Abstract
MotivationMass spectrometry is a complex technique used for large-scale protein profiling with clinical and pharmaceutical applications. While individual components in the system have been studied extensively, little work has been done to integrate various modules and evaluate them from a systems point of view.ResultsIn this work, we investigate this problem by putting together the different modules in a typical proteomics work flow, in order to capture and analyze key factors that impact the number of identified peptides and quantified proteins, protein quantification error, differential expression results, and classification performance. The proposed proteomics pipeline model can be used to optimize the work flow as well as to pinpoint critical bottlenecks worth investing time and resources into for improving performance. Using the model-based approach proposed here, one can study systematically the critical problem of proteomic biomarker discovery, by means of simulation using ground-truthed synthetic MS data.
Highlights
Mass spectrometry-based proteomics Mass spectrometry (MS) is widely used for large-scale protein profiling with applications in biomarker discovery [1], signaling pathway monitoring [2,3], drug development, and disease classification [4]
We would like to point out that we are not trying to develop a detailed physical model for mass spectrometry as is, for instance, attempted in [11], which models the mass spectra generated by matrix-assisted laser desorption/ionization (MALDI)-TOF instruments
The compound effects of instrument sensitivity and saturation demonstrate that the effectiveness of MS in quantitative analysis relies on achieving a wide linear dynamic range with a high saturation ceiling and a matching sensitivity
Summary
Mass spectrometry-based proteomics Mass spectrometry (MS) is widely used for large-scale protein profiling with applications in biomarker discovery [1], signaling pathway monitoring [2,3], drug development, and disease classification [4]. The samples are analyzed by an MS instrument and transformed into a series of mass spectra containing hundreds of thousands of intensity measurements with signal generated by thousands of proteins/ peptides (large feature dimension). This small-sample, high-dimensionality problem requires the experiment and analysis to be carefully designed and validated in order to arrive at statistically meaningful results
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