title: Comparison of sequencing based CNV discovery methods using monozygotic twin quartets.
abstract: BACKGROUND
  The advent of high throughput sequencing methods breeds an important amount of technical challenges. Among those is the one raised by the discovery of copy-number variations (CNVs) using whole-genome sequencing data. CNVs are genomic structural variations defined as a variation in the number of copies of a large genomic fragment, usually more than one kilobase. Here, we aim to compare different CNV calling methods in order to assess their ability to consistently identify CNVs by comparison of the calls in 9 quartets of identical twin pairs. The use of monozygotic twins provides a means of estimating the error rate of each algorithm by observing CNVs that are inconsistently called when considering the rules of Mendelian inheritance and the assumption of an identical genome between twins. The similarity between the calls from the different tools and the advantage of combining call sets were also considered.
  
  
  RESULTS
  ERDS and CNVnator obtained the best performance when considering the inherited CNV rate with a mean of 0.74 and 0.70, respectively. Venn diagrams were generated to show the agreement between the different algorithms, before and after filtering out familial inconsistencies. This filtering revealed a high number of false positives for CNVer and Breakdancer. A low overall agreement between the methods suggested a high complementarity of the different tools when calling CNVs. The breakpoint sensitivity analysis indicated that CNVnator and ERDS achieved better resolution of CNV borders than the other tools. The highest inherited CNV rate was achieved through the intersection of these two tools (81%).
  
  
  CONCLUSIONS
  This study showed that ERDS and CNVnator provide good performance on whole genome sequencing data with respect to CNV consistency across families, CNV breakpoint resolution and CNV call specificity. The intersection of the calls from the two tools would be valuable for CNV genotyping pipelines.
authors: Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada.
authors: Marc-André
authors: MA
authors: Legault
authors: Department of Human Genetics, McGill University, Montreal, Quebec, Canada.
authors: Simon
authors: S
authors: Girard
authors: Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada; Beaulieu-Saucier Pharmacogenomics Center, Montreal Heart, Institute Research Center, Montreal, Quebec, Canada.
authors: Louis-Philippe
authors: LP
authors: Lemieux Perreault
authors: Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
authors: Guy A
authors: GA
authors: Rouleau
authors: Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada; Beaulieu-Saucier Pharmacogenomics Center, Montreal Heart, Institute Research Center, Montreal, Quebec, Canada.
authors: Marie-Pierre
authors: MP
authors: Dubé
mesh_terms: D000465:Algorithms; D056905:Chromosome Breakpoints; D002874:Chromosome Mapping / Q000379:methods; D019295:Computational Biology / Q000379:methods; D056915:DNA Copy Number Variations; D023281:Genomics / Q000379:methods*; D006801:Humans; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software; D014430:Twins, Monozygotic / Q000235:genetics*