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Book Chapter: Metabolomics Data Preprocessing Using ADAP and MZmine 2

TitleMetabolomics Data Preprocessing Using ADAP and MZmine 2
Authors
KeywordsADAP
Alignment
Data preprocessing
GC–MS
LC–MS
Metabolomics
MZmine 2
Peak picking
Spectral deconvolution
Visualization
Issue Date2020
Citation
Methods in Molecular Biology, 2020, v. 2104, p. 25-48 How to Cite?
AbstractThe informatics pipeline for making sense of untargeted LC–MS or GC–MS data starts with preprocessing the raw data. Results from data preprocessing undergo statistical analysis and subsequently mapped to metabolic pathways for placing untargeted metabolomics data in the biological context. ADAP is a suite of computational algorithms that has been developed specifically for preprocessing LC–MS and GC–MS data. It consists of two separate computational workflows that extract compound-relevant information from raw LC–MS and GC–MS data, respectively. Computational steps include construction of extracted ion chromatograms, detection of chromatographic peaks, spectral deconvolution, and alignment. The two workflows have been incorporated into the cross-platform and graphical MZmine 2 framework and ADAP-specific graphical user interfaces have been developed for using ADAP with ease. This chapter summarizes the algorithmic principles underlying key steps in the two workflows and illustrates how to apply ADAP to preprocess LC–MS and GC–MS data.
Persistent Identifierhttp://hdl.handle.net/10722/342741
ISSN
2023 SCImago Journal Rankings: 0.399

 

DC FieldValueLanguage
dc.contributor.authorDu, Xiuxia-
dc.contributor.authorSmirnov, Aleksandr-
dc.contributor.authorPluskal, Tomáš-
dc.contributor.authorJia, Wei-
dc.contributor.authorSumner, Susan-
dc.date.accessioned2024-04-17T07:05:55Z-
dc.date.available2024-04-17T07:05:55Z-
dc.date.issued2020-
dc.identifier.citationMethods in Molecular Biology, 2020, v. 2104, p. 25-48-
dc.identifier.issn1064-3745-
dc.identifier.urihttp://hdl.handle.net/10722/342741-
dc.description.abstractThe informatics pipeline for making sense of untargeted LC–MS or GC–MS data starts with preprocessing the raw data. Results from data preprocessing undergo statistical analysis and subsequently mapped to metabolic pathways for placing untargeted metabolomics data in the biological context. ADAP is a suite of computational algorithms that has been developed specifically for preprocessing LC–MS and GC–MS data. It consists of two separate computational workflows that extract compound-relevant information from raw LC–MS and GC–MS data, respectively. Computational steps include construction of extracted ion chromatograms, detection of chromatographic peaks, spectral deconvolution, and alignment. The two workflows have been incorporated into the cross-platform and graphical MZmine 2 framework and ADAP-specific graphical user interfaces have been developed for using ADAP with ease. This chapter summarizes the algorithmic principles underlying key steps in the two workflows and illustrates how to apply ADAP to preprocess LC–MS and GC–MS data.-
dc.languageeng-
dc.relation.ispartofMethods in Molecular Biology-
dc.subjectADAP-
dc.subjectAlignment-
dc.subjectData preprocessing-
dc.subjectGC–MS-
dc.subjectLC–MS-
dc.subjectMetabolomics-
dc.subjectMZmine 2-
dc.subjectPeak picking-
dc.subjectSpectral deconvolution-
dc.subjectVisualization-
dc.titleMetabolomics Data Preprocessing Using ADAP and MZmine 2-
dc.typeBook_Chapter-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-1-0716-0239-3_3-
dc.identifier.pmid31953811-
dc.identifier.scopuseid_2-s2.0-85078055402-
dc.identifier.volume2104-
dc.identifier.spage25-
dc.identifier.epage48-
dc.identifier.eissn1940-6029-

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