ArticleBMC bioinformatics2013
Transcript features alone enable accurate prediction and understanding of gene expression in S. cerevisiae.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 29 citations in OpenAlex.
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- Effective Gene Expression Prediction and Optimization from Protein Sequences.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Amino acid sequence encodes protein abundance shaped by protein stability at reduced synthesis cost.Protein science : a publication of the Protein Society · 2025Article
- Modeling the ribosomal small subunit dynamic in Saccharomyces cerevisiae based on TCP-seq data.Nucleic acids research · 2022Article
- Optimizing the dynamics of protein expression.Scientific reports · 2019Article
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- The codon sequences predict protein lifetimes and other parameters of the protein life cycle in the mouse brain.Scientific reports · 2018Article
- Genome scale analysis of Escherichia coli with a comprehensive prokaryotic sequence-based biophysical model of translation initiation and elongation.DNA research : an international journal for rapid publication of reports on genes and genomes · 2018Article
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- Unsupervised detection of regulatory gene expression information in different genomic regions enables gene expression ranking.BMC bioinformatics · 2017Article
- Predictive biophysical modeling and understanding of the dynamics of mRNA translation and its evolution.Nucleic acids research · 2016Article
- Direct and Absolute Quantification of over 1800 Yeast Proteins via Selected Reaction Monitoring.Molecular & cellular proteomics : MCP · 2016Article
- Multiple roles of the coding sequence 5' end in gene expression regulation.Nucleic acids research · 2015Review
- Rationally designed, heterologous S. cerevisiae transcripts expose novel expression determinants.RNA biology · 2015Article
- Modelling the efficiency of codon-tRNA interactions based on codon usage bias.DNA research : an international journal for rapid publication of reports on genes and genomes · 2014Article
- Co-occurrence of transcription and translation gene regulatory features underlies coordinated mRNA and protein synthesis.BMC genomics · 2014Article
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Authors and funding
2 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundGene expression is a central process in all living organisms. Central questions in the field are related to the way the expression levels of genes are encoded in the transcripts and affect their evolution, and the potential to predict expression levels solely by transcript features. In this study we analyze S. cerevisiae, a model organism with the most abundant relevant cellular and genomic measurements, to evaluate the accuracy in which expression levels can be predicted by different parts of the transcript. To this end, we perform various types of regression analyses based on a total of 5323 features of the transcript. The main advantage of the proposed predictors over previous ones is related to the accurate and comprehensive definitions of the relevant transcript features, which are based on biophysical knowledge of the gene transcription and translation processes, their modeling and evolution.
resultsCross validation analyses of our predictors demonstrate that they achieve a correlation of 0.68/0.68/0.70/0.61/0.81 with mRNA levels, ribosomal density, protein levels, proteins per mRNA molecule (PPR), and ribosomal load (RL) respectively (all p-values <10(-140)). When we consider predictors that are based exclusively on the features related to different parts of the transcript (5'UTR, ORF, 3'UTR), the correlations with protein levels were 0.27/0.71/0.25 (all p-values <10(-5)), suggesting that the information in the UTRs is redundant, and features of the ORF alone yield similar predictions to the ones obtained based on the entire transcript.
conclusionsThe reported results demonstrate that in the analyzed model organism the expression levels of a gene are encoded in the transcript. Specifically, the prediction of a large fraction of the variance of the different gene expression steps based on transcript features alone is feasible in S. cerevisiae. We report dozens of novel transcript features related to expression levels predictions, demonstrating how such analyses can aid in understanding the gene expression process and its evolution, and how such predictors can be designed for other organisms in the future.
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