On the Dependency of Cellular Protein Levels on mRNA Abundance.

By Yansheng Liu, A. Beyer, R. Aebersold. Cell, 2016

2,851 citations recorded by the source corpus.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/35d55d0bd47e71b73a11165a9ec2e158ba575284

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Citations in the corpus

Deciphering cell–cell interactions and communication from gene expression (2020, background): “Although mRNA and protein levels are qualitatively correlated, transcriptomics may not represent a fully accurate view of intercellular communication, as transcript and protein abundances can be uncoupled by post-transcriptional and post-translational processes 82,115,116 .”
Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data (2020, result): “Recently, it has become evident that mRNA levels are not sufficient to predict protein levels and our results are consistent with the previous report [45].”
The impact of sex on gene expression across human tissues (2020, background): “In efforts to understand the molecular basis of sex differences in disease and other phenotypes, it is important to note that the connection between the molecular changes observed here and complex phenotypes is likely to be complicated by many compensatory and buffering effects (61).”
A deep proteome and transcriptome abundance atlas of 29 healthy human tissues (2018, background): “…the much higher dynamic range of protein abundance as well as rather poor correlation of mRNA and protein levels suggesting that further, and possibly diverse regulatory elements play important roles (Schwanhäusser et al. 2011; Liu, Beyer, and Aebersold 2016; Franks, Airoldi, and Slavov 2017).”
Quantitative Proteomics of the Cancer Cell Line Encyclopedia (2020, critical): “Although commonly used as a proxy for protein expression, on average RNA expression data predict protein expression poorly (Gygi et al., 1999; Liu et al., 2016).”
Joint probabilistic modeling of single-cell multi-omic data with totalVI (2021, background): “For example, totalVI could be used to investigate the relationship between the level of an RNA transcript and the level of its encoded protein in different biological settings, which remains an open question [52].”