Poplar (Populus) is a well-established model system for tree genomics and molecular breeding, and hybrid poplar is widely used in forest plantations. However, distinguishing its diploid homologous chromosomes is difficult, complicating advanced functional studies on specific alleles. In this study, we applied a trio-binning design and PacBio High-Fidelity long-read sequencing to obtain haplotype-phased telomere-to-telomere genome assemblies for the two parents of the well-studied F₁ hybrid “84K” (Populus alba × P. tremula var. glandulosa). Almost all chromosomes, including the telomeres and centromeres, were completely assembled for each haplotype subgenome apart from two small gaps on one chromosome. By incorporating information from these haplotype assemblies and extensive RNA-seq data, we analyzed gene expression patterns between the two subgenomes and alleles. Transcription bias at the subgenome level was not uncovered, but extensive expression differences were detected between alleles. We developed machine-learning (ML) models to predict allele-specific expression (ASE) with high accuracy and identified underlying genome features most highly influencing ASE. One of our models with 15 predictor variables achieved 77% accuracy on the training set and 74% accuracy on the testing set. ML models identified gene body CHG methylation, sequence divergence, and transposon occupancy both upstream and downstream of alleles as important factors for ASE. Our haplotype-phased genome assemblies and ML strategy highlight an avenue for functional studies in Populus and provide additional tools for studying ASE and heterosis in hybrids.

Summary: Long noncoding RNAs (lncRNAs) are critical regulators of numerous biological processes in plants. Nevertheless, their identification is challenging due to the low sequence conservation across various species. Existing computational methods for lncRNA identification often face difficulties in generalizing across diverse plant species, highlighting the need for more robust and versatile identification models. Here, we present PlantLncBoost, a novel computational tool designed to improve the generalization in plant lncRNA identification. By integrating advanced gradient boosting algorithms with comprehensive feature selection, our approach achieves both high accuracy and generalizability. We conducted an extensive analysis of 1662 features and identified three key features – ORF coverage, complex Fourier average, and atomic Fourier amplitude – that effectively distinguish lncRNAs from mRNAs. We assessed the performance of PlantLncBoost using comprehensive datasets from 20 plant species. The model exhibited exceptional performance, with an accuracy of 96.63%, a sensitivity of 98.42%, and a specificity of 94.93%, significantly outperforming existing tools. Further analysis revealed that the features we selected effectively capture the differences between lncRNAs and mRNAs across a variety of plant species. PlantLncBoost represents a significant advancement in plant lncRNA identification. It is freely accessible on GitHub ( https://github.com/xuechantian/PlantLncBoost ) and has been integrated into a comprehensive analysis pipeline, Plant‐LncRNA‐pipeline v.2 ( https://github.com/xuechantian/Plant‐LncRNA‐pipeline‐v2 ).

The genus Rhododendron exhibits an immense diversity of flower colors and represents one of the largest groups of woody plants, which is of great importance for ornamental plant research. This review summarizes recent progress in deciphering the genetic basis for flower coloration in Rhododendron. We describe advances in phylogenetic reconstruction and genome sequencing of Rhododendron species. The metabolic pathways of flower color are outlined, focusing on key structural and regulatory genes involved in pigment synthesis. Gene duplications and losses associated with color diversification are discussed. In addition, the application of multi-omics approaches and analysis of gene co-expression networks to elucidate complex gene regulatory mechanisms is emphasized. This synthesis of current knowledge provides a foundation for future research on the evolution of flower color diversity within the Rhododendron lineage. Ultimately, these discoveries will support breeding endeavors aimed at harnessing the genetics of flower coloration and developing novel cultivars that exhibit desired floral traits.

Crop breeding has entered the pangenomics era, unlocking a far more comprehensive view of genetic diversity than a single reference genome can capture. In rice (Oryza sativa), a staple crop critical to global food security, the construction of pangenome resources has uncovered extensive structural variations (SVs), novel genes, and presence/absence variations (PAVs) that underpin key agronomic traits. As the rice pangenome matures from a research resource into a practical breeding tool, it promises to accelerate the development of higher-yielding, stress-resilient, and disease-resistant varieties. This transition represents a pivotal advance toward sustainable agriculture and enhanced global food security, while also establishing a model for applying pangenomics to other crops. Here, we review how rice pangenome research, encompassing both cultivated and wild species, has advanced trait discovery (from yield improvement and disease resistance to stress tolerance) and enabled new molecular breeding strategies. Despite these advances, several challenges remain before pangenomic data can be routinely integrated into breeding pipelines. The complexity of graph-based data structures, difficulties in detecting multiallelic variants from population-wide resequencing data, and the lack of breeder-friendly genotyping tools are significant barriers. Additionally, while artificial intelligence (AI) and machine learning (ML) approaches show great promise for interpreting complex pangenomic data and accelerating trait discovery, their practical adoption is hindered by the absence of breeder-oriented interfaces, integration challenges with phenotypic and environmental data, and high computational demands. Overcoming these issues will require interdisciplinary collaboration, robust infrastructure, and innovations focused on practical breeding needs across diverse crop species.

Grain transparency (GT) is a key determinant of rice appearance quality and consumer preference, yet its genetic basis remains poorly understood. In this study, we evaluated GT in a panel of 450 rice accessions grown in two environments (Guangzhou and Yangjiang). Nineteen quantitative trait loci (QTL) for GT were identified by a genome-wide association study (GWAS), distributed across all rice chromosomes except chromosomes 2 and 12. Among these, four QTL (qGT-1b, qGT-3b, qGT-4b, and qGT-5b) were consistently detected in both environments. Notably, qGT-3b and qGT-5b co-localized with the grain shape genes GRAIN SIZE 3 (GS3) and GRAIN WIDTH 5 (GW5), respectively. For the novel and stable QTL qGT-4b, linkage disequilibrium decay analysis delimited an approximately 300 kb candidate region. Integrative transcriptomic and genomic analyses pointed to two candidate genes: LOC_Os04g40370 (OsFbx142), encoding an F-box domain protein, and LOC_Os04g40720 (OsSUBSrP1), encoding a subtilisin-like serine protease. Haplotype analysis further revealed that specific haplotypes of these genes were significantly associated with GT variation across environments. Our findings provide genetic insights into the regulation of rice grain transparency and offer candidate genes for further functional validation and potential application in improving rice appearance quality.

Background: The Rhododendron genus ( Rhododendron L.), recognized as the most extensive woody plant genus in the Northern Hemisphere, captivates with its strikingly beautiful corollas and variety of flower colors. In addition, the Rhododendron genus exhibits a complex evolutionary history and substantial species diversification. To comprehensively understand the genomic complexity and flower color diversity within this genus, comparative genomics has emerged as a promising approach, enabling analysis at a super-species level. Results: Here, we collected whole-genome data from seven rhododendrons of different subgenera to investigate the patterns of interspecific genomic and sequence divergence, as well as evolutionary dynamics of gene family related to flower color. We discovered that approximately 50% of Rhododendron genomes are composed of transposable elements (TEs), with over half of them being long terminal repeat retrotransposons (LTR-RTs). TEs significantly associate with genomic differentiation and structural variances within the genus. Additionally, the duplication and loss of genes associated with flower color and their corresponding expression over time are potentially driven by TEs. Conclusion: Our comparative genomic analysis accentuates the critical role of TEs in genome divergence within the Rhododendron genus, highlighting their potential role as a key factor governing speciation and interspecific variability within the genus.