S-Space College of Medicine/School of Medicine (의과대학/대학원) Pediatrics (소아과학전공) Journal Papers (저널논문_소아과학전공)
DeviCNV: detection and visualization of exon-level copy number variants in targeted next-generation sequencing data
Cited 8 time in Web of Science Cited 9 time in Scopus
- Issue Date
- BioMed Central
- BMC Bioinformatics, 19(1):381
- Copy-number variation ; Targeted sequencing ; Visualization ; Germ-line ; Exon-level
Targeted next-generation sequencing (NGS) is increasingly being adopted in clinical laboratories for genomic diagnostic tests.
We developed a new computational method, DeviCNV, intended for the detection of exon-level copy number variants (CNVs) in targeted NGS data. DeviCNV builds linear regression models with bootstrapping for every probe to capture the relationship between read depth of an individual probe and the median of read depth values of all probes in the sample. From the regression models, it estimates the read depth ratio of the observed and predicted read depth with confidence interval for each probe which is applied to a circular binary segmentation (CBS) algorithm to obtain CNV candidates. Then, it assigns confidence scores to those candidates based on the reliability and strength of the CNV signals inferred from the read depth ratios of the probes within them. Finally, it also provides gene-centric plots with confidence levels of CNV candidates for visual inspection. We applied DeviCNV to targeted NGS data generated for newborn screening and demonstrated its ability to detect novel pathogenic CNVs from clinical samples.
We propose a new pragmatic method for detecting CNVs in targeted NGS data with an intuitive visualization and a systematic method to assign confidence scores for candidate CNVs. Since DeviCNV was developed for use in clinical diagnosis, sensitivity is increased by the detection of exon-level CNVs.
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