Male Linked Genomic Region Determines Sex in Dioecious Amaranthus palmeri.

Dioecy, the separation of reproductive organs on different individuals, has evolved repeatedly in different plant families. Several evolutionary paths to dioecy have been suggested, but the mechanisms behind sex determination is not well understood. The diploid dioecious Amaranthus palmeri represents a well-suited model system to study sex determination in plants. Despite the agricultural importance of the species, the genetic control and evolutionary state of dioecy in A. palmeri is currently unknown. Early cytogenetic experiments did not identify heteromorphic chromosomes. Here, we used whole-genome sequencing of male and female pools from 2 independent populations to elucidate the genetic control of dioecy in A. palmeri. Read alignment to a close monoecious relative and allele frequency comparisons between male and female pools did not reveal significant sex-linked genes. Consequently, we employed an alignment-free k-mer comparison which enabled us to identify a large number of male-specific k-mers. We assembled male-specific contigs comprising a total of almost 2 Mb sequence, proposing a XY sex-determination system in the species. We were able to identify the potential Y chromosome in the A. palmeri draft genome sequence as 90% of our male-specific sequence aligned to a single scaffold. Based on our findings, we suggest an intermediate evolutionary state of dioecy with a young Y chromosome in A. palmeri. Our findings give insight into the evolution of sex chromosomes in plants and may help to develop sustainable strategies for weed management.

Artificial Intelligence in Epigenetic Studies: Shedding Light on Rare Diseases.

More than 7,000 rare diseases (RDs) exist worldwide, affecting approximately 350 million people, out of which only 5% have treatment. The development of novel genome sequencing techniques has accelerated the discovery and diagnosis in RDs. However, most patients remain undiagnosed. Epigenetics has emerged as a promise for diagnosis and therapies in common disorders (e.g., cancer) with several epimarkers and epidrugs already approved and used in clinical practice. Hence, it may also become an opportunity to uncover new disease mechanisms and therapeutic targets in RDs. In this "big data" age, the amount of information generated, collected, and managed in (bio)medicine is increasing, leading to the need for its rapid and efficient collection, analysis, and characterization. Artificial intelligence (AI), particularly deep learning, is already being successfully applied to analyze genomic information in basic research, diagnosis, and drug discovery and is gaining momentum in the epigenetic field. The application of deep learning to epigenomic studies in RDs could significantly boost discovery and therapy development. This review aims to collect and summarize the application of AI tools in the epigenomic field of RDs. The lower number of studies found, specific for RDs, indicate that this is a field open to expansion, following the results obtained for other more common disorders.