Peptide Correlation Analysis (PeCorA) Reveals Differential Proteoform Regulation.
Peptide Correlation Analysis (PeCorA) Reveals Differential Proteoform Regulation.
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DOI:
10.1021/acs.jproteome.0c00602
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发表时间:
2021-04-02
影响因子:
4.4
通讯作者:
Meyer JG
中科院分区:
文献类型:
--
作者:
Dermit M;Peters-Clarke TM;Shishkova E;Meyer JG
Shotgun proteomics techniques infer the presence and quantity of proteins using peptide proxies produced by cleavage of the proteome with a protease. Most protein quantitation strategies assume that multiple peptides derived from a protein will behave quantitatively similar across treatment groups, but this assumption may be false due to (1) heterogeneous proteoforms and (2) technical artifacts. Here we describe a strategy called peptide correlation analysis (PeCorA) that detects quantitative disagreements between peptides mapped to the same protein. PeCorA fits linear models to assess whether a peptide’s change across treatment groups differs from all other peptides assigned to the same protein. PeCorA revealed that ~15% of proteins in a mouse microglia stress data set contain at least one discordant peptide. Inspection of the discordant peptides shows the utility of PeCorA for the direct and indirect detection of regulated post-translational modifications (PTMs) and also for the discovery of poorly quantified peptides. The exclusion of poorly quantified peptides before protein quantity summarization decreased false-positives in a benchmark data set. Finally, PeCorA suggests that the inactive isoform of prothrombin, a coagulation cascade protease, is more abundant in plasma from COVID-19 patients relative to non-COVID-19 controls. PeCorA is freely available as an R package that works with arbitrary tables of quantified peptides.
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影响因子:
4.1
作者:
Lorenz, Robin;Moon, Eui-Whan;Herberg, Friedrich W.
通讯作者:
Herberg, Friedrich W.
影响因子:
3.3
作者:
Guergues, Jennifer;Wohlfahrt, Jessica;Stevens, Stanley M., Jr.
通讯作者:
Stevens, Stanley M., Jr.
影响因子:
158.5
作者:
Ackermann, Maximilian;Verleden, Stijn E.;Jonigk, Danny
通讯作者:
Jonigk, Danny
DOI:
10.1093/bioinformatics/btp101
发表时间:
2009-04-15
期刊:
Bioinformatics (Oxford, England)
影响因子:
--
作者:
Bindea G;Mlecnik B;Hackl H;Charoentong P;Tosolini M;Kirilovsky A;Fridman WH;Pagès F;Trajanoski Z;Galon J
通讯作者:
Galon J
DOI:
10.1111/j.2517-6161.1995.tb02031.x
发表时间:
1995-01-01
影响因子:
5.8
作者:
BENJAMINI, Y;HOCHBERG, Y
通讯作者:
HOCHBERG, Y