ArticleBMC bioinformatics2022
Algorithmic improvements for discovery of germline copy number variants in next-generation sequencing data.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- High Concordance of Copy Number Variants Detected by Chromosomal Microarray and Exome Sequencing in Clinical Diagnostics.Clinical genetics · 2026Article
- Detection of germline CNVs from gene panel data: benchmarking the state of the art.Briefings in bioinformatics · 2024Article
- The Role of Genetic Testing in Adult CKD.Journal of the American Society of Nephrology : JASN · 2024Article
- Case Report: Whole exome sequencing identifies compound heterozygous variants in theFrontiers in genetics · 2024Article
- Association of Rare Recurrent Copy Number Variants With Congenital Heart Defects Based on Next-Generation Sequencing Data From Family Trios.Frontiers in genetics · 2019Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCopy number variants (CNVs) play a significant role in human heredity and disease. However, sensitive and specific characterization of germline CNVs from NGS data has remained challenging, particularly for hybridization-capture data in which read counts are the primary source of copy number information.
resultsWe describe two algorithmic adaptations that improve CNV detection accuracy in a Hidden Markov Model (HMM) context. First, we present a method for computing target- and copy number-specific emission distributions. Second, we demonstrate that the Pointwise Maximum a posteriori (PMAP) HMM decoding procedure yields improved sensitivity for small CNV calls compared to the more common Viterbi HMM decoder. We develop a prototype implementation, called Cobalt, and compare it to other CNV detection tools using sets of simulated and previously detected CNVs with sizes spanning a single exon to a full chromosome.
conclusionsIn both the simulation and previously detected CNV studies Cobalt shows similar sensitivity but significantly fewer false positive detections compared to other callers. Overall sensitivity is 80-90% for deletion CNVs spanning 1-4 targets and 90-100% for larger deletion events, while sensitivity is somewhat lower for small duplication CNVs.
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