Usage of next gen sequencing data is becoming more prevalent in healthcare, which increases the demand for hospital labs. A key step in analyzing DNA is the interpretation, carried out by a variant analyst, who goes through the sequencing output and relates the relevant features to the ordering physician. The interpretation is a complex process of separating true variants from artifacts, and relating variants to the patient's phenotype. As the tools for detecting single nucleotide variants become more accurate, analysis is being broadened to also include copy number variants (CNVs). However, interpretation of CNVs is difficult in NGS data due to their large size and the error proneness of detection algorithms for this variant type. There is also a lack of software tools for reviewing CNVs that utilize visualizations of the data. This results in the variant interpreter needing to use a number of different tools and This project targets the interpretation step by applying visualization techniques to genomics data, with a particular focus on the challenges that arise in the clinical environment. By leveraging the parallel processing capabilities of the visual system, we aspire to make interpretation more efficient and accurate. To reach this goal we are designing software with visual interfaces that are tailored to reviewing NGS data, specifically looking for CNVs. We will test the usefulness of the designs through user studies, where users review exome sequencing data with confirmed CNVs.