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Extending a Tandem Mass Spectral Library to Include MS 2 Spectra of Fragment Ions Produced In-Source and MS n Spectra

Tandem mass spectral library searching is finding increased use as an effective means of determining chemical identity in mass spectrometry-based omics studies. We previously reported on constructing a tandem mass spectral library that includes spectra for multiple precursor ions for each analyte. H... Full description

Journal Title: Journal of The American Society for Mass Spectrometry 2017, Vol.28(11), pp.2280-2287
Main Author: Yang, Xiaoyu
Other Authors: Neta, Pedatsur , Stein, Stephen
Format: Electronic Article Electronic Article
Language: English
Subjects:
MS
ID: ISSN: 1044-0305 ; E-ISSN: 1879-1123 ; DOI: 10.1007/s13361-017-1748-2
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recordid: springer_jour10.1007/s13361-017-1748-2
title: Extending a Tandem Mass Spectral Library to Include MS 2 Spectra of Fragment Ions Produced In-Source and MS n Spectra
format: Article
creator:
  • Yang, Xiaoyu
  • Neta, Pedatsur
  • Stein, Stephen
subjects:
  • Tandem mass spectral library
  • In-source fragmentation
  • MS
  • Density-based clustering algorithm
  • Top-down hierarchical divisive clustering algorithm
  • Metabolite identification
ispartof: Journal of The American Society for Mass Spectrometry, 2017, Vol.28(11), pp.2280-2287
description: Tandem mass spectral library searching is finding increased use as an effective means of determining chemical identity in mass spectrometry-based omics studies. We previously reported on constructing a tandem mass spectral library that includes spectra for multiple precursor ions for each analyte. Here we report our method for expanding this library to include MS 2 spectra of fragment ions generated during the ionization process (in-source fragment ions) as well as MS 3 and MS 4 spectra. These can assist the chemical identification process. A simple density-based clustering algorithm was used to cluster all significant precursor ions from MS 1 scans for an analyte acquired during an infusion experiment. The MS 2 spectra associated with these precursor ions were grouped into the same precursor clusters. Subsequently, a new top-down hierarchical divisive clustering algorithm was developed for clustering the spectra from fragmentation of ions in each precursor cluster, including the MS 2 spectra of the original precursors and of the in-source fragments as well as the MS n spectra. This algorithm starts with all the spectra of one precursor in one cluster and then separates them into sub-clusters of similar spectra based on the fragment patterns. Herein, we describe the algorithms and spectral evaluation methods for extending the library. The new library features were demonstrated by searching the high resolution spectra of E. coli extracts against the extended library, allowing identification of compounds and their in-source fragment ions in a manner that was not possible before. Graphical Abstract ᅟ
language: eng
source:
identifier: ISSN: 1044-0305 ; E-ISSN: 1879-1123 ; DOI: 10.1007/s13361-017-1748-2
fulltext: fulltext_linktorsrc
issn:
  • 1879-1123
  • 18791123
  • 1044-0305
  • 10440305
url: Link


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titleExtending a Tandem Mass Spectral Library to Include MS 2 Spectra of Fragment Ions Produced In-Source and MS n Spectra
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subjectTandem mass spectral library ; In-source fragmentation ; MS ; Density-based clustering algorithm ; Top-down hierarchical divisive clustering algorithm ; Metabolite identification
descriptionTandem mass spectral library searching is finding increased use as an effective means of determining chemical identity in mass spectrometry-based omics studies. We previously reported on constructing a tandem mass spectral library that includes spectra for multiple precursor ions for each analyte. Here we report our method for expanding this library to include MS 2 spectra of fragment ions generated during the ionization process (in-source fragment ions) as well as MS 3 and MS 4 spectra. These can assist the chemical identification process. A simple density-based clustering algorithm was used to cluster all significant precursor ions from MS 1 scans for an analyte acquired during an infusion experiment. The MS 2 spectra associated with these precursor ions were grouped into the same precursor clusters. Subsequently, a new top-down hierarchical divisive clustering algorithm was developed for clustering the spectra from fragmentation of ions in each precursor cluster, including the MS 2 spectra of the original precursors and of the in-source fragments as well as the MS n spectra. This algorithm starts with all the spectra of one precursor in one cluster and then separates them into sub-clusters of similar spectra based on the fragment patterns. Herein, we describe the algorithms and spectral evaluation methods for extending the library. The new library features were demonstrated by searching the high resolution spectra of E. coli extracts against the extended library, allowing identification of compounds and their in-source fragment ions in a manner that was not possible before. Graphical Abstract ᅟ
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abstractTandem mass spectral library searching is finding increased use as an effective means of determining chemical identity in mass spectrometry-based omics studies. We previously reported on constructing a tandem mass spectral library that includes spectra for multiple precursor ions for each analyte. Here we report our method for expanding this library to include MS 2 spectra of fragment ions generated during the ionization process (in-source fragment ions) as well as MS 3 and MS 4 spectra. These can assist the chemical identification process. A simple density-based clustering algorithm was used to cluster all significant precursor ions from MS 1 scans for an analyte acquired during an infusion experiment. The MS 2 spectra associated with these precursor ions were grouped into the same precursor clusters. Subsequently, a new top-down hierarchical divisive clustering algorithm was developed for clustering the spectra from fragmentation of ions in each precursor cluster, including the MS 2 spectra of the original precursors and of the in-source fragments as well as the MS n spectra. This algorithm starts with all the spectra of one precursor in one cluster and then separates them into sub-clusters of similar spectra based on the fragment patterns. Herein, we describe the algorithms and spectral evaluation methods for extending the library. The new library features were demonstrated by searching the high resolution spectra of E. coli extracts against the extended library, allowing identification of compounds and their in-source fragment ions in a manner that was not possible before. Graphical Abstract ᅟ
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