A Deep Learning Approach for Detecting Copy Number Variation in Next-Generation Sequencing Data

• Main Authors: Tom Hill, Robert L. Unckless
• Format: Article
• Language: English
• Published: Oxford University Press 2019-11-01
• Series: G3: Genes, Genomes, Genetics
• Subjects: coverage; deletion; duplication; machine-learning; next-generation sequencing
• Online Access: http://g3journal.org/lookup/doi/10.1534/g3.119.400596

Copy number variants (CNV) are associated with phenotypic variation in several species. However, properly detecting changes in copy numbers of sequences remains a difficult problem, especially in lower quality or lower coverage next-generation sequencing data. Here, inspired by recent applications of machine learning in genomics, we describe a method to detect duplications and deletions in short-read sequencing data. In low coverage data, machine learning appears to be more powerful in the detection of CNVs than the gold-standard methods of coverage estimation alone, and of equal power in high coverage data. We also demonstrate how replicating training sets allows a more precise detection of CNVs, even identifying novel CNVs in two genomes previously surveyed thoroughly for CNVs using long read data.