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Title: Improved LC-MS/MS Spectral Counting Statistics by Recovering Low Scoring Spectra Matched to Confidently Identified Peptide Sequences

Abstract

Spectral counting has become a popular semi-quantitative method for LC-MS/MS based proteome quantification; however, this methodology is often not reliable when proteins are identified by a small number of spectra. Here we present a simple strategy to improve spectral counting based quantification for low abundance proteins by recovering low quality or low scoring spectra for confidently identified peptides. In this approach, stringent data filtering criteria were initially applied to achieve confident peptide identifications with low false discovery rate (e.g., <1%) after LC-MS/MS analysis and database search by SEQUEST. Then, all low scoring MS/MS spectra that match to this set of confidently identified peptides were recovered, leading to more than 20% increase of total identified spectra. The validity of these recovered spectra was assessed by the parent ion mass measurement error distribution, retention time distribution, and by comparing the individual low score and high score spectra that correspond to the same peptides. The results support that the recovered low scoring spectra have similar confidence levels in peptide identifications as the spectra passing the initial stringent filter. The application of this strategy of recovering low scoring spectra significantly improved the spectral count quantification statistics for low abundance proteins, as illustrated in themore » identification of mouse brain region specific proteins.« less

Authors:
; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (US), Environmental Molecular Sciences Laboratory (EMSL)
Sponsoring Org.:
USDOE
OSTI Identifier:
1000626
Report Number(s):
PNNL-SA-74147
36197; 24698; 400412000; TRN: US201101%%424
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Journal of Proteome Research, 9(11):5698-5704
Additional Journal Information:
Journal Volume: 9; Journal Issue: 11
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ABUNDANCE; BRAIN; DISTRIBUTION; PEPTIDES; PROTEINS; RETENTION; SPECTRA; STATISTICS; Environmental Molecular Sciences Laboratory

Citation Formats

Zhou, Jianying, Schepmoes, Athena A, Zhang, Xu, Moore, Ronald J, Monroe, Matthew E, Lee, Jung Hwa, Camp, David G, Smith, Richard D, and Qian, Weijun. Improved LC-MS/MS Spectral Counting Statistics by Recovering Low Scoring Spectra Matched to Confidently Identified Peptide Sequences. United States: N. p., 2010. Web. doi:10.1021/pr100508p.
Zhou, Jianying, Schepmoes, Athena A, Zhang, Xu, Moore, Ronald J, Monroe, Matthew E, Lee, Jung Hwa, Camp, David G, Smith, Richard D, & Qian, Weijun. Improved LC-MS/MS Spectral Counting Statistics by Recovering Low Scoring Spectra Matched to Confidently Identified Peptide Sequences. United States. https://doi.org/10.1021/pr100508p
Zhou, Jianying, Schepmoes, Athena A, Zhang, Xu, Moore, Ronald J, Monroe, Matthew E, Lee, Jung Hwa, Camp, David G, Smith, Richard D, and Qian, Weijun. Thu . "Improved LC-MS/MS Spectral Counting Statistics by Recovering Low Scoring Spectra Matched to Confidently Identified Peptide Sequences". United States. https://doi.org/10.1021/pr100508p.
@article{osti_1000626,
title = {Improved LC-MS/MS Spectral Counting Statistics by Recovering Low Scoring Spectra Matched to Confidently Identified Peptide Sequences},
author = {Zhou, Jianying and Schepmoes, Athena A and Zhang, Xu and Moore, Ronald J and Monroe, Matthew E and Lee, Jung Hwa and Camp, David G and Smith, Richard D and Qian, Weijun},
abstractNote = {Spectral counting has become a popular semi-quantitative method for LC-MS/MS based proteome quantification; however, this methodology is often not reliable when proteins are identified by a small number of spectra. Here we present a simple strategy to improve spectral counting based quantification for low abundance proteins by recovering low quality or low scoring spectra for confidently identified peptides. In this approach, stringent data filtering criteria were initially applied to achieve confident peptide identifications with low false discovery rate (e.g., <1%) after LC-MS/MS analysis and database search by SEQUEST. Then, all low scoring MS/MS spectra that match to this set of confidently identified peptides were recovered, leading to more than 20% increase of total identified spectra. The validity of these recovered spectra was assessed by the parent ion mass measurement error distribution, retention time distribution, and by comparing the individual low score and high score spectra that correspond to the same peptides. The results support that the recovered low scoring spectra have similar confidence levels in peptide identifications as the spectra passing the initial stringent filter. The application of this strategy of recovering low scoring spectra significantly improved the spectral count quantification statistics for low abundance proteins, as illustrated in the identification of mouse brain region specific proteins.},
doi = {10.1021/pr100508p},
url = {https://www.osti.gov/biblio/1000626}, journal = {Journal of Proteome Research, 9(11):5698-5704},
number = 11,
volume = 9,
place = {United States},
year = {2010},
month = {9}
}