International Journal of Molecular Evolution and Biodiversity 2024, Vol.14, No.3, 120-132 http://ecoevopublisher.com/index.php/ijmeb 123 Figure 2 Assembly and biallelic differentiation of the cassava genome SC205 (Photo credit: Hu et al., 2021) Image caption: (A) Comparison of the cassava SC205 and previously assembled AM560 genomes. (B) Assessment of the homologous chromosomes assembly based on frequencies of intrachromosomal interactions using Hi-C. (C and D) Allelic expression profiles of divergent bialleles on chromosome 3 in various tissues (C) and developmental stages of storage roots (D). (E) Histograms of biallelic expression of divergent alleles on chromosome three among different tissues and developmental stages of storage roots (SR). (F) Comparison of expression dominance of divergent bialleles in different tissues. (G) Sequence divergence between promoter regions and between the coding DNA sequence (CDS) of bialleles (Wilcoxon signed-rank test, P < 0.01). (H) Relationship between median values for Ka/Ks subsets and the maximum observed biallelic expression divergence for each divergent biallele pair with 0.95 confidence (Adapted from Hu et al., 2021) 2.3 Role of bioinformatics Bioinformatics plays a pivotal role in cassava phylogenetic analysis by providing the software and computational tools necessary for handling and analyzing large-scale genetic data. For example, genomic selection models that incorporate spatial kernels, such as Power, Spherical, and Gaussian, are used to account for spatial variation in field experiments, thereby increasing the accuracy of breeding value estimations (Elias et al., 2017). Additionally, genome-wide association analysis (GWAS) is facilitated by bioinformatics tools that can handle the complex data derived from image phenotyping protocols, as demonstrated in the study of root characteristics in cassava (Yonis et al., 2020). These tools are essential for the high-throughput methods applied in cassava research, enabling the identification of significant quantitative trait loci (QTL) and the prediction of genomic selection accuracies.
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