Computational Approach to Annotating Variants of Unknown Significance in Clinical Next Generation Sequencing.
Next generation sequencing (NGS) has become a common technology in the clinical laboratory, particularly for the analysis of malignant neoplasms. However, most mutations identified by NGS are variants of unknown clinical significance (VOUS). Although the approach to define these variants differs by institution, software algorithms that predict variant effect on protein function may be used. However, these algorithms commonly generate conflicting results, potentially adding uncertainty to interpretation. In this review, we examine several computational tools used to predict whether a variant has clinical significance. In addition to describing the role of these tools in clinical diagnostics, we assess their efficacy in analyzing known pathogenic and benign variants in hematologic malignancies.
Department of Laboratory Medicine, Yale University School of Medicine, West Haven, CT wade.schulz@yale.edu.
Wade L
WL
Schulz
Department of Laboratory Medicine, Yale University School of Medicine, West Haven, CT Pathology and Laboratory Medicine Service, VA Connecticut Healthcare System, West Haven, CT.
Christopher A
CA
Tormey
Department of Laboratory Medicine, Yale University School of Medicine, West Haven, CT.
Richard
R
Torres
D000465:Algorithms; D019295:Computational Biology; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D058977:Molecular Sequence Annotation; D017422:Sequence Analysis, DNA