The study benchmarked over 20 web‑based gRNA on‑target efficiency prediction tools against an experimental plant CRISPR editing dataset, finding several machine‑learning based tools whose scores strongly correlated with observed InDel frequencies. Additionally, the performance of popular platforms such as CRISPOR and CRISPR‑P was assessed, offering guidance for improved gRNA design in plant genome editing.

The study evaluates the use of single-cell RNA sequencing (scRNA-seq) data to predict plant metabolic pathway genes (MPGs) in Arabidopsis thaliana, comparing five multi-label machine‑learning algorithms against traditional bulk RNA‑seq approaches. scRNA‑seq generated co‑expression networks that, while different, yielded significantly higher MPG classification accuracy, especially when data were split by genetic background or tissue type, and deep learning outperformed classical methods. The authors conclude that scRNA‑seq offers superior predictive power and should be incorporated into future MPG discovery pipelines.

The study developed a molecular assay to identify Myrtle rust‑resistant Melaleuca quinquenervia individuals for restoration. Artificial inoculations were followed by whole‑genome sequencing of 492 seedlings and a GWAS that pinpointed SNP clusters in three chromosomal regions, including candidate R‑genes, which were refined into a 1,049‑SNP panel achieving a genomic prediction accuracy of R = 0.83.

The study developed a high-throughput phenotyping platform to assess root infestation by Orobanche cumana in a diverse sunflower association mapping population and applied a dual GWAS using SNPs and k-mers to uncover resistance loci. It validated known QTLs with higher resolution, identified novel candidate genes such as leucine‑rich repeat receptor kinases, and highlighted introgressed segments from wild Helianthus species that contribute to broomrape resistance.

The study reports the creation and validation of a high‑density Axiom SNP array for Solanum tuberosum, based on 10X Genomics sequencing of 108 diverse clones and integration of existing Illumina markers. The array demonstrated high reproducibility and, after filtering, provided 206,616 informative markers for population structure analysis, GWAS of polyphenol oxidase activity, and genomic prediction with accuracies up to 0.86.

A genome-wide association study of 166 Iberian Arabidopsis accessions identified loci, including ABA3 and GA2ox2, that modulate the inhibitory effect of the auxin precursor indole-3-acetamide (IAM) on primary root elongation. Integrating sequence analysis, transcriptomics, 3D protein modeling, and mutant physiology revealed that IAM promotes ABA biosynthesis and signaling, uncovering a novel node of hormone crosstalk.

The study integrated weekly morphophysiological measurements with high-density genotyping-by-sequencing data and a machine‑learning pipeline to dissect flowering time variation in diverse Cannabis sativa landraces. By applying mutual information, recursive feature elimination, random forest, and support vector machine classifiers to over 234,000 combined genetic, phenotypic, and environmental features, the authors identified 53 key markers that classify early, medium, and late flowering types with 96.6% accuracy. Notable loci, including CsFT3 and CsCFL1, were highlighted as promising targets for breeding and smart‑crop strategies.

The study used multi‑season phenotyping for iron, zinc, and protein content together with whole‑genome re‑sequencing of a groundnut mini‑core collection to conduct a genome‑wide association study, identifying numerous marker‑trait associations and candidate genes linked to nutrient homeostasis. SNP‑based KASP markers were designed for nine loci, of which three showed polymorphism and are ready for deployment in genomics‑assisted breeding for nutrient‑rich groundnut varieties.

The study integrates genome, transcriptome, and chromatin accessibility data from 380 soybean accessions to dissect the genetic and regulatory basis of symbiotic nitrogen fixation (SNF). Using GWAS, TWAS, eQTL mapping, and ATAC-seq, the authors identify key loci, co‑expression modules, and regulatory elements, and validate the circadian clock gene GmLHY1b as a negative regulator of nodulation via CRISPR and CUT&Tag. These resources illuminate SNF networks and provide a foundation for soybean improvement.

The study generated a phenotypic dataset for 550 Lactuca accessions, including 20 wild relatives, and applied an iterative two‑step GWAS using a jointly processed SNP set for cultivated lettuce (L. sativa) and its wild progenitor (L. serriola) to dissect trait loci. Known and novel QTLs for anthocyanin accumulation, leaf morphology, and pathogen resistance were identified, with several L. serriola‑specific QTLs revealing unique genetic architectures, underscoring the breeding value of wild lettuce species.