Bioinformatics Tools for Mass Spectrometry-Based High-Throughput Quantitative Proteomics Platforms

Alexey      V. Nefedov; Miroslaw      J. Gilski; Rovshan      G. Sadygov

doi:10.2174/157016411795678020

Abstract

Determining global proteome changes is important for advancing a systems biology view of cellular processes and for discovering biomarkers. Liquid chromatography, coupled to mass spectrometry, has been widely used as a proteomics technique for discovering differentially expressed proteins in biological samples. However, although a large number of high-throughput studies have identified differentially regulated proteins, only a small fraction of these results have been reproduced and independently verified. The use of different approaches to data processing and analyses is among the factors which contribute to inconsistent conclusions. This paper provides a comprehensive and critical overview of bioinformatics methods for commonly used mass spectrometry-based quantitative proteomics, employing both stable isotope labeling and label-free approaches. We evaluate the challenges associated with current quantitative proteomics techniques, placing particular emphasis on data analyses. The complexity of processing and interpreting proteomics datasets has become a central issue as sensitivity, mass resolution, mass accuracy and throughput of mass spectrometers have improved. We review a number of computer programs designed to address these challenges. We focus on approaches for signal processing, noise reduction, and methods for protein abundance estimation.

Keywords: Mass spectrometry, stable-isotope labeling, protein quantification, label-free quantification, isotope distribution, mass accuracy, mass resolution, Applied biosystems, Collision induced dissociation, Dalton, False discovery rate, Fourier transform ion cyclotron resonance, Isotope coded affinity tag