Evaluating Reproducibility and Similarity of Mass and Intensity Data in Complex Spectra-Applications to Tubulin

被引:0
|
作者
Olson, Matthew T. [1 ]
Blank, Paul S. [1 ]
Sackett, Dan L. [2 ]
Yergey, Alfred L. [1 ]
机构
[1] Laboratory of Cellular and Molecular Biophysics, National Institute of Child Health and Human Development, NIH, Bethesda, MD, United States
[2] Laboratory of Integrative and Medical Biophysics, National Institute of Child Health and Human Development, NIH, Bethesda, MD, United States
来源
Journal of the American Society for Mass Spectrometry | 2008年 / 19卷 / 03期
关键词
We present a data processing approach based on the spectral dot product for evaluating spectral similarity and reproducibility. The method introduces 95% confidence intervals on the spectral dot product to evaluate the strength of spectral correlation; it is the only calculation described to date that accounts for both the non-normal sampling distribution of the dot product and the number of peaks the spectra have in common. These measures of spectral similarity allow for the recursive generation of a consensus spectrum; which incorporates the most consistent features from statistically similar replicate spectra. Taking the spectral dot product and 95% confidence intervals between consensus spectra from different samples yields the similarity between these samples. Applying the data analysis scheme to replicates of brain tubulin CNBr peptides enables a robust comparison of tubulin isotype expression and post-translational modification patterns in rat and cow brains. © 2008 American Society for Mass Spectrometry;
D O I
暂无
中图分类号
学科分类号
摘要
Journal article (JA)
引用
收藏
页码:367 / 374
相关论文
共 17 条
  • [1] Evaluating reproducibility and similarity of mass and intensity data in complex spectra - Applications to tubulin
    Olson, Matthew T.
    Blank, Paul S.
    Sackett, Dan L.
    Yergey, Alfred L.
    JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY, 2008, 19 (03) : 367 - 374
  • [2] Metrics for evaluating the stability and reproducibility of mass spectra
    E. S. Zhvansky
    S. I. Pekov
    A. A. Sorokin
    V. A. Shurkhay
    V. A. Eliferov
    A. A. Potapov
    E. N. Nikolaev
    I. A. Popov
    Scientific Reports, 9
  • [3] Metrics for evaluating the stability and reproducibility of mass spectra
    Zhvansky, E. S.
    Pekov, S. I.
    Sorokin, A. A.
    Shurkhay, V. A.
    Eliferov, V. A.
    Potapov, A. A.
    Nikolaev, E. N.
    Popov, I. A.
    SCIENTIFIC REPORTS, 2019, 9 (1)
  • [4] A Method for Monitoring and Controlling Reproducibility of Intensity Data in Complex Electrospray Mass Spectra: A Thermometer Ion-based Strategy
    Lecchi, Paolo
    Zhao, Jinghua
    Wiggins, Wesley S.
    Chen, Tzong-Hao
    Yip, Ping F.
    Mansfield, Brian C.
    Peltier, John M.
    JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY, 2009, 20 (03) : 398 - 410
  • [5] Mass spectral similarity for untargeted metabolomics data analysis of complex mixtures
    Garg, Neha
    Kapono, Clifford A.
    Lim, Yan Wei
    Koyama, Nobuhiro
    Vermeij, Mark J. A.
    Conrad, Douglas
    Rohwer, Forest
    Dorrestein, Pieter C.
    INTERNATIONAL JOURNAL OF MASS SPECTROMETRY, 2015, 377 : 719 - 727
  • [6] Establishing a Measure of Reproducibility of Ultrahigh-Resolution Mass Spectra for Complex Mixtures of Natural Organic Matter
    Sleighter, Rachel L.
    Chen, Hongmei
    Wozniak, Andrew S.
    Willoughby, Amanda S.
    Caricasole, Paolo
    Hatcher, Patrick G.
    ANALYTICAL CHEMISTRY, 2012, 84 (21) : 9184 - 9191
  • [7] Automated intensity descent algorithm for interpretation of complex high-resolution mass spectra
    Chen, Li
    Sze, Siu Kwan
    Yang, He
    ANALYTICAL CHEMISTRY, 2006, 78 (14) : 5006 - 5018
  • [8] A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data
    Zhou, Cong
    Bowler, Lucas D.
    Feng, Jianfeng
    BMC BIOINFORMATICS, 2008, 9 (1)
  • [9] A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data
    Cong Zhou
    Lucas D Bowler
    Jianfeng Feng
    BMC Bioinformatics, 9
  • [10] Quantitative analysis of multivariate data using artificial neural networks: A tutorial review and applications to the deconvolution of pyrolysis mass spectra
    Goodacre, R
    Neal, MJ
    Kell, DB
    ZENTRALBLATT FUR BAKTERIOLOGIE-INTERNATIONAL JOURNAL OF MEDICAL MICROBIOLOGY VIROLOGY PARASITOLOGY AND INFECTIOUS DISEASES, 1996, 284 (04): : 516 - 539