Although NGS technology is increasingly available in clinical settings, the main challenge remains the interpretation of sequencing data, especially when it comes to larger data sets, such as exomes and genomes. The MedSeq Project examined the effort needed to reanalyze genomes 6–23 months after the initial analysis and how often new findings were revealed . Over the course of the MedSeq study, 14 variants were reclassified and, upon reanalysis, 18 new variants met criteria for reporting. According to Yska et al , the percentage of patients who were genetically diagnosed for primary immunodeficiencies using NGS and array-based methodologies was as low as 15%. These findings highlight the need for periodic reinterpretation and reanalysis of sequencing data for both diagnostic indications and secondary findings.
Next-generation Sequencing (NGS) is becoming increasingly more adopted by the clinical community as a primary tool for diagnostics and monitoring of many diseases, uncovering millions of variants previously unknown. However, the sheer quantity of NGS data presents challenges, especially in the interpretation of the clinical significance of genetic variation, and as such may have serious implications for treatment decisions and further medical outcomes. Thus, innovative analytical approaches are critical for scaling up the adoption and diagnostic yield of NGS-based methodology in clinical settings.
As a result of little standardization, large amount of new scientific findings generated almost every day and an explosion of sequencing data for various purposes, the landscape of human genomics is quite fragmented, siloed, and inconsistent. We all know how frustrating the process of assessing a comprehensive information for genomic variant can be. The way forward is data integration, harmonization and cross-referencing.