ArticleScientific reports2018
Predicting proteome dynamics using gene expression data.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Sharing approaches in predictive genomics across animals, plants and humans.Nature genetics · 2026Review
- Integrative Transcriptomic, Proteomic and Epigenetic Analysis Uncovers Reproductive Dysregulation in F1 Males ofInternational journal of molecular sciences · 2026Article
- Expression of Truncated Products at the 5'-Terminal Region of RIPK2 and Evolutive Aspects that Support Their Biological Importance.Genome biology and evolution · 2024Article
- Article
- Longitudinal multi-omic changes in the transcriptome and proteome of peripheral blood cells after a 4 Gy total body radiation dose to Rhesus macaques.BMC genomics · 2023Article
- Efficacy of resveratrol against breast cancer and hepatocellular carcinoma cell lines.Saudi medical journal · 2023Article
- Better Agreement of Human Transcriptomic and Proteomic Cancer Expression Data at the Molecular Pathway Activation Level.International journal of molecular sciences · 2022Article
- Article
- RNA-sequencing and mass-spectrometry proteomic time-series analysis of T-cell differentiation identified multiple splice variants models that predicted validated protein biomarkers in inflammatory diseases.Frontiers in molecular biosciences · 2022Article
- Deciphering cell-cell interactions and communication from gene expression.Nature reviews. Genetics · 2021Review
- Article
- Integrative characterization of the near-minimal bacterium Mesoplasma florum.Molecular systems biology · 2020Article
- Proteomic Analysis of Stationary Growth Stage Adaptation and Nutritional Deficiency Response ofFrontiers in microbiology · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
While protein concentrations are physiologically most relevant, measuring them globally is challenging. mRNA levels are easier to measure genome-wide and hence are typically used to infer the corresponding protein abundances. The steady-state condition (assumption that protein levels remain constant) has typically been used to calculate protein concentrations, as it is mathematically convenient, even though it is often not satisfied. Here, we propose a method to estimate genome-wide protein abundances without this assumption. Instead, we assume that the system returns to its baseline at the end of the experiment, which is true for cyclic phenomena (e.g. cell cycle) and many time-course experiments. Our approach only requires availability of gene expression and protein half-life data. As proof-of-concept, we predicted proteome dynamics associated with the budding yeast cell cycle, the results are available for browsing online at http://dynprot.cent.uw.edu.pl/ . The approach was validated experimentally by verifying that the predicted protein concentration changes were consistent with measurements for all proteins tested. Additionally, if proteomic data are available as well, we can also infer changes in protein half-lives in response to posttranslational regulation, as we did for Clb2, a post-translationally regulated protein. The predicted changes in Clb2 abundance are consistent with earlier observations.
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