Analysis of computational codon usage models and their association with translationally slow codons

Abstract

<jats:title>Abstract</jats:title><jats:p>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 <jats:italic>genome-wide</jats:italic> 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 <jats:italic>highly expressed</jats:italic> 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 <jats:italic>S. cerevisiae</jats:italic>, 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.</jats:p>

Sentiment Analysis with Machine Learning Methods on Social Media

“…In this study, confusion matrix was used to evaluate the models developed with classification algorithms (Wright et al, 2020). For the performance evaluation, four statistical measures were used, including the accuracy (ACC), sensitivity (SENS), specificity (SPEC), and F-measure (F).…”

CHARMING: Harmonizing synonymous codon usage to replicate a desired codon usage pattern

“…Currently, there is no broad consensus regarding which codon usage measure is most predictive of translation rate or functional protein production. 26,27 Hence it can be useful to compare codon patterns generated by different codon usage measures. Here, we focus primarily on CAI, as it is one of the earliest and most widely used codon usage measures, and %MinMax, which has been shown to be predictive of the effect of local ribosome elongation rate on co-translational protein folding in E. coli.…”

“…In the examples shown here (Figures 1 and 4), window sizes of 17 and 9 codons were used; note that a window size of 10 codons was found to correlate most closely with ribosome footprint counts. 27,30 CHARMING then assigns the resulting value for a particular sliding window to the middle codon in the sliding window (or, in the case of even-sized windows, the right-most center codon), creating per-codon output values. These per-codon values are then compared to the target values (i.e., the per-codon values of the WT sequence when analyzed with the same measure, using codon usage values from the origin species).…”

Position-dependent Codon Usage Bias in the Human Transcriptome

“…Another approach to quantifying mRNA coding potential is codon usage bias (CUB), which refers to the frequency of usage for each codon in the coding portion of a transcriptome relative to the frequency of synonymous codons. Computational models of CUB have been shown to perform well in predicting translational efficiency and also show an association with ribosome profiling data [10]. The codon adaptation index (CAI) is a widely recognized metric of CUB that assigns a score to each transcript based on its length and codon composition relative to overall CUB across the coding transcriptome [11].…”