title: A statistical approach to detection of copy number variations in PCR-enriched targeted sequencing data.
abstract: BACKGROUND
  Multiplex polymerase chain reaction (PCR) is a common enrichment technique for targeted massive parallel sequencing (MPS) protocols. MPS is widely used in biomedical research and clinical diagnostics as the fast and accurate tool for the detection of short genetic variations. However, identification of larger variations such as structure variants and copy number variations (CNV) is still being a challenge for targeted MPS. Some approaches and tools for structural variants detection were proposed, but they have limitations and often require datasets of certain type, size and expected number of amplicons affected by CNVs. In the paper, we describe novel algorithm for high-resolution germinal CNV detection in the PCR-enriched targeted sequencing data and present accompanying tool.
  
  
  RESULTS
  We have developed a machine learning algorithm for the detection of large duplications and deletions in the targeted sequencing data generated with PCR-based enrichment step. We have performed verification studies and established the algorithm's sensitivity and specificity. We have compared developed tool with other available methods applicable for the described data and revealed its higher performance.
  
  
  CONCLUSIONS
  We showed that our method has high specificity and sensitivity for high-resolution copy number detection in targeted sequencing data using large cohort of samples.
authors: Parseq Lab, Birzhevaya, 16, Saint-Petersburg, 199053, Russia.|Department of Mathematics and Information Technology in SPbAU RAS, Khlopina, 8/3, Saint-Petersburg, 194021, Russia.|Genomic and Epigenomic Variation in Disease Group, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain.|Universitat Pompeu Fabra (UPF), Barcelona, Spain.
authors: German
authors: G
authors: Demidov
authors: Parseq Lab, Birzhevaya, 16, Saint-Petersburg, 199053, Russia.
authors: Tamara
authors: T
authors: Simakova
authors: Parseq Lab, Birzhevaya, 16, Saint-Petersburg, 199053, Russia.
authors: Julia
authors: J
authors: Vnuchkova
authors: Parseq Lab, Birzhevaya, 16, Saint-Petersburg, 199053, Russia. abragin@parseq.pro.
authors: Anton
authors: http://orcid.org/0000-0002-4138-2354
authors: A
authors: Bragin
mesh_terms: D000465:Algorithms; D056915:DNA Copy Number Variations / Q000235:genetics*; D003627:Data Interpretation, Statistical; D014644:Genetic Variation / Q000235:genetics*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D006801:Humans; D016133:Polymerase Chain Reaction / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods
keywords: Germline CNV; MPS; Machine learning; Multiplex PCR; Targeted amplification