This review presents a comprehensive analysis of plant-based proteins from soybeans, pulses, cereals, and pseudo-cereals by examining their structural properties, modification techniques, bioactivities, and applicability in food systems. It addresses the critical need for a proper utilization strategy of proteins from various plant sources amidst the rising environmental footprint of animal protein production. The inherent composition diversity among plant proteins, their nutritional profiles, digestibility, environmental impacts, and consumer acceptance are compared. The innovative modification techniques to enhance the functional properties of plant proteins are also discussed. The review also investigates the bioactive properties of plant proteins, including their antioxidant, antimicrobial, and antitumoral activities, and their role in developing meat analogs, dairy alternatives, baked goods, and 3D-printed foods. It underscores the consideration parameters of using plant proteins as sustainable, nutritious, and functional ingredients and advocates for research to overcome sensory and functional challenges for improved consumer acceptance and marketability.

Background: As an important source of genetic variation, copy number variation (CNV) can alter the dosage of DNA segments, which in turn may affect gene expression level and phenotype. However, our knowledge of CNV in apple is still limited. Here, we obtained high-confidence CNVs and investigated their functional impact based on genome resequencing data of two apple populations, cultivars and wild relatives. Results: In this study, we identified 914,610 CNVs comprising 14,839 CNV regions (CNVRs) from 346 apple accessions, including 289 cultivars and 57 wild relatives. CNVRs summed to 71.19 Mb, accounting for 10.03% of the apple genome. Under the low linkage disequilibrium (LD) with nearby SNPs, they could also accurately reflect the population structure of apple independent of SNPs. Furthermore, A total of 3,621 genes were covered by CNVRs and functionally involved in biological processes such as defense response, reproduction and metabolic processes. In addition, the population differentiation index ( $${V}_{st}$$V st ) analysis between cultivars and wild relatives revealed 127 CN-differentiated genes, which may contribute to trait differences in these two populations. Conclusions: This study was based on identification of CNVs from 346 diverse apple accessions, which to our knowledge was the largest dataset for CNV analysis in apple. Our work presented the first comprehensive CNV map and provided valuable resources for understanding genomic variations in apple.

The recently emerging high-throughput Pore-C (HiPore-C) can identify whole-genome high-order chromatin multi-way interactions with an ultra-high output, contributing to deciphering three-dimensional (3D) genome organization. However, it also brings new challenges to relevant data analysis. To alleviate this problem, we proposed the EpiMCI, a model for multi-way chromatin interaction prediction based on a hypergraph neural network with epigenomic signals as the input. The EpiMCI integrated separate hyperedge representations with coupling hyperedge information and obtained AUCs of 0.981 and 0.984 in the GM12878 and K562 datasets, respectively, which outperformed the current available method. Moreover, the EpiMCI can be applied to denoise the HiPore-C data and improve the data quality efficiently. Furthermore, the vertex embeddings extracted from the EpiMCI reflected the global chromatin architecture accurately. The principal component analysis suggested that it was well aligned with the activities of genomic regions at the chromatin compartment level. Taken together, the EpiMCI can accurately predict multi-way chromatin interactions and can be applied to studies relying on chromatin architecture.