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Next‐generation sequencing (NGS) facilitates comprehensive molecular analyses that help with diagnosing unsolved disorders. In addition to detecting single‐nucleotide variations and small insertions/deletions, bioinformatics tools can identify copy number variations (CNVs) in NGS data, which improves the diagnostic yield. However, due to the possibility of false positives, subsequent confirmation tests are generally performed. Here, we introduce Copy‐number Analysis by BAse‐level NormAlization (CABANA), a visualization tool that allows users to intuitively identify candidate CNVs using the normalized single‐base‐level read depth calculated from NGS data. To demonstrate how CABANA works, NGS data were obtained from 474 patients with neuromuscular disorders. CNVs were screened using a conventional bioinformatics tool, ExomeDepth, and then we normalized and visualized those data at the single‐base level using CABANA, followed by manual inspection by geneticists to filter out false positives and determine candidate CNVs. In doing so, we identified 31 candidate CNVs (7%) in 474 patients and subsequently confirmed all of them to be true using multiplex ligation‐dependent probe amplification. The performance of CABANA was deemed acceptable by comparing its diagnostic yield with previous data about neuromuscular disorders. Despite some limitations, we expect CABANA to help researchers accurately identify CNVs and reduce the need for subsequent confirmation testing. [ABSTRACT FROM AUTHOR]

Purpose: We designed and evaluated the clinical performance of a plasma circulating tumor DNA (ctDNA) panel of 112 genes in various subtypes of lymphoma., Materials and Methods: Targeted deep sequencing with an error-corrected algorithm was performed in ctDNA from plasma samples that were collected before treatment in 42 lymphoma patients. Blood buffy coat was utilized as a germline control. We evaluated the targeted gene panel using mutation detection concordance on the plasma samples with matched tissue samples analyzed the mutation profiles of the ctDNA., Results: Next-generation sequencing analysis using matched tissue samples was available for 18 of the 42 patients. At least one mutation was detected in the majority of matched tissue biopsy samples (88.9%) and plasma samples (83.3%). A considerable number of mutations (40.4%) that were detected in the tissue samples were also found in the matched plasma samples. Majority of patients (21/42) were diffuse large B cell lymphoma patients. The overall detection rate of ctDNA in patients was 85.7% (36/42). The frequently mutated genes included PIM1, TET2, BCL2, KMT2D, KLHL6, HIST1H1E, and IRF8. A cutoff concentration (4,506 pg/mL) of ctDNA provided 88.9% sensitivity and 82.1% specificity to predict ctDNA mutation detection. The ctDNA concentration correlated with elevated lactate dehydrogenase level and the disease stage., Conclusion: Our design panel can detect many actionable gene mutations, including those at low frequency. Therefore, liquid biopsy can be applied clinically in the evaluation of lymphoma patients, especially in aggressive lymphoma patients.