Literature DB >> 19892804

Methods for combining peptide intensities to estimate relative protein abundance.

Brian Carrillo1, Corey Yanofsky, Sylvie Laboissiere, Robert Nadon, Robert E Kearney.   

Abstract

MOTIVATION: Labeling techniques are being used increasingly to estimate relative protein abundances in quantitative proteomic studies. These techniques require the accurate measurement of correspondingly labeled peptide peak intensities to produce high-quality estimates of differential expression ratios. In mass spectrometers with counting detectors, the measurement noise varies with intensity and consequently accuracy increases with the number of ions detected. Consequently, the relative variability of peptide intensity measurements varies with intensity. This effect must be accounted for when combining information from multiple peptides to estimate relative protein abundance.
RESULTS: We examined a variety of algorithms that estimate protein differential expression ratios from multiple peptide intensity measurements. Algorithms that account for the variation of measurement error with intensity were found to provide the most accurate estimates of differential abundance. A simple Sum-of-Intensities algorithm provided the best estimates of true protein ratios of all algorithms tested.

Mesh:

Substances:

Year:  2009        PMID: 19892804     DOI: 10.1093/bioinformatics/btp610

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  27 in total

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2.  Quantitative analysis of energy metabolic pathways in MCF-7 breast cancer cells by selected reaction monitoring assay.

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Journal:  Nucleic Acids Res       Date:  2017-07-03       Impact factor: 16.971

4.  gpGrouper: A Peptide Grouping Algorithm for Gene-Centric Inference and Quantitation of Bottom-Up Proteomics Data.

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5.  Proteomic profiling of the retinas in a neonatal rat model of oxygen-induced retinopathy with a reproducible ion-current-based MS1 approach.

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Review 8.  A Review on Quantitative Multiplexed Proteomics.

Authors:  Nishant Pappireddi; Lance Martin; Martin Wühr
Journal:  Chembiochem       Date:  2019-04-18       Impact factor: 3.164

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Authors:  Jennifer Parker; Kelly Balmant; Fanchao Zhu; Ning Zhu; Sixue Chen
Journal:  Mol Cell Proteomics       Date:  2014-10-14       Impact factor: 5.911

10.  Experimental Null Method to Guide the Development of Technical Procedures and to Control False-Positive Discovery in Quantitative Proteomics.

Authors:  Xiaomeng Shen; Qiang Hu; Jun Li; Jianmin Wang; Jun Qu
Journal:  J Proteome Res       Date:  2015-09-01       Impact factor: 4.466

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