Statistical and Computational Methods for Comparative Proteomic Profiling Using Liquid Chromatography-Tandem Mass Spectrometry

By Jennifer Listgarten, Andrew Emili. Molecular & Cellular Proteomics, 2005

316 citations recorded by the source corpus.

The combined method of LC-MS/MS is increasingly being used to explore differences in the proteomic composition of complex biological systems. The reliability and utility of such comparative protein expression profiling studies is critically dependent on an accurate and rigorous assessment of quantitative changes in the relative abundance of the myriad of proteins typically present in a biological sample such as blood or tissue. In this review, we provide an overview of key statistical and computational issues relevant to bottom-up shotgun global proteomic analysis, with an emphasis on methods that can be applied to improve the dependability of biological inferences drawn from large proteomic datasets. Focusing on a start-to-finish approach, we address the following topics: 1) low-level data processing steps, such as formation of a data matrix, filtering, and baseline subtraction to minimize noise, 2) mid-level processing steps, such as data normalization, alignment in time, peak detection, peak quantification, peak matching, and error models, to facilitate profile comparisons; and, 3) high-level processing steps such as sample classification and biomarker discovery, and related topics such as significance testing, multiple testing, and choice of feature space. We report on approaches that have recently been developed for these steps, discussing their merits and limitations, and propose areas deserving of further research.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/7b0dfc62ed6c3c51cbaf823ef6b38658886f544b

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Citations in the corpus

Less label, more free: Approaches in label‐free quantitative mass spectrometry (2011, background): “These aspects of quantitation based on AUC necessitate a computational ‘clean up’ of the raw LC-MS data [11].”
Sorting Signals, N-Terminal Modifications and Abundance of the Chloroplast Proteome (2008, background): “For further review and discussions were refer to [19–21].”
Correlation of mRNA and protein in complex biological samples (2009, unknown): “MS data analysis itself requires several steps, such as baseline adjustments, data normalization and filtering, peak detection and quantification, the application of error models and statistics analysis [78].”
Significance Analysis of Spectral Count Data in Label-free Shotgun Proteomics*S (2008, methodology): “Despite the rich information contained in the LC-MS data, daunting computational effort needs to be spent on processing the data, including background filtering, peak detection, and alignment (8, 11).”
Analysis and validation of proteomic data generated by tandem mass spectrometry (2007, background): “The drawbacks include increased computational complexity owing to the presence of a large number of spurious features and noise, and more stringent requirements in robustness and reproducibility at various data acquisition step”
Options and considerations when selecting a quantitative proteomics strategy (2010, methodology): “The various methods differ in the requirements for sample preparation, the extent of sample fractionation and the level (protein or peptide) at which fractionation is performed, the type of mass spectrometer used and their needs in data processing tool”