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Visualizing codon usage within and across genomes: concepts and tools. (English) Zbl 1460.92136

Srinivasa, K. G. (ed.) et al., Statistical modelling and machine learning principles for bioinformatics techniques, tools, and applications. Singapore: Springer. Algorithms Intell. Syst., 213-288 (2020).
Summary: Cost and time of genome sequencing have plummeted over the last decade. This leads to explosive growth of genetic databases and development of novel sequencing-based approaches to study various biological phenomena. The database growth was particularly beneficial for investigation of protein-coding sequences at the codon level, requiring the access to large sets of related genomes. Such studies are expected to illuminate biological forces that shape primary structure of coding sequences and predict their evolutionary trajectories more precisely. In addition to fundamental interest, codon usage studies are of ample practical value, for example, in drug discovery and genomic medicine areas. Nevertheless, the depth of our understanding of codon-related issues is currently shallower as compared to what we know about nucleotide and amino acid sequences. Besides the lack of adequate datasets in the early days of molecular biology, codon usage studies, in our opinion, suffer from underdevelopment of easy-to-use tools to analyze and visualize how codon sequence changes along the gene and across the homologous genes in course of evolution. In this review, we aim to describe main areas of codon usage studies with an emphasis on the tools that allow visual interpretation of the data. We discuss underlying principles of different approaches, what kind of statistics lends confidence in their results and what has to be done to further boost the field of codon usage research.
For the entire collection see [Zbl 1460.92005].

MSC:

92D10 Genetics and epigenetics
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