DeviCNV

Detection and visualization of exon-level copy number variants in targeted next-generation sequencing data

Yeeok Kang, Seong Hyeuk Nam, Kyung Sun Park, Yoonjung Kim, Jong Won Kim, Eunjung Lee, Jung Min Ko, Kyunga Lee, Inho Park

Research output: Contribution to journalArticle

Abstract

Background: Targeted next-generation sequencing (NGS) is increasingly being adopted in clinical laboratories for genomic diagnostic tests. Results: 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. Conclusions: 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.

Original languageEnglish
Article number381
JournalBMC Bioinformatics
Volume19
Issue number1
DOIs
Publication statusPublished - 2018 Oct 16

Fingerprint

Sequencing
Exons
Visualization
Linear Models
Probe
Routine Diagnostic Tests
Clinical laboratories
Confidence Intervals
Confidence
Assign
Computational methods
Linear regression
Screening
Genes
Inspection
Diagnostic Tests
Bootstrapping
Confidence Level
Linear Regression Model
Computational Methods

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

Kang, Yeeok ; Nam, Seong Hyeuk ; Park, Kyung Sun ; Kim, Yoonjung ; Kim, Jong Won ; Lee, Eunjung ; Ko, Jung Min ; Lee, Kyunga ; Park, Inho. / DeviCNV : Detection and visualization of exon-level copy number variants in targeted next-generation sequencing data. In: BMC Bioinformatics. 2018 ; Vol. 19, No. 1.
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DeviCNV : Detection and visualization of exon-level copy number variants in targeted next-generation sequencing data. / Kang, Yeeok; Nam, Seong Hyeuk; Park, Kyung Sun; Kim, Yoonjung; Kim, Jong Won; Lee, Eunjung; Ko, Jung Min; Lee, Kyunga; Park, Inho.

In: BMC Bioinformatics, Vol. 19, No. 1, 381, 16.10.2018.

Research output: Contribution to journalArticle

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