Synonymous codon usage significantly impacts translational and transcriptional efficiency, gene expression, the secondary structure of both mRNA and proteins, and has been implicated in various diseases. However, population-specific differences in codon usage biases remain largely unexplored. Here, we present a web server, https://cubap.byu.edu, to facilitate analyses of codon usage biases across populations (CUBAP). Using the 1000 Genomes Project, we calculated and visually depict population-specific differences in codon frequencies, codon aversion, identical codon pairing, co-tRNA codon pairing, ramp sequences, and nucleotide composition in 17,634 genes. We found that codon pairing significantly differs between populations in 35.8% of genes, allowing us to successfully predict the place of origin for African and East Asian individuals with 98.8% and 100% accuracy, respectively. We also used CUBAP to identify a significant bias toward decreased CTG pairing in the immunity related GTPase M (IRGM) gene in East Asian and African populations, which may contribute to the decreased association of rs10065172 with Crohn's disease in those populations. CUBAP facilitates in-depth gene-specific and codon-specific visualization that will aid in analyzing candidate genes identified in genome-wide association studies, identifying functional implications of synonymous variants, predicting population-specific impacts of synonymous variants and categorizing genetic biases unique to certain populations.
Ramp sequences occur when the average translational efficiency of codons near the 5′ end of highly expressed genes is significantly lower than the rest of the gene sequence, which counterintuitively increases translational efficiency by decreasing downstream ribosomal collisions. Here, we show that the relative codon adaptiveness within different tissues changes the existence of a ramp sequence without altering the underlying genetic code. We present the first comprehensive analysis of tissue and cell type-specific ramp sequences and report 3108 genes with ramp sequences that change between tissues and cell types, which corresponds with increased gene expression within those tissues and cells. The Ramp Atlas (https://ramps.byu.edu/) allows researchers to query precomputed ramp sequences in 18 388 genes across 62 tissues and 66 cell types and calculate tissue-specific ramp sequences from user-uploaded FASTA files through an intuitive web interface. We used The Ramp Atlas to identify seven SARS-CoV-2 genes and seven human SARS-CoV-2 entry factor genes with tissue-specific ramp sequences that may help explain viral proliferation within those tissues. We anticipate that The Ramp Atlas will facilitate personalized and creative tissue-specific ramp sequence analyses for both human and viral genes that will increase our ability to utilize this often-overlooked regulatory region.
Translational ramp sequences are essential regulatory elements that have yet to be characterized in specific tissues. Ramp sequences increase gene expression by evenly spacing ribosomes and slowing initial translation. Therefore, the relative codon adaptiveness within different tissues changes the existence of a ramp sequence without altering the underlying genetic code. Here, we present the first comprehensive analysis of tissue and cell type-specific ramp sequences, and report 3,108 genes with ramp sequences that change between tissues and cell types. The Ramp Atlas (https://ramps.byu.edu/) is an accompanying web portal that allows researchers to query ramp sequences in 18,388 genes across 62 tissues and 66 cell types. We also identified seven SARS-CoV-2 genes and seven human SARS-CoV-2 entry factor genes with tissue-specific ramp sequences that may help explain viral proliferation within those tissues. We anticipate that The Ramp Atlas will facilitate future tissue-specific ramp sequence analyses to develop targeted therapeutics for human disease.
Motivation: Ramp sequences are an understudied evolutionarily-conserved mechanism for regulating protein translational efficiency. Slowly-translated codons concentrated at the 5' end of genes form ramp sequences that counterintuitively increase overall translational efficiency by evenly spacing ribosomes at initiation, which limits downstream ribosomal collisions. We previously developed ExtRamp, which is the only algorithm to identify translational ramp sequences in single genes. ExtRamp currently lacks a web interface to facilitate wider adoption and application for non-programmers. Additionally, ExtRamp currently identifies ramp sequences using only species-wide codon efficiencies that may lack the specificity of tissue and cell type-specific codon usage biases.Results: We present an online interface for ExtRamp to facilitate wider adoption and application for non-programmers, along with a significant improvement to the underlying algorithm to calculate tissue and cell type-specific ramp sequences (https://ramps.byu.edu/ExtRampOnline). ExtRamp Online contains all options available in the original ExtRamp algorithm with additional pre-set default values to enable researchers to calculate human tissue-specific or genome-wide ramp sequences on any web browser. Human tissue and cell type-specific codon usage biases have been precomputed and can be applied with a simple drop-down menu. Hover-over hints provide users with detailed information on all available options, which will help facilitate future creative analyses using ramp sequences. Availability: ExtRamp Online is publicly available at https://ramps.byu.edu/ExtRampOnline. All associated scripts are publicly available at https://github.com/ridgelab/ExtRampOnline.
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