[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126784-en":3,"doc-seo-126784-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126784,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements - Research overview","Multidimensional measurements integrating advanced separations and mass spectrometry improve untargeted metabolomics for biological and environmental biochemical processes. Limited application has stemmed from the absence of rapid analytical methods and robust algorithms for heterogeneous data. The work presents a sensitive, high-throughput experimental and computational workflow that combines LC, ion mobility spectrometry, and data-independent acquisition MS with PeakDecoder, enabling accurate metabolite profiling and estimation of identification error rates across diverse engineered microbial strains.","Lawrence Berkeley National Laboratory  \nBiological Systems & Engineering  \nTitle  \nPeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements  \nPermalink  \n[https://escholarship.org/uc/item/6mx6n2bc](https://escholarship.org/uc/item/6mx6n2bc)  \nJournal  \nNature Communications, 14(1)  \nISSN  \n2041-1723  \nAuthors  \nBilbao, Aivett  \nMunoz, Nathalie Kim, Joonhoon et al.  \nPublication Date  \n2023  \nDOI  \n10.1038/s41467-023-37031-9  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution-ShareAlike License, availalbe at [https://creativecommons.org/licenses/by-sa/4.0/](https://creativecommons.org/licenses/by-sa/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \n1 PeakDecoder enables machine learning-based metabolite annotation  \n2 and accurate profiling in multidimensional mass spectrometry  \n3 measurements  \n4  \n5 Aivett Bilbao 1,2* , Nathalie Munoz 1,2 , Joonhoon Kim1,2 , Daniel J Orton 1 , Yuqian Gao 1,2 , Kunal  \n6 Poorey3 , Kyle R. Pomraning 1,2 , Karl Weitz 1 , Meagan Burnet 1 , Carrie D. Nicora 1 , Rosemarie  \n7 Wilton4,2 , Shuang Deng 1,2 , Ziyu Dai 1,2 , Ethan Oksen5 , Aaron Gee6 , Rick A. Fasani6 , Anya  \n8 Tsalenko6 , Deepti Tanjore5,2 , James Gardner5,2 , Richard D. Smith 1 , Joshua K. Michener7,2 , John 9 M. Gladden3,2 , Erin S. Baker8 , Christopher J. Petzold5,2 , Young-Mo Kim 1,2 , Alex Apffel6 , Jon K.  \n10 Magnuson 1,2 and Kristin E. Burnum-Johnson 1,2* 11  \n12 1 Pacific Northwest National Laboratory, Richland, WA, USA  \n13 2 US Department of Energy, Agile BioFoundry, Emeryville, CA, USA  \n14 3 Sandia National Laboratory, Livermore, CA, USA  \n15 4 Argonne National Laboratory, Lemont, IL, USA  \n16 5 Lawrence Berkeley National Laboratory, Berkeley, CA, USA  \n17 6 Agilent Research Laboratories, Agilent Technologies, Santa Clara, CA, USA  \n18 7 Oak Ridge National Laboratory, Oak Ridge, TN, USA  \n19 8 Department of Chemistry, University of North Carolina, Chapel Hill, NC, USA 20  \n21 These authors contributed equally: Aivett Bilbao, Nathalie Munoz, and Joonhoon Kim. 22  \n23 * Correspondence:  \n24 [Aivett.Bilbao@pnnl.gov](Aivett.Bilbao@pnnl.gov) and [Kristin.Burnum-Johnson@pnnl.gov](Kristin.Burnum-Johnson@pnnl.gov)  \n25 Abstract  \n26 Multidimensional measurements using state-of-the-art separations and mass spectrometry  \n27 provide advantages in untargeted metabolomics analyses for studying biological and  \n28 environmental bio-chemical processes. However, the lack of rapid analytical methods and robust  \n29 algorithms for these heterogeneous data has limited its application. Here, we develop and  \n30 evaluate a sensitive and high-throughput analytical and computational workflow to enable  \n31 accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility  \n32 spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine  \n33 learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data  \n34 and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling  \n35 of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas  \n36 putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction  \n37 monitoring and gas-chromatography platforms, show that 2683 features could be confidently  \n38 annotated and quantified across 116 microbial sample runs using a library built from 64 standards. 39  \n40 Introduction  \n41 Metabolomics is the study of the small molecules produced by complex networks of cellular  \n42 processes and biochemical reactions in living systems. Metabolites are the end point of the flow  \n43 of information from DNA to the biological phenotype and represent chemical fingerprints directly  \n44 reflecting the physiological co","cbCaicj4x64fZRX1","https://ap.wps.com/l/cbCaicj4x64fZRX1","pdf",2833312,1,38,"English","en",105,"# Abstract\n# Introduction\n# Methods and workflow\n# PeakDecoder algorithm for identification error rates\n# Experimental validation and results\n## Application to engineered microbial strains","[{\"question\":\"What problem does PeakDecoder address in untargeted metabolomics?\",\"answer\":\"It targets the lack of rapid analytical methods and robust algorithms to handle heterogeneous, multidimensional mass spectrometry data for accurate metabolite identification and profiling.\"},{\"question\":\"What experimental workflow is combined with PeakDecoder?\",\"answer\":\"The workflow integrates liquid chromatography, ion mobility spectrometry, and data-independent acquisition mass spectrometry, using PeakDecoder to learn true co-elution and co-mobility patterns from raw data.\"},{\"question\":\"How are metabolite identification results validated?\",\"answer\":\"Results are validated manually and compared against selected reaction monitoring and gas-chromatography platforms.\"}]","PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements - Research overview | PDF",1785934761,96,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"peakdecoder-enables-machine-learning-based-metabolite-annotation-and-accurate-profiling-in-multidimensional-mass-spectrometry-measurements-research-overview","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/peakdecoder-enables-machine-learning-based-metabolite-annotation-and-accurate-profiling-in-multidimensional-mass-spectrometry-measurements-research-overview/126784/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does PeakDecoder address in untargeted metabolomics?","Question",{"text":75,"@type":76},"It targets the lack of rapid analytical methods and robust algorithms to handle heterogeneous, multidimensional mass spectrometry data for accurate metabolite identification and profiling.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What experimental workflow is combined with PeakDecoder?",{"text":80,"@type":76},"The workflow integrates liquid chromatography, ion mobility spectrometry, and data-independent acquisition mass spectrometry, using PeakDecoder to learn true co-elution and co-mobility patterns from raw data.",{"name":82,"@type":73,"acceptedAnswer":83},"How are metabolite identification results validated?",{"text":84,"@type":76},"Results are validated manually and compared against selected reaction monitoring and gas-chromatography platforms.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]