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.read more
Citations
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Liquid chromatography with tandem mass spectrometry-based proteomic discovery in aging and Alzheimer’s disease
TL;DR: The methods and examples of liquid chromatography—electrospray ionization—tandem mass spectrometry— based proteomics, with and without quantification using isotope-coded affinity tags, in the investigation of aging and Alzheimer’s disease are presented.
Journal ArticleDOI
IsoQuant: A software tool for stable isotope labeling by amino acids in cell culture-based mass spectrometry quantitation
TL;DR: A new SILAC-based proteomics quantitation software tool, named IsoQuant, is reported, which is used to process high mass accuracy mass spectrometry data and offers a convenient quantitation framework to calculate peptide/protein relative abundance ratios.
Journal ArticleDOI
Urinary protein profiling by liquid chromatography/tandem mass spectrometry: ADAM28 is overexpressed in bladder transitional cell carcinoma
Yu-Chang Tyan,Ming Hui Yang,Sharon C.-A. Chen,Shiang-Bin Jong,Wen-Cheng Chen,Yuan Han Yang,Tze Wen Chung,Pao-Chi Liao +7 more
TL;DR: Three proteins, SPINK5, ADAM28 and PTP1, were also confirmed by Western blotting and showed significant differential expression compared with the control group and may be used as a possible biomarker of bladder cancer.
Journal ArticleDOI
Peptide correlation: A means to identify high quality quantitative information in large‐scale proteomic studies
TL;DR: This study shows that peptides that accurately reflect relative protein abundances in large-scale sample sets can be selected based on the correlation to each other, and provides an intuitive and simple procedure to obtain a high quality quantitative information from proteomics data.
Journal ArticleDOI
mzDB: A File Format Using Multiple Indexing Strategies for the Efficient Analysis of Large LC-MS/MS and SWATH-MS Data Sets
David Bouyssié,David Bouyssié,Marc Dubois,Marc Dubois,Sara Nasso,Anne Gonzalez de Peredo,Anne Gonzalez de Peredo,Odile Burlet-Schiltz,Odile Burlet-Schiltz,Ruedi Aebersold,Bernard Monsarrat,Bernard Monsarrat +11 more
TL;DR: The mzDB described here can boost existing mass spectrometry data analysis pipelines, offering unprecedented performance in terms of efficiency, portability, compactness, and flexibility.
References
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Blagoy Blagoev,Irina Kratchmarova,Shao En Ong,Mogens Brøndsted Nielsen,Leonard J. Foster,Matthias Mann +5 more
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