Impact of AI on Next-Generation Sequencing (NGS) Workflows
Next-Generation Sequencing (NGS) has revolutionized genomics by making sequencing faster and more affordable. However, the sheer scale of data produced requires advanced tools to analyze and interpret it effectively. The Artificial Intelligence in Genomics Market highlights how AI is transforming NGS workflows, making them more efficient, accurate, and clinically relevant.
AI enhances every stage of the NGS process, from raw data processing to variant interpretation. Deep learning algorithms are particularly valuable for improving base calling accuracy, ensuring that sequencing reads are correctly translated into genetic information. By reducing sequencing errors, AI improves the reliability of results.
In addition, AI automates variant annotation, classification, and prioritization. This streamlines workflows and significantly reduces the time required to move from sequencing to actionable insights. Clinical labs benefit from faster turnaround times, which are critical in areas like oncology, prenatal testing, and infectious disease surveillance.
AI-driven NGS workflows are also essential for population-scale genomic projects. National genomic databases generate petabytes of data that must be managed and analyzed efficiently. AI makes it possible to process this information at scale, uncovering population-level trends and supporting public health initiatives.
As sequencing technologies advance, AI will continue to optimize NGS by improving speed, scalability, and cost-efficiency. This synergy is likely to unlock new frontiers in genomics research and clinical applications.