ArticleMolecular diagnosis & therapy2017
Evaluating the Calling Performance of a Rare Disease NGS Panel for Single Nucleotide and Copy Number Variants.
Article in Molecular diagnosis & therapy, 2017. 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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5 citing papers in PubMed.
- Pangenomes aid accurate detection of large insertions and deletions from targeted sequencing: the case of cardiomyopathies.Genome medicine · 2026Article
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- Rapid Identification of Pathogenic Variants in Two Cases of Charcot-Marie-Tooth Disease by Gene-Panel Sequencing.International journal of molecular sciences · 2017Article
- Genetic-molecular characterization in the diagnosis of primary immunodeficiencies.Jornal de pediatriaReview
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11 authors.
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Abstract
introductionVariant detection protocols for clinical next-generation sequencing (NGS) need application-specific optimization. Our aim was to analyze the performance of single nucleotide variant (SNV) and copy number (CNV) detection programs on an NGS panel for a rare disease.
methodsThirty genes were sequenced in 83 patients with hereditary spastic paraplegia. The variant calls obtained with LifeScope, GATK UnifiedGenotyper and GATK HaplotypeCaller were compared with Sanger sequencing. The calling efficiency was evaluated for 187 (56 unique) SNVs and indels. Five multiexon deletions detected by multiple ligation probe assay were assessed from the NGS panel data with ExomeDepth, panelcn.MOPS and CNVPanelizer software.
resultsThere were 48/51 (94%) SNVs and 1/5 (20%) indels consistently detected by all the calling algorithms. Two SNVs were not detected by any of the callers because of a rare reference allele, and one SNV in a low coverage region was only detected by two algorithms. Regarding CNVs, ExomeDepth detected 5/5 multi-exon deletions, panelcn.MOPs 4/5 and only 3/5 deletions were accurately detected by CNVPanelizer.
conclusionsThe calling efficiency of NGS algorithms for SNVs is influenced by variant type and coverage. NGS protocols need to account for the presence of rare variants in the reference sequence as well as for ambiguities in indel calling. CNV detection algorithms can be used to identify large deletions from NGS panel data for diagnostic applications; however, sensitivity depends on coverage, selection of the reference set and deletion size. We recommend the incorporation of several variant callers in the NGS pipeline to maximize variant detection efficiency.
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