Journal Article10.1021/AC034633I
Automated statistical analysis of protein abundance ratios from data generated by stable-isotope dilution and tandem mass spectrometry.
TL;DR: 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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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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Citations
Comparison of Label-free Methods for Quantifying Human Proteins by Shotgun Proteomics
William M. Old,Karen Meyer-Arendt,Lauren D. Aveline-Wolf,Kevin G. Pierce,Alex M Mendoza,Joel Sevinsky,Katheryn A. Resing,Natalie G. Ahn,Natalie G. Ahn +8 more
TL;DR: Serac, software developed to evaluate the ability of each method to quantify relative changes in protein abundance is described, with overall spectral counting proved to be a more sensitive method for detecting proteins that undergo changes in abundance, whereas peak area intensity measurements yielded more accurate estimates of protein ratios.
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TL;DR: The first edition of this book as mentioned in this paper was published in 1992 and was used for the first year of a physics course at the University of Sheffield. But it was not intended to be a statistics text, nor was it intended to serve as a statistic text, but an introdution to the mathematics required for the analysis of measurements at the level of a first year laboratory course.
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