Scalable detection of technically challenging variants through modified next-generation sequencing

The spectrum of movement disorders in young children with ARX-related epilepsy-dyskinesia syndrome

Children with developmental and epileptic encephalopathies often present with co-occurring dyskinesias. Pathogenic variants in ARX cause a pleomorphic syndrome that includes infantile epilepsy with a variety of movement disorders ranging from focal hand dystonia to generalized dystonia with frequent status dystonicus. In this report, we present three patients with severe movement disorders as part of ARX-associated epilepsy-dyskinesia syndrome, including a patient with a novel pathogenic missense variant (p.R371G). These cases illustrate diagnostic and management challenges of ARX-related disorder and shed light on broader challenges concerning epilepsy-dyskinesia syndromes.

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.