MOTIVATION: Codon usage preference patterns have been associated with modulation of translation efficiency, protein folding, and mRNA decay. However, new studies support that codon pair usage has also a remarkable effect at the gene expression level. Here, we expand the concept of CAI to answer if codon pair usage patterns can be understood in terms of codon usage bias, or if they offer new information regarding coding translation efficiency. RESULTS: Through the implementation of a weighting strategy to consider the dicodon contributions, we observe that the dicodon-based measure has greater correlations with gene expression level than CAI. Interestingly, we have noted that dicodons associated with a low value of adaptiveness are related to dicodons which mediate strong translational inhibition in yeast. We have also noticed that some codon-pairs have a smaller dicodon contribution than estimated by the product of the respective codon contributions. AVAILABILITY AND IMPLEMENTATION: Scripts, implemented in Python, are freely available for download at https://zenodo.org/record/7738276#.ZBIDBtLMIdU.

MOTIVATION: The need is to visualize and quantify gene expression spatial patterns. Because of their generality for representation of interaction among several elements, complex networks are used to measure the spatial interactions and adjacencies defined by gene expression patterns. RESULTS: Enhanced visualization of spatial interactions between elements where genes are expressed is possible, allowing the identification of structures which would go unnoticed by using conventional imaging. The quantification of the expression intensity in terms of the node degree and clustering coefficient allows the identification of different types of interactions, yielding insights about cell signaling and differentiation, and providing the basis for comparison and discrimination of the patterns along the developmental stages. AVAILABILITY: Supplementary Material, including visualizations as well as the basic routines for translating gene expression images into complex networks and obtaining node degree and clustering coefficient measurements, are provided. CONTACT: [email protected]; [email protected].