Discussion on common data analysis strategies used in MS‐based proteomics

Abstract

Current proteomics technology is limited in resolving the proteome complexity of biological systems. The main issue at stake is to increase throughput and spectra quality so that spatiotemporal dimensions, population parameters and the complexity of protein modifications on a quantitative scale can be considered. MS-based proteomics and protein arrays are the main players in large-scale proteome analysis and an integration of these two methodologies is powerful but presently not sufficient for detailed quantitative and spatiotemporal proteome characterization. Improvements of instrumentation for MS-based proteomics have been achieved recently resulting in data sets of approximately one million spectra which is a large step in the right direction. The corresponding raw data range from 50 to 100 Gb and are frequently made available. Multidimensional LC-MS data sets have been demonstrated to identify and quantitate 2000-8000 proteins from whole cell extracts. The analysis of the resulting data sets requires several steps from raw data processing, to database-dependent search, statistical evaluation of the search result, quantitative algorithms and statistical analysis of quantitative data. A large number of software tools have been proposed for the above-mentioned tasks. However, it is not the aim of this review to cover all software tools, but rather discuss common data analysis strategies used by various algorithms for each of the above-mentioned steps in a non-redundant approach and to argue that there are still some areas which need improvements.

Labeling Methods in Mass Spectrometry Based Quantitative Proteomics

“…As a consequence, only a subset of peptides and proteins present in a sample can be identified. Over the past years, a series of experimental strategies for mass spectrometry based quantitative proteomics and corresponding computational methodology for the processing of quantitative data have been generated (reviewed in (Matthiesen et al, 2011;Mueller et al, 2008). Conceptually different methods to perform quantitative LC-MS experiments demand different quantification principles and available software solutions for data analysis.…”

Functional Genomics, Proteomics, Metabolomics and Bioinformatics for Systems Biology

“…Recent techniques produce data sets of approximately one million spectra, up to 100 Gb in size, where up to 8,000 proteins can be identified [133]. Pre-processing of raw spectra entails noise filtering, baseline subtraction, peak detection, and calibration and alignment of LC/MS maps.…”

“…Current methods identify approximately two thirds of tandem MS spectra. Proteins are reported on the basis of single-peptide match, or more stringently of match to protease specific peptides [133,134]. Experiments are described using MIAME-like Minimum Information About a Proteomics Experiment (MIAPE) guidelines [135].…”

“…Recent studies have shown convincing examples of quantitative proteomics efforts ran across different laboratories and using several experimental platforms. Currently, about two-third of human proteins predicted to exist have been detected with MS, hence the need to improve sensitivity, reproducibility of identification, and sensitivity and accuracy of quantification [133,134,136]. Protein-protein interactions and cell signalling cascades are mainly studied with the following approaches: yeast two-hybrid complementation, protein microarray, immunoaffinity chromatography and MS [137], and with a lower throughput by immunoprecipitation and mass spectrometry in Mammals [138,139].…”

Proteomics and Systems Biology: Current and Future Applications in the Nutritional Sciences

“…On the other hand, use of available open source tools can be problematic for researchers without programming experience. Bioinformatics in proteomics is an advancing field and computational algorithms, database repositories, and a wealth of software platforms continue to be developed (67,68). In conjunction with these Depending on the approach, labeling can be at one of several points in the experiment as indicated by the dashed arrow or, alternatively, a labelfree route can be followed.…”