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Journal ArticleDOI

Automated statistical analysis of protein abundance ratios from data generated by stable-isotope dilution and tandem mass spectrometry.

TLDR
The utility of the ASAPRatio program was clearly demonstrated by its speed and the accuracy of the generated protein abundance ratios and by its capability to identify specific core components of the RNA polymerase II transcription complex within a high background of copurifying proteins.
Abstract
We describe an algorithm for the automated statistical analysis of protein abundance ratios (ASAPRatio) of proteins contained in two samples. Proteins are labeled with distinct stable-isotope tags and fragmented, and the tagged peptide fragments are separated by liquid chromatography (LC) and analyzed by electrospray ionization (ESI) tandem mass spectrometry (MS/MS). The algorithm utilizes the signals recorded for the different isotopic forms of peptides of identical sequence and numerical and statistical methods, such as Savitzky-Golay smoothing filters, statistics for weighted samples, and Dixon's test for outliers, to evaluate protein abundance ratios and their associated errors. The algorithm also provides a statistical assessment to distinguish proteins of significant abundance changes from a population of proteins of unchanged abundance. To evaluate its performance, two sets of LC-ESI-MS/MS data were analyzed by the ASAPRatio algorithm without human intervention, and the data were related to the expected and manually validated values. The utility of the ASAPRatio program was clearly demonstrated by its speed and the accuracy of the generated protein abundance ratios and by its capability to identify specific core components of the RNA polymerase II transcription complex within a high background of copurifying proteins.

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Journal ArticleDOI

Chemistry meets proteomics: the use of chemical tagging reactions for MS-based proteomics.

TL;DR: This review of proteomics methodologies focuses on approaches that allow the introduction of affinity tags for the enrichment of subclasses of peptides or proteins and strategies for in vitro stable isotope labeling for quantification purposes, among others.
Journal ArticleDOI

Current chemical tagging strategies for proteome analysis by mass spectrometry.

TL;DR: This review summarizes the different application fields for tagging strategies for today's MS-based proteome analysis and highlights the advantages and drawbacks of the numerous strategies that have appeared in the literature in the last years.
Journal ArticleDOI

Identification of carbonylated proteins from enriched rat skeletal muscle mitochondria using affinity chromatography-stable isotope labeling and tandem mass spectrometry

TL;DR: The results demonstrate the utility of PQD operation on the LTQ instrument for quantitative analysis of iTRAQ reagent‐labeled peptide mixtures, as well as the quantitative reproducibility of the avidin‐affinity enrichment method.
Journal ArticleDOI

CPFP: a central proteomics facilities pipeline

TL;DR: The central proteomics facilities pipeline provides identification, validation, and quantitation of peptides and proteins from LC-MS/MS datasets through an easy to use web interface, reducing the data analysis load on staff, and allowing facility clients to easily access and work with their data.
Journal ArticleDOI

Experimental and computational approaches to quantitative proteomics: status quo and outlook.

TL;DR: Special emphasis is placed on quantification techniques: gel-based, and label-free techniques are briefly discussed whereas stable-isotope coding and internal peptide standards are extensively reviewed.
References
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Journal ArticleDOI

Probability-based protein identification by searching sequence databases using mass spectrometry data.

TL;DR: A new computer program, Mascot, is presented, which integrates all three types of search for protein identification by searching a sequence database using mass spectrometry data, and the scoring algorithm is probability based.
Journal ArticleDOI

A statistical model for identifying proteins by tandem mass spectrometry.

TL;DR: A statistical model is presented for computing probabilities that proteins are present in a sample on the basis of peptides assigned to tandem mass (MS/MS) spectra acquired from a proteolytic digest of the sample, and it is shown to produce probabilities that are accurate and have high power to discriminate correct from incorrect protein identifications.
Journal ArticleDOI

Integrated genomic and proteomic analyses of a systematically perturbed metabolic network.

TL;DR: An integrated approach to build, test, and refine a model of a cellular pathway, in which perturbations to critical pathway components are analyzed using DNA microarrays, quantitative proteomics, and databases of known physical interactions, suggests hypotheses about the regulation of galactose utilization and physical interactions between this and a variety of other metabolic pathways.
Journal ArticleDOI

Linking genome and proteome by mass spectrometry: Large-scale identification of yeast proteins from two dimensional gels

TL;DR: This study establishes that mass spectrometry provides the required throughput, the certainty of identification, and the general applicability to serve as the method of choice to connect genome and proteome.
Journal ArticleDOI

A proteomics strategy to elucidate functional protein-protein interactions applied to EGF signaling

TL;DR: Stable isotopic amino acids in cell culture is employed to differentially label proteins in EGF-stimulated versus unstimulated cells and SILAC combined with modification-based affinity purification is a useful approach to detect specific and functional protein-protein interactions.
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