Searching for sequencing signal anomalies associated with genome structural variations

Abstract

Genomic structural variations (SVs) are one of the main sources of genetic diversity. Structural variants as mutagens may have a significant impact on human health and lead to hereditary diseases and cancers. Existing methods of finding structural variants are based on analysis of high-throughput sequencing data and despite significant progress in the development of the detection methods, there is still a need for improving the identification of structural variations with accuracy appropriate for use in a diagnostic procedure. Analysis of the signal of sequencing coverage (i.e., the number of sequencing fragments that aligned to every point of a genome) holds new potential for the design of approaches for structural variations discovery, and can be used as time-series analysis. Here, we present an approach for identification of patterns in the coverage signal. The method has been developed based on algorithms used for analysis of time series data, namely KNN (K-nearest neighbour) search algorithm and the SAX (Symbolic Aggregation Approximation) method. Using the rich dataset encompassing full genomes of 911 individuals with different ethnic backgrounds generated by the Human Genome Diversity Project initiative, we constructed generalized patterns of signal coverage in the vicinity of breakpoints corresponding to various structural variant types. Also, with the benefit of the SAX models of the motifs we developed a software package for fast detection of anomalies in the coverage signal.