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

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.

doi:10.1038/s41467-023-37031-9

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

Journal Article · Fri Apr 28 00:00:00 UTC 2023 · Nature Communications

DOI: https://doi.org/10.1038/s41467-023-37031-9 · OSTI ID:1973113

^[1];

^[2];

^[1]; Poorey, Kunal ^[3];

^[1];

^[2]; Burnet, Meagan ^[2];

^[2]; Wilton, Rosemarie ^[4]; Deng, Shuang ^[1]; Dai, Ziyu ^[1];

^[5]; Gee, Aaron ^[6]; Fasani, Rick A. ^[6]; Tsalenko, Anya ^[6]; Tanjore, Deepti ^[7]; Gardner, James ^[7];

^[2] more »

Pacific Northwest National Lab. (PNNL), Richland, WA (United States); USDOE Agile BioFoundry, Emeryville, CA (United States)
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Argonne National Lab. (ANL), Argonne, IL (United States); USDOE Agile BioFoundry, Emeryville, CA (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Agilent Technologies, Santa Clara, CA (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); USDOE Agile BioFoundry, Emeryville, CA (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); USDOE Agile BioFoundry, Emeryville, CA (United States)
Sandia National Lab. (SNL-CA), Livermore, CA (United States); USDOE Agile BioFoundry, Emeryville, CA (United States)
University of North Carolina, Chapel Hill, NC (United States)

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.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: National Institutes of Health (NIH); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Bioenergy Technologies Office (BETO)

Grant/Contract Number:: AC02-06CH11357; AC05-00OR22725; AC05-76RL01830

OSTI ID:: 1973113

Report Number(s):: PNNL-SA-174727

Journal Information:: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 14; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

References (67)

PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints Yap, Chun Wei Journal of Computational Chemistry, Vol. 32, Issue 7 https://doi.org/10.1002/jcc.21707	journal	December 2010
An improved method for the construction of decoy peptide MS/MS spectra suitable for the accurate estimation of false discovery rates Ahrné, Erik; Ohta, Yuki; Nikitin, Frederic PROTEOMICS, Vol. 11, Issue 20 https://doi.org/10.1002/pmic.201000665	journal	September 2011
Processing strategies and software solutions for data-independent acquisition in mass spectrometry Bilbao, Aivett; Varesio, Emmanuel; Luban, Jeremy PROTEOMICS, Vol. 15, Issue 5-6 https://doi.org/10.1002/pmic.201400323	journal	February 2015
Rapid screening methods for yeast sub‐metabolome analysis with a high‐resolution ion mobility quadrupole time‐of‐flight mass spectrometer Mairinger, Teresa; Kurulugama, Ruwan; Causon, Tim J. Rapid Communications in Mass Spectrometry, Vol. 33, Issue S2 https://doi.org/10.1002/rcm.8420	journal	May 2019
Overview of Mass Spectrometry-Based Metabolomics: Opportunities and Challenges Gowda, G. A. Nagana; Djukovic, Danijel Mass Spectrometry in Metabolomics https://doi.org/10.1007/978-1-4939-1258-2_1	book	January 2014
Using Skyline to Analyze Data-Containing Liquid Chromatography, Ion Mobility Spectrometry, and Mass Spectrometry Dimensions MacLean, Brendan X.; Pratt, Brian S.; Egertson, Jarrett D. Journal of the American Society for Mass Spectrometry, Vol. 29, Issue 11 https://doi.org/10.1007/s13361-018-2028-5	journal	July 2018
From dirt to industrial applications: Pseudomonas putida as a Synthetic Biology chassis for hosting harsh biochemical reactions Nikel, Pablo I.; Chavarría, Max; Danchin, Antoine Current Opinion in Chemical Biology, Vol. 34 https://doi.org/10.1016/j.cbpa.2016.05.011	journal	October 2016
Challenges, progress and promises of metabolite annotation for LC–MS-based metabolomics Chaleckis, Romanas; Meister, Isabel; Zhang, Pei Current Opinion in Biotechnology, Vol. 55 https://doi.org/10.1016/j.copbio.2018.07.010	journal	February 2019
Evaluation of chromosomal insertion loci in the Pseudomonas putida KT2440 genome for predictable biosystems design Chaves, Julie E.; Wilton, Rosemarie; Gao, Yuqian Metabolic Engineering Communications, Vol. 11 https://doi.org/10.1016/j.mec.2020.e00139	journal	December 2020
Deep learning, reinforcement learning, and world models Matsuo, Yutaka; LeCun, Yann; Sahani, Maneesh Neural Networks, Vol. 152 https://doi.org/10.1016/j.neunet.2022.03.037	journal	August 2022
Ion mobility spectrometry and the omics: Distinguishing isomers, molecular classes and contaminant ions in complex samples Burnum-Johnson, Kristin E.; Zheng, Xueyun; Dodds, James N. TrAC Trends in Analytical Chemistry, Vol. 116 https://doi.org/10.1016/j.trac.2019.04.022	journal	July 2019
Carotenoid-based phenotypic screen of the yeast deletion collection reveals new genes with roles in isoprenoid production Özaydın, Bilge; Burd, Helcio; Lee, Taek Soon Metabolic Engineering, Vol. 15 https://doi.org/10.1016/j.ymben.2012.07.010	journal	January 2013
Establishing a synthetic pathway for high-level production of 3-hydroxypropionic acid in Saccharomyces cerevisiae via β-alanine Borodina, Irina; Kildegaard, Kanchana R.; Jensen, Niels B. Metabolic Engineering, Vol. 27 https://doi.org/10.1016/j.ymben.2014.10.003	journal	January 2015
MetaboliteDetector: Comprehensive Analysis Tool for Targeted and Nontargeted GC/MS Based Metabolome Analysis Hiller, Karsten; Hangebrauk, Jasper; Jäger, Christian Analytical Chemistry, Vol. 81, Issue 9 https://doi.org/10.1021/ac802689c	journal	May 2009
DaDIA: Hybridizing Data-Dependent and Data-Independent Acquisition Modes for Generating High-Quality Metabolomic Data Guo, Jian; Shen, Sam; Xing, Shipei Analytical Chemistry, Vol. 93, Issue 4 https://doi.org/10.1021/acs.analchem.0c05022	journal	January 2021
MetDIA: Targeted Metabolite Extraction of Multiplexed MS/MS Spectra Generated by Data-Independent Acquisition Li, Hao; Cai, Yuping; Guo, Yuan Analytical Chemistry, Vol. 88, Issue 17 https://doi.org/10.1021/acs.analchem.6b02122	journal	August 2016
Customized Consensus Spectral Library Building for Untargeted Quantitative Metabolomics Analysis with Data Independent Acquisition Mass Spectrometry and MetaboDIA Workflow Chen, Gengbo; Walmsley, Scott; Cheung, Gemmy C. M. Analytical Chemistry, Vol. 89, Issue 9 https://doi.org/10.1021/acs.analchem.6b05006	journal	April 2017
An Interlaboratory Evaluation of Drift Tube Ion Mobility–Mass Spectrometry Collision Cross Section Measurements Stow, Sarah M.; Causon, Tim J.; Zheng, Xueyun Analytical Chemistry, Vol. 89, Issue 17 https://doi.org/10.1021/acs.analchem.7b01729	journal	August 2017
Structure Annotation of All Mass Spectra in Untargeted Metabolomics Blaženović, Ivana; Kind, Tobias; Sa, Michael R. Analytical Chemistry, Vol. 91, Issue 3 https://doi.org/10.1021/acs.analchem.8b04698	journal	January 2019
XY-Meta: A High-Efficiency Search Engine for Large-Scale Metabolome Annotation with Accurate FDR Estimation Li, Dehua; Liu, Binghang; Zheng, Hancheng Analytical Chemistry, Vol. 92, Issue 8 https://doi.org/10.1021/acs.analchem.9b03355	journal	March 2020
Comparison of Full-Scan, Data-Dependent, and Data-Independent Acquisition Modes in Liquid Chromatography–Mass Spectrometry Based Untargeted Metabolomics Guo, Jian; Huan, Tao Analytical Chemistry, Vol. 92, Issue 12 https://doi.org/10.1021/acs.analchem.9b05135	journal	May 2020
BioCompoundML: A General Biofuel Property Screening Tool for Biological Molecules Using Random Forest Classifiers Whitmore, Leanne S.; Davis, Ryan W.; McCormick, Robert L. Energy & Fuels, Vol. 30, Issue 10 https://doi.org/10.1021/acs.energyfuels.6b01952	journal	September 2016
A Preprocessing Tool for Enhanced Ion Mobility–Mass Spectrometry-Based Omics Workflows Bilbao, Aivett; Gibbons, Bryson C.; Stow, Sarah M. Journal of Proteome Research, Vol. 21, Issue 3 https://doi.org/10.1021/acs.jproteome.1c00425	journal	August 2021
Ranking Fragment Ions Based on Outlier Detection for Improved Label-Free Quantification in Data-Independent Acquisition LC–MS/MS Bilbao, Aivett; Zhang, Ying; Varesio, Emmanuel Journal of Proteome Research, Vol. 14, Issue 11 https://doi.org/10.1021/acs.jproteome.5b00394	journal	October 2015
Target-Decoy-Based False Discovery Rate Estimation for Large-Scale Metabolite Identification Wang, Xusheng; Jones, Drew R.; Shaw, Timothy I. Journal of Proteome Research, Vol. 17, Issue 7 https://doi.org/10.1021/acs.jproteome.8b00019	journal	May 2018
pmartR : Quality Control and Statistics for Mass Spectrometry-Based Biological Data Stratton, Kelly G.; Webb-Robertson, Bobbie-Jo M.; McCue, Lee Ann Journal of Proteome Research, Vol. 18, Issue 3 https://doi.org/10.1021/acs.jproteome.8b00760	journal	January 2019
Combined Statistical Analyses of Peptide Intensities and Peptide Occurrences Improves Identification of Significant Peptides from MS-Based Proteomics Data Webb-Robertson, Bobbie-Jo M.; McCue, Lee Ann; Waters, Katrina M. Journal of Proteome Research, Vol. 9, Issue 11 https://doi.org/10.1021/pr1005247	journal	November 2010
Spectrum-based Method to Generate Good Decoy Libraries for Spectral Library Searching in Peptide Identifications Cheng, Chia-Ying; Tsai, Chia-Feng; Chen, Yu-Ju Journal of Proteome Research, Vol. 12, Issue 5 https://doi.org/10.1021/pr301039b	journal	April 2013
Posterior Error Probabilities and False Discovery Rates: Two Sides of the Same Coin Käll, Lukas; Storey, John D.; MacCoss, Michael J. Journal of Proteome Research, Vol. 7, Issue 1 https://doi.org/10.1021/pr700739d	journal	January 2008
MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification Cox, Jürgen; Mann, Matthias Nature Biotechnology, Vol. 26, Issue 12 https://doi.org/10.1038/nbt.1511	journal	November 2008
OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data Röst, Hannes L.; Rosenberger, George; Navarro, Pedro Nature Biotechnology, Vol. 32, Issue 3 https://doi.org/10.1038/nbt.2841	journal	March 2014
Identification and microbial production of a terpene-based advanced biofuel Peralta-Yahya, Pamela P.; Ouellet, Mario; Chan, Rossana Nature Communications, Vol. 2, Issue 1 https://doi.org/10.1038/ncomms1494	journal	September 2011
mProphet: automated data processing and statistical validation for large-scale SRM experiments Reiter, Lukas; Rinner, Oliver; Picotti, Paola Nature Methods, Vol. 8, Issue 5 https://doi.org/10.1038/nmeth.1584	journal	March 2011
FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry Palmer, Andrew; Phapale, Prasad; Chernyavsky, Ilya Nature Methods, Vol. 14, Issue 1 https://doi.org/10.1038/nmeth.4072	journal	November 2016
Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses Rosenberger, George; Bludau, Isabell; Schmitt, Uwe Nature Methods, Vol. 14, Issue 9 https://doi.org/10.1038/nmeth.4398	journal	August 2017
Significance estimation for large scale metabolomics annotations by spectral matching Scheubert, Kerstin; Hufsky, Franziska; Petras, Daniel Nature Communications, Vol. 8, Issue 1 https://doi.org/10.1038/s41467-017-01318-5	journal	November 2017
Building a global alliance of biofoundries Hillson, Nathan; Caddick, Mark; Cai, Yizhi Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-10079-2	journal	May 2019
DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics Alka, Oliver; Shanthamoorthy, Premy; Witting, Michael Nature Communications, Vol. 13, Issue 1 https://doi.org/10.1038/s41467-022-29006-z	journal	March 2022
Bacillus subtilis biofilm matrix components target seed oil bodies to promote growth and anti-fungal resistance in melon Berlanga-Clavero, M. V.; Molina-Santiago, C.; Caraballo-Rodríguez, A. M. Nature Microbiology, Vol. 7, Issue 7 https://doi.org/10.1038/s41564-022-01134-8	journal	June 2022
A lipidome atlas in MS-DIAL 4 Tsugawa, Hiroshi; Ikeda, Kazutaka; Takahashi, Mikiko Nature Biotechnology, Vol. 38, Issue 10 https://doi.org/10.1038/s41587-020-0531-2	journal	June 2020
DecoID improves identification rates in metabolomics through database-assisted MS/MS deconvolution Stancliffe, Ethan; Schwaiger-Haber, Michaela; Sindelar, Miriam Nature Methods, Vol. 18, Issue 7 https://doi.org/10.1038/s41592-021-01195-3	journal	July 2021
Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification Li, Yuanyue; Kind, Tobias; Folz, Jacob Nature Methods, Vol. 18, Issue 12 https://doi.org/10.1038/s41592-021-01331-z	journal	December 2021
Mass spectrometry-based metabolomics in health and medical science: a systematic review Zhang, Xi-wu; Li, Qiu-han; Xu, Zuo-di RSC Advances, Vol. 10, Issue 6 https://doi.org/10.1039/C9RA08985C	journal	January 2020
A Highly Conserved Signal Controls Degradation of 3-Hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) Reductase in Eukaryotes Gardner, Richard G.; Hampton, Randolph Y. Journal of Biological Chemistry, Vol. 274, Issue 44 https://doi.org/10.1074/jbc.274.44.31671	journal	October 1999
Geranylgeranyl Pyrophosphate Is a Potent Regulator of HRD-dependent 3-Hydroxy-3-methylglutaryl-CoA Reductase Degradation in Yeast Garza, Renee M.; Tran, Peter N.; Hampton, Randolph Y. Journal of Biological Chemistry, Vol. 284, Issue 51 https://doi.org/10.1074/jbc.M109.023994	journal	December 2009
Multi-omics analysis unravels a segregated metabolic flux network that tunes co-utilization of sugar and aromatic carbons in Pseudomonas putida Kukurugya, Matthew A.; Mendonca, Caroll M.; Solhtalab, Mina Journal of Biological Chemistry, Vol. 294, Issue 21 https://doi.org/10.1074/jbc.RA119.007885	journal	April 2019
Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisition: A New Concept for Consistent and Accurate Proteome Analysis Gillet, Ludovic C.; Navarro, Pedro; Tate, Stephen Molecular & Cellular Proteomics, Vol. 11, Issue 6 https://doi.org/10.1074/mcp.O111.016717	journal	January 2012
Skyline: an open source document editor for creating and analyzing targeted proteomics experiments MacLean, Brendan; Tomazela, Daniela M.; Shulman, Nicholas Bioinformatics, Vol. 26, Issue 7 https://doi.org/10.1093/bioinformatics/btq054	journal	February 2010
High‐quality genome‐scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities Nogales, Juan; Mueller, Joshua; Gudmundsson, Steinn Environmental Microbiology, Vol. 22, Issue 1 https://doi.org/10.1111/1462-2920.14843	journal	November 2019
MPLEx: a Robust and Universal Protocol for Single-Sample Integrative Proteomic, Metabolomic, and Lipidomic Analyses Nakayasu, Ernesto S.; Nicora, Carrie D.; Sims, Amy C. mSystems, Vol. 1, Issue 3 https://doi.org/10.1128/mSystems.00043-16	journal	May 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Ribeiro, Marco Tulio; Singh, Sameer; Guestrin, Carlos Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16 https://doi.org/10.1145/2939672.2939778	conference	January 2016
COBRApy: COnstraints-Based Reconstruction and Analysis for Python Ebrahim, Ali; Lerman, Joshua A.; Palsson, Bernhard O. BMC Systems Biology, Vol. 7, Issue 1 https://doi.org/10.1186/1752-0509-7-74	journal	January 2013
In silico-guided engineering of Pseudomonas putida towards growth under micro-oxic conditions Kampers, Linde F. C.; van Heck, Ruben G. A.; Donati, Stefano Microbial Cell Factories, Vol. 18, Issue 1 https://doi.org/10.1186/s12934-019-1227-5	journal	October 2019
Rhodosporidium toruloides: a new platform organism for conversion of lignocellulose into terpene biofuels and bioproducts Yaegashi, Junko; Kirby, James; Ito, Masakazu Biotechnology for Biofuels, Vol. 10, Issue 1 https://doi.org/10.1186/s13068-017-0927-5	journal	October 2017
Further engineering of R. toruloides for the production of terpenes from lignocellulosic biomass Kirby, James; Geiselman, Gina M.; Yaegashi, Junko Biotechnology for Biofuels, Vol. 14, Issue 1 https://doi.org/10.1186/s13068-021-01950-w	journal	April 2021
A community-driven reconstruction of the Aspergillus niger metabolic network Brandl, Julian; Aguilar-Pontes, Maria Victoria; Schäpe, Paul Fungal Biology and Biotechnology, Vol. 5, Issue 1 https://doi.org/10.1186/s40694-018-0060-7	journal	September 2018
Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways King, Zachary A.; Dräger, Andreas; Ebrahim, Ali PLOS Computational Biology, Vol. 11, Issue 8 https://doi.org/10.1371/journal.pcbi.1004321	journal	August 2015
Feature Selection with the Boruta Package Kursa, Miron B.; Rudnicki, Witold R. Journal of Statistical Software, Vol. 36, Issue 11 https://doi.org/10.18637/jss.v036.i11	journal	January 2010
High-Throughput Large-Scale Targeted Proteomics Assays for Quantifying Pathway Proteins in Pseudomonas putida KT2440 Gao, Yuqian; Fillmore, Thomas L.; Munoz, Nathalie Frontiers in Bioengineering and Biotechnology, Vol. 8 https://doi.org/10.3389/fbioe.2020.603488	journal	December 2020
Multi-Omics Driven Metabolic Network Reconstruction and Analysis of Lignocellulosic Carbon Utilization in Rhodosporidium toruloides Kim, Joonhoon; Coradetti, Samuel T.; Kim, Young-Mo Frontiers in Bioengineering and Biotechnology, Vol. 8 https://doi.org/10.3389/fbioe.2020.612832	journal	January 2021
Integration of Proteomics and Metabolomics Into the Design, Build, Test, Learn Cycle to Improve 3-Hydroxypropionic Acid Production in Aspergillus pseudoterreus Pomraning, Kyle R.; Dai, Ziyu; Munoz, Nathalie Frontiers in Bioengineering and Biotechnology, Vol. 9 https://doi.org/10.3389/fbioe.2021.603832	journal	April 2021
Engineering the Oleaginous Yeast Rhodosporidium toruloides for Improved Resistance Against Inhibitors in Biomass Hydrolysates Lyu, Liting; Chu, Yadong; Zhang, Sufang Frontiers in Bioengineering and Biotechnology, Vol. 9 https://doi.org/10.3389/fbioe.2021.768934	journal	November 2021
Diel metabolomics analysis of a hot spring chlorophototrophic microbial mat leads to new hypotheses of community member metabolisms Kim, Young-Mo; Nowack, Shane; Olsen, Millie T. Frontiers in Microbiology, Vol. 6 https://doi.org/10.3389/fmicb.2015.00209	journal	April 2015
Machine Learning Applications for Mass Spectrometry-Based Metabolomics Liebal, Ulf W.; Phan, An N. T.; Sudhakar, Malvika Metabolites, Vol. 10, Issue 6 https://doi.org/10.3390/metabo10060243	journal	June 2020
Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics Blaženović, Ivana; Kind, Tobias; Ji, Jian Metabolites, Vol. 8, Issue 2 https://doi.org/10.3390/metabo8020031	journal	May 2018
PeakDecoder enables machine learning-1 based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements Yuqian, Gao,; Yuqian, Gao, Panorama Public https://doi.org/10.6069/6j7y-t592	dataset	January 2023
Functional genomics of lipid metabolism in the oleaginous yeast Rhodosporidium toruloides Coradetti, Samuel T.; Pinel, Dominic; Geiselman, Gina M. eLife, Vol. 7 https://doi.org/10.7554/eLife.32110	journal	March 2018

Similar Records

EMSL-Computing/PeakDecoder

Software · Wed Sep 07 00:00:00 UTC 2022 · OSTI ID:code-80693

Bilbao Pena, Aivett

Refining PeakDecoder Version 2

Technical Report · Tue Sep 30 04:00:00 UTC 2025 · OSTI ID:3001898

Bilbao, Aivett; Rahman, Ashfiqur; Harrison, Andrea L.

Agilent AgileBioFoundry CRADA (Final Report)

Technical Report · Thu Mar 09 04:00:00 UTC 2023 · OSTI ID:1959781

Burnum-Johnson, Kristin E.; Apffel, Alex; Tsalenko, Anya; +2 more

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
Data processing
HILIC
Machine learning
data-independent acquisition
ion mobility spectrometry
mass spectrometry
metabolomics
synthetic biology

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

Citation Formats

References (67)

Similar Records

Related Subjects