apLCMS—adaptive processing of high-resolution LC/MS data

Motivation: Liquid chromatography-mass spectrometry (LC/MS) profiling is a promising approach for the quantification of metabolites from complex biological samples. Significant challenges exist in the analysis of LC/MS data, including noise reduction, feature identification/ quantification, feature...

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Published in	Bioinformatics Vol. 25; no. 15; pp. 1930 - 1936
Main Authors	Yu, Tianwei, Park, Youngja, Johnson, Jennifer M., Jones, Dean P.
Format	Journal Article
Language	English
Published	Oxford Oxford University Press 01.08.2009 Oxford Publishing Limited (England)
Subjects	Algorithms Bioinformatics Biological and medical sciences Biological samples Chromatography, Liquid - methods Computational Biology Databases, Protein Fundamental and applied biological sciences. Psychology General aspects Liquid chromatography Mass spectrometry Mass Spectrometry - methods Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Metabolites Noise reduction Original Papers Proteome - analysis Proteomics - methods Software High resolution Data
Online Access	Get full text
ISSN	1367-4803 1367-4811 1460-2059 1367-4811
DOI	10.1093/bioinformatics/btp291

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Abstract	Motivation: Liquid chromatography-mass spectrometry (LC/MS) profiling is a promising approach for the quantification of metabolites from complex biological samples. Significant challenges exist in the analysis of LC/MS data, including noise reduction, feature identification/ quantification, feature alignment and computation efficiency. Result: Here we present a set of algorithms for the processing of high-resolution LC/MS data. The major technical improvements include the adaptive tolerance level searching rather than hard cutoff or binning, the use of non-parametric methods to fine-tune intensity grouping, the use of run filter to better preserve weak signals and the model-based estimation of peak intensities for absolute quantification. The algorithms are implemented in an R package apLCMS, which can efficiently process large LC/ MS datasets. Availability: The R package apLCMS is available at www.sph.emory.edu/apLCMS. Contact: tyu8@sph.emory.edu Supplementary information: Supplementary data are available at Bioinformatics online.
AbstractList	Motivation: Liquid chromatography-mass spectrometry (LC/MS) profiling is a promising approach for the quantification of metabolites from complex biological samples. Significant challenges exist in the analysis of LC/MS data, including noise reduction, feature identification/ quantification, feature alignment and computation efficiency. Result: Here we present a set of algorithms for the processing of high-resolution LC/MS data. The major technical improvements include the adaptive tolerance level searching rather than hard cutoff or binning, the use of non-parametric methods to fine-tune intensity grouping, the use of run filter to better preserve weak signals and the model-based estimation of peak intensities for absolute quantification. The algorithms are implemented in an R package apLCMS, which can efficiently process large LC/ MS datasets. Availability: The R package apLCMS is available at www.sph.emory.edu/apLCMS. Contact: tyu8@sph.emory.edu Supplementary information: Supplementary data are available at Bioinformatics online. Motivation: Liquid chromatography-mass spectrometry (LC-MS) profiling is a promising approach for the quantification of metabolites from complex biological samples. Significant challenges exist in the analysis of LC-MS data, including noise reduction, feature identification- quantification, feature alignment and computation efficiency.Result: Here we present a set of algorithms for the processing of high-resolution LC/MS data. The major technical improvements include the adaptive tolerance level searching rather than hard cutoff or binning, the use of non-parametric methods to fine-tune intensity grouping, the use of run filter to better preserve weak signals and the model-based estimation of peak intensities for absolute quantification. The algorithms are implemented in an R package apLCMS, which can efficiently process large LC- MS datasets.Availability: The R package apLCMS is available at www.sph.emory.edu/apLCMS.Contact: tyu8atsph.emory.eduSupplementary information: Supplementary data are available at Bioinformatics online. Motivation: Liquid chromatography-mass spectrometry (LC/MS) profiling is a promising approach for the quantification of metabolites from complex biological samples. Significant challenges exist in the analysis of LC/MS data, including noise reduction, feature identification/ quantification, feature alignment and computation efficiency. Result: Here we present a set of algorithms for the processing of high-resolution LC/MS data. The major technical improvements include the adaptive tolerance level searching rather than hard cutoff or binning, the use of non-parametric methods to fine-tune intensity grouping, the use of run filter to better preserve weak signals and the model-based estimation of peak intensities for absolute quantification. The algorithms are implemented in an R package apLCMS, which can efficiently process large LC/ MS datasets. Availability: The R package apLCMS is available at www.sph.emory.edu/apLCMS. Contact: tyu8@sph.emory.edu Supplementary information: Supplementary data are available at Bioinformatics online. Motivation: Liquid chromatography-mass spectrometry (LC MS) profiling is a promising approach for the quantification of metabolites from complex biological samples. Significant challenges exist in the analysis of LC MS data, including noise reduction, feature identification quantification, feature alignment and computation efficiency. Result: Here we present a set of algorithms for the processing of high-resolution LC/MS data. The major technical improvements include the adaptive tolerance level searching rather than hard cutoff or binning, the use of non-parametric methods to fine-tune intensity grouping, the use of run filter to better preserve weak signals and the model-based estimation of peak intensities for absolute quantification. The algorithms are implemented in an R package apLCMS, which can efficiently process large LC MS datasets. Availability: The R package apLCMS is available at www.sph.emory.edu/apLCMS. Contact: tyu8@sph.emory.edu Supplementary information: Supplementary data are available at Bioinformatics online. Liquid chromatography-mass spectrometry (LC/MS) profiling is a promising approach for the quantification of metabolites from complex biological samples. Significant challenges exist in the analysis of LC/MS data, including noise reduction, feature identification/ quantification, feature alignment and computation efficiency.MOTIVATIONLiquid chromatography-mass spectrometry (LC/MS) profiling is a promising approach for the quantification of metabolites from complex biological samples. Significant challenges exist in the analysis of LC/MS data, including noise reduction, feature identification/ quantification, feature alignment and computation efficiency.Here we present a set of algorithms for the processing of high-resolution LC/MS data. The major technical improvements include the adaptive tolerance level searching rather than hard cutoff or binning, the use of non-parametric methods to fine-tune intensity grouping, the use of run filter to better preserve weak signals and the model-based estimation of peak intensities for absolute quantification. The algorithms are implemented in an R package apLCMS, which can efficiently process large LC/ MS datasets.RESULTHere we present a set of algorithms for the processing of high-resolution LC/MS data. The major technical improvements include the adaptive tolerance level searching rather than hard cutoff or binning, the use of non-parametric methods to fine-tune intensity grouping, the use of run filter to better preserve weak signals and the model-based estimation of peak intensities for absolute quantification. The algorithms are implemented in an R package apLCMS, which can efficiently process large LC/ MS datasets.The R package apLCMS is available at www.sph.emory.edu/apLCMS.AVAILABILITYThe R package apLCMS is available at www.sph.emory.edu/apLCMS.Supplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online. Liquid chromatography-mass spectrometry (LC/MS) profiling is a promising approach for the quantification of metabolites from complex biological samples. Significant challenges exist in the analysis of LC/MS data, including noise reduction, feature identification/ quantification, feature alignment and computation efficiency. Here we present a set of algorithms for the processing of high-resolution LC/MS data. The major technical improvements include the adaptive tolerance level searching rather than hard cutoff or binning, the use of non-parametric methods to fine-tune intensity grouping, the use of run filter to better preserve weak signals and the model-based estimation of peak intensities for absolute quantification. The algorithms are implemented in an R package apLCMS, which can efficiently process large LC/ MS datasets. The R package apLCMS is available at www.sph.emory.edu/apLCMS. Supplementary data are available at Bioinformatics online.
Author	Johnson, Jennifer M. Jones, Dean P. Park, Youngja Yu, Tianwei
AuthorAffiliation	1 Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta and 2 Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA
AuthorAffiliation_xml	– name: 1 Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta and 2 Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA
Author_xml	– sequence: 1 givenname: Tianwei surname: Yu fullname: Yu, Tianwei organization: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta and Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA – sequence: 2 givenname: Youngja surname: Park fullname: Park, Youngja organization: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta and Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA – sequence: 3 givenname: Jennifer M. surname: Johnson fullname: Johnson, Jennifer M. organization: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta and Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA – sequence: 4 givenname: Dean P. surname: Jones fullname: Jones, Dean P. organization: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta and Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA
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Notes	To whom correspondence should be addressed. ArticleID:btp291 ark:/67375/HXZ-8D921RX7-D istex:CAE311883B580B0F5F49D1C03CF4B17C74D70678 Associate Editor: John Quackenbush ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23
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Snippet	Motivation: Liquid chromatography-mass spectrometry (LC/MS) profiling is a promising approach for the quantification of metabolites from complex biological... Motivation: Liquid chromatography-mass spectrometry (LC MS) profiling is a promising approach for the quantification of metabolites from complex biological... Liquid chromatography-mass spectrometry (LC/MS) profiling is a promising approach for the quantification of metabolites from complex biological samples.... Motivation: Liquid chromatography-mass spectrometry (LC-MS) profiling is a promising approach for the quantification of metabolites from complex biological...
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SubjectTerms	Algorithms Bioinformatics Biological and medical sciences Biological samples Chromatography, Liquid - methods Computational Biology Databases, Protein Fundamental and applied biological sciences. Psychology General aspects Liquid chromatography Mass spectrometry Mass Spectrometry - methods Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Metabolites Noise reduction Original Papers Proteome - analysis Proteomics - methods Software
Title	apLCMS—adaptive processing of high-resolution LC/MS data
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