An Automated Peak Identification/Calibration Procedure for High-Dimensional Protein Measures from Mass Spectrometers

Yutaka Yasui, Dale McLerran, Bao Ling Adam, Marcy Winget, Mark Thornquist, Ziding Feng

Research output: Contribution to journalArticlepeer-review

88 Scopus citations

Abstract

Discovery of "signature" protein profiles that distinguish disease states (eg, malignant, benign, and normal) is a key step towards translating recent advancements in proteomic technologies into clinical utilities. Protein data generated from mass spectrometers are, however, large in size and have complex features due to complexities in both biological specimens and interfering biochemical/physical processes of the measurement procedure. Making sense out of such high-dimensional complex data is challenging and necessitates the use of a systematic data analytic strategy. We propose here a data processing strategy for two major issues in the analysis of such mass-spectrometry-generated proteomic data: (1) separation of protein "signals" from background "noise" in protein intensity measurements and (2) calibration of protein mass/charge measurements across samples. We illustrate the two issues and the utility of the proposed strategy using data from a prostate cancer biomarker discovery project as an example.

Original languageEnglish (US)
Pages (from-to)242-248
Number of pages7
JournalJournal of Biomedicine and Biotechnology
Volume2003
Issue number4
DOIs
StatePublished - Oct 29 2003
Externally publishedYes

ASJC Scopus subject areas

  • Biotechnology
  • Molecular Medicine
  • Molecular Biology
  • Genetics
  • Health, Toxicology and Mutagenesis

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