The authors used a bottom‑up thermodynamic modelling framework to investigate how plants decode calcium signals, starting from Ca2+ binding to EF‑hand proteins and extending to higher‑order decoding modules. They identified six universal Ca2+-decoding modules that can explain variations in calcium sensitivity among kinases and provide a theoretical basis for interpreting calcium signal amplitude and frequency in plant cells.

High-quality PacBio HiFi draft genome assemblies were generated for three Bouteloua species (B. curtipendula, B. gracilis, B. eriopoda) with >98.5% BUSCO completeness. Gene prediction with Helixer produced inflated gene counts likely reflecting polyploidy and fragmented predictions, and panEDTA identified 25–40% transposable-element content dominated by LTR retrotransposons. These assemblies provide foundational references for comparative genomics within PACMAD grasses.

The study presents high-quality nuclear and organellar genome assemblies for Gerbera hybrida, generated using PacBio HiFi and Omni-C chromatin capture sequencing, resulting in a 2.32 Gb nuclear genome assembled into 25 scaffolds matching its chromosome number. Annotation identified 36,160 protein‑coding genes and detailed mitochondrial and chloroplast genomes, establishing a valuable genomic resource for molecular breeding and research in Gerbera and the Asteraceae family.

The study combined long‑read whole‑genome assembly, multi‑omics profiling (DNA methylation, mRNA, ribosome‑associated transcripts, tRNA abundance, and protein levels) in two Arabidopsis thaliana accessions to evaluate how genomic information propagates through the Central Dogma. Codon usage in gene sequences emerged as the strongest predictor of both mRNA and protein abundance, while methylation, tRNA levels, and ribosome‑associated transcripts contributed little additional information under stable conditions.

The study performed a comprehensive computational analysis of the Arabidopsis thaliana proteome, classifying 48,359 proteins by melting temperature (Tm) and melting temperature index (TI) and linking thermal stability to amino acid composition, molecular mass, and codon usage. Machine‑learning and evolutionary analyses revealed that higher molecular mass and specific codon pairs correlate with higher Tm, and that gene duplication has driven the evolution of high‑Tm proteins, suggesting a genomic basis for stress resilience.