Literature DB >> 24799117

pQuant improves quantitation by keeping out interfering signals and evaluating the accuracy of calculated ratios.

Chao Liu1, Chun-Qing Song, Zuo-Fei Yuan, Yan Fu, Hao Chi, Le-Heng Wang, Sheng-Bo Fan, Kun Zhang, Wen-Feng Zeng, Si-Min He, Meng-Qiu Dong, Rui-Xiang Sun.   

Abstract

In relative protein abundance determination from peptide intensities recorded in full mass scans, a major complication that affects quantitation accuracy is signal interference from coeluting ions of similar m/z values. Here, we present pQuant, a quantitation software tool that solves this problem. pQuant detects interference signals, identifies for each peptide a pair of least interfered isotopic chromatograms: one for the light and one for the heavy isotope-labeled peptide. On the basis of these isotopic pairs, pQuant calculates the relative heavy/light peptide ratios along with their 99.75% confidence intervals (CIs). From the peptides ratios and their CIs, pQuant estimates the protein ratios and associated CIs by kernel density estimation. We tested pQuant, Census and MaxQuant on data sets obtained from mixtures (at varying mixing ratios from 10:1 to 1:10) of light- and heavy-SILAC labeled HeLa cells or (14)N- and (15)N-labeled Escherichia coli cells. pQuant quantitated more peptides with better accuracy than Census and MaxQuant in all 14 data sets. On the SILAC data sets, the nonquantified "NaN" (not a number) ratios generated by Census, MaxQuant, and pQuant accounted for 2.5-10.7%, 1.8-2.7%, and 0.01-0.5% of all ratios, respectively. On the (14)N/(15)N data sets, which cannot be quantified by MaxQuant, Census and pQuant produced 0.9-10.0% and 0.3-2.9% NaN ratios, respectively. Excluding these NaN results, the standard deviations of the numerical ratios calculated by Census or MaxQuant are 30-100% larger than those by pQuant. These results show that pQuant outperforms Census and MaxQuant in SILAC and (15)N-based quantitation.

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Year:  2014        PMID: 24799117     DOI: 10.1021/ac404246w

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  37 in total

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4.  Comprehensive identification of peptides in tandem mass spectra using an efficient open search engine.

Authors:  Hao Chi; Chao Liu; Hao Yang; Wen-Feng Zeng; Long Wu; Wen-Jing Zhou; Rui-Min Wang; Xiu-Nan Niu; Yue-He Ding; Yao Zhang; Zhao-Wei Wang; Zhen-Lin Chen; Rui-Xiang Sun; Tao Liu; Guang-Ming Tan; Meng-Qiu Dong; Ping Xu; Pei-Heng Zhang; Si-Min He
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Review 5.  Proteome-Wide Analysis of Cysteine S-Sulfenylation Using a Benzothiazine-Based Probe.

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6.  Establishment of Dimethyl Labeling-based Quantitative Acetylproteomics in Arabidopsis.

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Journal:  Mol Cell Proteomics       Date:  2018-02-13       Impact factor: 5.911

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Authors:  Yuanhui Ma; Daniel B McClatchy; Salim Barkallah; William W Wood; John R Yates
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Journal:  Elife       Date:  2016-03-08       Impact factor: 8.140

10.  A quantitative thiol reactivity profiling platform to analyze redox and electrophile reactive cysteine proteomes.

Authors:  Ling Fu; Zongmin Li; Keke Liu; Caiping Tian; Jixiang He; Jingyang He; Fuchu He; Ping Xu; Jing Yang
Journal:  Nat Protoc       Date:  2020-07-20       Impact factor: 13.491

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