A Copy Number Variation Detection Method Based on OCSVM Algorithm Using Multi Strategies Integration

Copy number variation (CNV) is an important part of human genetic variations, which is associated with various kinds of diseases. To tackle the limitations of traditional CNV detection methods, such as restricted detection types, high error rates, and challenges in precisely identifying the location of variant breakpoints, a new method called MSCNV (copy number variations detection method for multi-strategies integration based on a one-class support vector machine model) is proposed. MSCNV establishes a multi-signal channel that integrates three strategies: read depth, split read, and read pair. First, a one-class support vector machine algorithm is used to detect abnormal signals in read depth and mapping quality values to determine the rough CNV region. Then, the rough CNV region is filtered by using paired read signals to improve the precision of MSCNV method. Finally, MSCNV explores and recognizes tandem duplication regions, interspersed duplication regions, and loss regions. It uses split read signals to determine the precise location of mutation points and to determine the type of variation. Compared with Manta, FREEC, GROM-RD, Rsicnv, and CNVkit, MSCNV significantly improves the sensitivity, precision, F1-score, and overlap density score of CNV detection while reducing the boundary bias of the detection results.