Year-end Sale % OFF 
Abstract
BackgroundTargeted next-generation sequencing (NGS) is increasingly being adopted in clinical laboratories for genomic diagnostic tests.ResultsWe 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.ConclusionsWe 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.
Highlights
Targeted next-generation sequencing (NGS) is increasingly being adopted in clinical laboratories for genomic diagnostic tests
Eight of them are known to have pathogenic Copy number variant (CNV). We used those pathogenic CNVs as a standard answer set for parameter optimization of DeviCNV
We evaluated how many of the 5-score CNVs confirmed by qPCR could be detected with other methods
Summary
Targeted next-generation sequencing (NGS) is increasingly being adopted in clinical laboratories for genomic diagnostic tests. For NGS-based CNV detection, there are two major approaches: read-depth and paired-ends mapping methods [1,2,3, 23,24,25,26,27,28]. Read-depth based methods detect a CNV by comparing the observed number of mapped reads with the expected number of mapped reads in a genomic interval [29]. The calculation of the expected number of mapped reads in a genomic interval assumes a neutral copy number in that interval. Paired-ends mapping based methods identify a CNV by looking for concordantly mapped paired-ends reads whose insert sizes are deviated significantly from the distribution of insert sizes in a sequencing library [19]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
