The invention provides a model-independent
genome structure variation detection
system and method, wherein a model-independent structure variation detection theory is used as a core, and structure variation detection without depending on any variation model is achieved through a variation
signal extraction module, a frequent maximum subgraph mining module and a classification module. According tothe
system, a frequent variation pattern mining module is used for capturing the characteristics of
structural variation left on a
genome, and judging a potential
structural variation region only by mining abnormal points in a large amount of normal data; and according to different
genome disturbance
modes of different variation types, different arrangement sequences of variation signals are further caused, and the different variation types are classified on the basis of the different arrangement sequences in combination with a
deep learning model with a memory function. According to the invention, the
system does not depend on any variation model, so that the variation detection sensitivity and error rate are greatly reduced; and the system is suitable for detection of complex variation types, and an additional
structural variation model does not need to be established.