Utility of mass spectrometry for proteome analysis: part II. Ion-activation methods, statistics, bioinformatics and annotation

Abstract

This is the second article in a series, intended as a tutorial to provide the interested reader with an overview of the concepts not covered in part I, such as: the principles of ion-activation methods, the ability of mass-spectrometric methods to interface with various proteomic strategies, analysis techniques, bioinformatics and data interpretation and annotation. Although these are different topics, it is important that a reader has a basic and collective understanding of all of them for an overall appreciation of how to carry out and analyze a proteomic experiment. Different ion-activation methods for MS/MS, such as collision-induced dissociation (including postsource decay) and surface-induced dissociation, electron capture and electron-transfer dissociation, infrared multiphoton and blackbody infrared radiative dissociation have been discussed since they are used in proteomic research. The high dimensionality of data generated from proteomic studies requires an understanding of the underlying analytical procedures used to obtain these data, as well as the development of improved bioinformatics tools and data-mining approaches for efficient and accurate statistical analyses of biological samples from healthy and diseased individuals, in addition to determining the utility of the interpreted data. Currently available strategies for the analysis of the proteome by mass spectrometry, such as those employed for the analysis of substantially purified proteins and complex peptide mixtures, as well as hypothesis-driven strategies, have been elaborated upon. Processing steps prior to the analysis of mass spectrometry data, statistics and the several informatics steps currently used for the analysis of shotgun proteomic experiments, as well as proteomics ontology, are also discussed.

Proteomics in Transplantation

“…An excellent in-depth tutorial of the technical principles of MS instrumentation and concepts for proteomic analysis has been published by Ahmed [16,17]. Here, we provide a summary of his extensive review aimed as a guide for the inexperienced Figure 2 Steps of a proteomic experiment.…”

“…clinician/reader to facilitate critical interpretation of proteomic-driven results [16][17][18][19].…”

Urinary proteomics as a novel tool for biomarker discovery in kidney diseases

“…While the supervised learning approaches, such as classification and regression trees, Bayesian classification, neural networks, genetic algorithms, and support vector machine, are analogous to classification, they need to divide samples into a validation set and a training set (if the training set is too large, it can be subdivided into a training set and a test set). Feature selection and model-building are performed on the training set and their performances are assessed in the test set in order to obtain better accuracy (Ahmed, 2009). In addition, cross-validation techniques such as k-fold crossvalidation and leave-one-out cross-validation can be used to maximize the training set size and avoid falsely low error estimates (Dakna et al, 2009).…”

The role of capillary electrophoresis–mass spectrometry to proteome analysis and biomarker discovery

“…In addition, to obtain statistically significant data, the increasing number of analyzed components requires increasing the number of analyzed samples and consequently the need for greater computing power. Therefore, it is important to find a balance between the desire for maximal data for analysis, and the limitation of needed effort and analysis time [88].…”