Facilitating NGS-Based Screening of Genetic Disorders Using -AI-Driven Bioinformatics

Next-Generation Sequencing (NGS) is used as a diagnostic strategy for identifying pathogenic genetic variants in children and adults. However, the analysis is complex, requiring specialized bioinformaticians, and it can take weeks to finalize one study. This has been a limiting factor for the application of NGS in the screening of populations for rare genetic diseases. In this work, we show two case studies, where we applied an AI-driven bioinformatics framework in a diagnostic and a preventive scenario, respectively. The AI analysis was accurate and substantially faster than using conventional bioinformatics tools. Our results support the concept that AI-driven bioinformatics is a scalable solution for rendering accurate results and enabling a more widely available genetic screening for rare diseases

Heavy metals contents on beach sediments north and south of Sohar industrial area, Oman.

total of 63 surface sediment samples from three sites: Harmul north of Sohar industrial area, Majees south of Sohar industrial area and Zafaran nine kilometers south of Majees were collected in November 2005. They were analyzed for 11 heavy metals including: Vanadium (V), chromium (Cr), manganese (Mn), nickel (Ni), copper (Cu), selenium (Se), mercury (Hg), cadmium (Cd), lead (Pb), zinc (Zn) and arsenic (As) using microwave digestion followed by Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). The concentrations of chromium, manganese and vanadium were higher in Harmul than Majees and Zafaran. Arsenic concentration was generally low except in some stations. The results revealed that heavy metals concentrations in these sites are not alarming