Abstract
Whole-exome sequencing is an attractive alternative to microarray analysis because of the low cost and potential ability to detect copy number variations (CNV) of various sizes (from 1–2 exons to several Mb). Previous comparison of the most popular CNV calling tools showed a high portion of false-positive calls. Moreover, due to a lack of a gold standard CNV set, the results are limited and incomparable. Here, we aimed to perform a comprehensive analysis of tools capable of germline CNV calling available at the moment using a single CNV standard and reference sample set. Compiling variants from previous studies with Bayesian estimation approach, we constructed an internal standard for NA12878 sample (pilot National Institute of Standards and Technology Reference Material) including 110,050 CNV or non-CNV exons. The standard was used to evaluate the performance of 16 germline CNV calling tools on the NA12878 sample and 10 correlated exomes as a reference set with respect to length distribution, concordance, and efficiency. Each algorithm had a certain range of detected lengths and showed low concordance with other tools. Most tools are focused on detection of a limited number of CNVs one to seven exons long with a false-positive rate below 50%. EXCAVATOR2, exomeCopy, and FishingCNV focused on detection of a wide range of variations but showed low precision. Upon unified comparison, the tools were not equivalent. The analysis performed allows choosing algorithms or ensembles of algorithms most suitable for a specific goal, e.g. population studies or medical genetics.
Highlights
Copy number variations (CNVs) are variations of the number of copies of a DNA fragment in a population
To perform a unified comparative analysis: (1) we chose NA12878 as one of the most characterized samples of the Genome in a Bottle project; (2) we used exon as a minimal unit for comparison, (3) we constructed the set of CNV and non-CNV exons based on available CNV sets for the NA12878 using Bayes model, and (4) we evaluated the performances of 16 existing germline CNV tools (Table 1) using the same reference set
CNV is an important type of structural variation, accurate detection and interpretation of which are essential for both population studies, medical genetics, evolution, and cancer research
Summary
Copy number variations (CNVs) are variations of the number of copies of a DNA fragment in a population. WES has many features that impede accurate CNV detection These include basic features (like capture step) and those originating from the PCR stages (problems with sequencing low complexity regions, dependence on GC content), directly affecting the over- and underrepresentation of target regions, which can be mistakenly interpreted as CNVs. Multiple tools have been elaborated to detect CNVs in exome data; they mainly use the read depth-based strategy, in which the number of reads (read count, RC) mapped onto a fragment of interest is being e valuated[9,10]. Multiple tools have been elaborated to detect CNVs in exome data; they mainly use the read depth-based strategy, in which the number of reads (read count, RC) mapped onto a fragment of interest is being e valuated[9,10] These tools vary greatly at every step of the analysis, including read-depth distribution assumption, RC data normalization, and segmentation approach (Table 1). Base-level log-ratios, GC-content, library-size correction, calling region significant based on normal distribution, CBS for large variation
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