Proteomics

Dataset Information

0

Serial mixture of peptides from mouse skin and primary human mesenchymal stem cells for protein quant methods benchmarking


ABSTRACT: Multivariate regression modelling provides a statistically powerful means of quantifying the effects of a given treatment while compensating for sources of variation and noise, such as variability between human donors and the behaviour of different peptides during mass spectrometry. However, methods to quantify endogenous post-translational modifications (PTMs) are typically reliant on summary statistical methods that fail to consider sources of variability such as changes in levels of the parent protein. Here, we compare three multivariate regression methods, including a novel Bayesian elastic net algorithm (BayesENproteomics) that enables assessment of relative protein abundances while also quantifying identified PTMs for each protein. We tested the ability of these methods to accurately quantify expression of proteins in a mixed-species benchmark experiment, and to quantify synthetic PTMs induced by stable isotope labelling. Finally, we extended our regression pipeline to calculate fold changes at the pathway level, providing a complement to commonly used enrichment analysis. Our results show that BayesENproteomics can quantify changes to protein levels across a broad dynamic range while also accurately quantifying PTM and pathway-level fold changes.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human) Mus Musculus (mouse)

TISSUE(S): Multipotent Stem Cell, Cell Culture, Skin

SUBMITTER: Venkatesh Mallikarjun  

LAB HEAD: Joe Swift

PROVIDER: PXD012772 | Pride | 2019-03-12

REPOSITORIES: Pride

Dataset's files

Source:
Action DRS
20171208_SwiftJ_VM12B.raw Raw
20171208_SwiftJ_VM12C.raw Raw
20171208_SwiftJ_VM12D.raw Raw
20171208_SwiftJ_VM34B.raw Raw
20171208_SwiftJ_VM34C.raw Raw
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Publications

BayesENproteomics: Bayesian Elastic Nets for Quantification of Peptidoforms in Complex Samples.

Mallikarjun Venkatesh V   Richardson Stephen M SM   Swift Joe J  

Journal of proteome research 20200508 6


Multivariate regression modelling provides a statistically powerful means of quantifying the effects of a given treatment while compensating for sources of variation and noise, such as variability between human donors and the behavior of different peptides during mass spectrometry. However, methods to quantify endogenous post-translational modifications (PTMs) are typically reliant on summary statistical methods that fail to consider sources of variability such as changes in the levels of the pa  ...[more]

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