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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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Book ChapterDOI

Platforms and Pipelines for Proteomics Data Analysis and Management.

TL;DR: Since mass spectrometry was introduced as the core technology for large-scale analysis of the proteome, the speed of data acquisition, dynamic ranges of measurements, and data quality are continuously improving.
Journal ArticleDOI

Stable isotope labeling tandem mass spectrometry (SILT): integration with peptide identification and extension to data-dependent scans.

TL;DR: A software package (SILTmass) that automates protein identification and quantification by the SILT method and has the ability to analyze the kinetics of protein turnover, in addition to relative and absolute protein quantitation.
Journal ArticleDOI

iPQF: A new peptide-to-protein summarization method using peptide spectra characteristics to improve protein quantification

TL;DR: IoTQF is presented as a novel peptide-to-protein summarization method, which integrates peptide spectra characteristics as well as quantitative values for protein ratio estimation, and a feature-based weighting of peptide Spectra reliability is developed.
Journal ArticleDOI

Quantitative analysis of global phosphorylation changes with high-resolution tandem mass spectrometry and stable isotopic labeling.

TL;DR: Common strategies for mass spectrometric analysis of stable isotope labeled samples, as well as two widely applied phosphopeptide enrichment methods based on IMAC(NTA-Fe³⁺) and metal oxide (ZrO₂) are described.
Journal ArticleDOI

APP: an Automated Proteomics Pipeline for the analysis of mass spectrometry data based on multiple open access tools

TL;DR: APP automates the processing of proteomics tasks such as peptide identification, validation and quantitation from LC-MS/MS data and allows easy integration of many separate proteomics tools, and provides distributed computing nodes that are simple to set up.
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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