Introduction: Mouse models share significant genetic similarities with humans and have expanded our understanding of how embryonic tissue-specific genes influence disease states. By improved analyses of temporal, transcriptional data from these models, we can capture unique tissue codon usage patterns and determine how deviations from these patterns can influence developmental disorders.
Methods: We analyzed transcriptomic-weighted data from four mouse strains across three different germ layer tissues (liver, heart, and eye) and through embryonic stages. Applying a multifaceted approach, we calculated relative synonymous codon usage, reduced the dimensionality, and employed machine learning clustering techniques.
Results and discussion: These techniques identified relative synonymous codon usage differences/similarities among strains and deviations in codon usage patterns between healthy and disease-linked genes. Original transcriptomic mouse data and RefSeq gene sequences can be found at the associated Mouse Embryo CoCoPUTs (codon and codon pair usage tables) website. Future studies can leverage this resource to uncover further insights into the dynamics of embryonic development and the corresponding codon usage biases that are paramount to understanding disease processes of embryologic origin.
Keywords: clustering methods; disease-associated comparison; machine learning; mouse embryology; relative synonymous codon usage; tissue-specific; transcriptomic-weighted.
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