207$$D View this record in MEDLINE/PubMed
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CorporateAuthor Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
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SSID ssj0000391844
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Snippet Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying...
Abstract Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for...
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Title PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements
URI https://link.springer.com/article/10.1038/s41467-023-37031-9
https://www.ncbi.nlm.nih.gov/pubmed/37117207
https://www.proquest.com/docview/2807214475
https://www.proquest.com/docview/2807915788
https://www.osti.gov/servlets/purl/1973113
https://pubmed.ncbi.nlm.nih.gov/PMC10147702
https://doaj.org/article/824cf255ab3a47ab8c1ea26aef65e040
Volume 14
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