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RAMClust: a novel feature clustering method enables spectral-matching-based annotation for metabolomics data.

Metabolomic data are frequently acquired using chromatographically coupled mass spectrometry (MS) platforms. For such datasets, the first step in data analysis relies on feature detection, where a feature is defined by a mass and retention time. While a feature typically is derived from a single com... Full description

Journal Title: Analytical chemistry July 15, 2014, Vol.86(14), pp.6812-6817
Main Author: Broeckling, C D
Other Authors: Afsar, F A , Neumann, S , Ben-Hur, A , Prenni, J E
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: E-ISSN: 1520-6882 ; DOI: 10.1021/ac501530d
Link: http://search.proquest.com/docview/1545418558/?pq-origsite=primo
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title: RAMClust: a novel feature clustering method enables spectral-matching-based annotation for metabolomics data.
format: Article
creator:
  • Broeckling, C D
  • Afsar, F A
  • Neumann, S
  • Ben-Hur, A
  • Prenni, J E
subjects:
  • Animals–Metabolism
  • Cerebrospinal Fluid–Methods
  • Cluster Analysis–Methods
  • Databases, Factual–Methods
  • Horses–Methods
  • Mass Spectrometry–Methods
  • Metabolomics–Methods
  • Signal Processing, Computer-Assisted–Methods
  • Software–Methods
  • Tandem Mass Spectrometry–Methods
ispartof: Analytical chemistry, July 15, 2014, Vol.86(14), pp.6812-6817
description: Metabolomic data are frequently acquired using chromatographically coupled mass spectrometry (MS) platforms. For such datasets, the first step in data analysis relies on feature detection, where a feature is defined by a mass and retention time. While a feature typically is derived from a single compound, a spectrum of mass signals is more a more-accurate representation of the mass spectrometric signal for a given metabolite. Here, we report a novel feature grouping method that operates in an unsupervised manner to group signals from MS data into spectra without relying on predictability of the in-source phenomenon. We additionally address a fundamental bottleneck in metabolomics, annotation of MS level signals, by incorporating indiscriminant MS/MS (idMS/MS) data implicitly: feature detection is performed on both MS and idMS/MS data, and feature-feature relationships are determined simultaneously from the MS and idMS/MS data. This approach facilitates identification of metabolites using in-source MS and/or idMS/MS spectra from a single experiment, reduces quantitative analytical variation compared to single-feature measures, and decreases false positive annotations of unpredictable phenomenon as novel compounds. This tool is released as a freely available R package, called RAMClustR, and is sufficiently versatile to group features from any chromatographic-spectrometric platform or feature-finding software.
language: eng
source:
identifier: E-ISSN: 1520-6882 ; DOI: 10.1021/ac501530d
fulltext: fulltext
issn:
  • 15206882
  • 1520-6882
url: Link


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titleRAMClust: a novel feature clustering method enables spectral-matching-based annotation for metabolomics data.
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subjectAnimals–Metabolism ; Cerebrospinal Fluid–Methods ; Cluster Analysis–Methods ; Databases, Factual–Methods ; Horses–Methods ; Mass Spectrometry–Methods ; Metabolomics–Methods ; Signal Processing, Computer-Assisted–Methods ; Software–Methods ; Tandem Mass Spectrometry–Methods
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descriptionMetabolomic data are frequently acquired using chromatographically coupled mass spectrometry (MS) platforms. For such datasets, the first step in data analysis relies on feature detection, where a feature is defined by a mass and retention time. While a feature typically is derived from a single compound, a spectrum of mass signals is more a more-accurate representation of the mass spectrometric signal for a given metabolite. Here, we report a novel feature grouping method that operates in an unsupervised manner to group signals from MS data into spectra without relying on predictability of the in-source phenomenon. We additionally address a fundamental bottleneck in metabolomics, annotation of MS level signals, by incorporating indiscriminant MS/MS (idMS/MS) data implicitly: feature detection is performed on both MS and idMS/MS data, and feature-feature relationships are determined simultaneously from the MS and idMS/MS data. This approach facilitates identification of metabolites using in-source MS and/or idMS/MS spectra from a single experiment, reduces quantitative analytical variation compared to single-feature measures, and decreases false positive annotations of unpredictable phenomenon as novel compounds. This tool is released as a freely available R package, called RAMClustR, and is sufficiently versatile to group features from any chromatographic-spectrometric platform or feature-finding software.
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