A phased genome of the highly heterozygous 'Texas' almond uncovers patterns of allele-specific expression linked to heterozygous structural variants.

The vast majority of traditional almond varieties are self-incompatible, and the level of variability of the species is very high, resulting in a high-heterozygosity genome. Therefore, information on the different haplotypes is particularly relevant to understand the genetic basis of trait variability in this species. However, although reference genomes for several almond varieties exist, none of them is phased and has genome information at the haplotype level.

Genomic analysis of fruit size and shape traits in apple: unveiling candidate genes through GWAS analysis.

Genomic tools facilitate the efficient selection of improved genetic materials within a breeding program. Here, we focus on two apple fruit quality traits: shape and size. We utilized data from 11 fruit morphology parameters gathered across three years of harvest from 355 genotypes of the apple REFPOP collection, which serves as a representative sample of the genetic variability present in European-cultivated apples.

PERSEUS: an interactive and intuitive web-based tool for pedigree visualization.

Summary: Pedigree-based analyses' prime role is to unravel relationships between individuals in breeding programs and germplasms. This is critical information for decoding the genetics underlying main inherited traits of relevance, and unlocking the genotypic variability of a species to carry out genomic selections and predictions. Despite the great interest, current lineage visualizations become quite limiting in terms of public display, exploration, and tracing of traits up to ancestral donors.

GenoDrawing: An Autoencoder Framework for Image Prediction from SNP Markers.

Advancements in genome sequencing have facilitated whole-genome characterization of numerous plant species, providing an abundance of genotypic data for genomic analysis. Genomic selection and neural networks (NNs), particularly deep learning, have been developed to predict complex traits from dense genotypic data. Autoencoders, an NN model to extract features from images in an unsupervised manner, has proven to be useful for plant phenotyping.

LeTra: a leaf tracking workflow based on convolutional neural networks and intersection over union.

Background: The study of plant photosynthesis is essential for productivity and yield. Thanks to the development of high-throughput phenotyping (HTP) facilities, based on chlorophyll fluorescence imaging, photosynthetic traits can be measured in a reliable, reproducible and efficient manner. In most state-of-the-art HTP platforms, these traits are automatedly analyzed at individual plant level, but information at leaf level is often restricted by the use of manual annotation.