1. Analysis of computational codon usage models and their association with translationally slow codons
- Author
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Gabriel Wright, Jun Li, Scott J. Emrich, Tijana Milenkovic, Patricia L. Clark, and Anabel Rodriguez
- Subjects
Computer science ,Gene Expression ,Genetic Footprinting ,Yeast and Fungal Models ,Ribosome ,Biochemistry ,Database and Informatics Methods ,0302 clinical medicine ,Databases, Genetic ,Codon Usage ,0303 health sciences ,Computational model ,Multidisciplinary ,Data Processing ,Messenger RNA ,030302 biochemistry & molecular biology ,Eukaryota ,Translation (biology) ,Nucleic acids ,Experimental Organism Systems ,Codon usage bias ,Medicine ,Cellular Structures and Organelles ,Information Technology ,Sequence Analysis ,Research Article ,Computer and Information Sciences ,Bioinformatics ,Science ,Genomics ,Computational biology ,Saccharomyces cerevisiae ,Genetic Fingerprinting and Footprinting ,Research and Analysis Methods ,03 medical and health sciences ,Saccharomyces ,Model Organisms ,Genetics ,RNA, Messenger ,Protein translation ,Association (psychology) ,Codon ,Molecular Biology Techniques ,Gene ,Molecular Biology ,030304 developmental biology ,fungi ,Organisms ,Fungi ,Computational Biology ,Biology and Life Sciences ,Cell Biology ,Models, Theoretical ,Yeast ,Protein Biosynthesis ,Animal Studies ,RNA ,Protein Translation ,Ribosomes ,030217 neurology & neurosurgery - Abstract
Improved computational modeling of protein translation rates, including better prediction of where translational slowdowns along an mRNA sequence may occur, is critical for understanding co-translational folding. Because codons within a synonymous codon group are translated at different rates, many computational translation models rely on analyzing synonymous codons. Some models rely on genome-wide codon usage bias (CUB), believing that globally rare and common codons are the most informative of slow and fast translation, respectively. Others use the CUB observed only in highly expressed genes, which should be under selective pressure to be translated efficiently (and whose CUB may therefore be more indicative of translation rates). No prior work has analyzed these models for their ability to predict translational slowdowns. Here, we evaluate five models for their association with slowly translated positions as denoted by two independent ribosome footprint (RFP) count experiments from S. cerevisiae, because RFP data is often considered as a “ground truth” for translation rates across mRNA sequences. We show that all five considered models strongly associate with the RFP data and therefore have potential for estimating translational slowdowns. However, we also show that there is a weak correlation between RFP counts for the same genes originating from independent experiments, even when their experimental conditions are similar. This raises concerns about the efficacy of using current RFP experimental data for estimating translation rates and highlights a potential advantage of using computational models to understand translation rates instead.
- Published
- 2020