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Can we predict protein from mRNA levels?

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A Brief Communications Arising to this article was published on 27 July 2017

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Figure 1: Across-genes correlations versus within-genes correlations between observed and predicted protein levels.

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Authors and Affiliations

Authors

Contributions

N.F. and G.V.C.F. designed the research project. C.M.O. and P.P. contributed to the design of the project and provided funding. N.F. designed the manuscript and analysed the data. P.P. and G.V.C.F. wrote the paper. G.V.C.F. supervised the research project. All authors contributed to extensive discussions and revisions of all drafts of the paper.

Corresponding author

Correspondence to Gabriela V. Cohen Freue.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Relation between gene-specific protein predictions and observed protein levels.

a, mRNA and protein in simulated data for three genes (colours) in five tissues. The data points for one tissue are highlighted and the error from the ratio-based prediction is indicated. b, Predicted and observed protein in simulated data for three genes (colours) in one tissue from a. The error in the prediction is indicated by the distance from the point to the 45° line. c, mRNA (open symbols) and predicted protein (solid symbols) on the x-axis and observed protein on the y-axis. The plot shows real data for four example genes. Data points from one tissue and their modification by the prediction of Wilhelm et al.1 are indicated by an error.

Extended Data Figure 2 mRNA contribution to protein prediction.

mRNA and protein in simulated data for three genes (A, B, and C, colours) in five tissues. a, Three gene-specific models (grey lines) to predict protein levels from mRNA levels as in Wilhelm et al. b, Three gene-specific models (grey lines) to predict protein levels without using mRNA.

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Fortelny, N., Overall, C., Pavlidis, P. et al. Can we predict protein from mRNA levels?. Nature 547, E19–E20 (2017). https://doi.org/10.1038/nature22293

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