Model-independent genome structure variation detection system and method

Abstract

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.

Description

technical field [0001] The invention belongs to the technical field of precision medicine, and relates to a model-independent genome structure variation detection system and method. Background technique [0002] No two people in the world have the exact same genome sequence. Even the genomes of identical twins show acquired differences in genetic material during developmental differentiation. For most diseases, whether people get sick is often related to the disease susceptibility gene variants they carry. Therefore, understanding individual genome variation can help people grasp the risk of disease, which is the key to realizing precision medicine. In recent years, genome sequencing technology has developed rapidly. More and more scientific research institutions, hospitals, and disease diagnosis service companies around the world have carried out gene sequencing based on large samples of global multi-ethnic populations, a variety of common tumors and genetic diseases, wit...

AI Technical Summary

Problems solved by technology

However, this "model first, then calculate" variant detection method design theory can no longer meet the needs of future scientific research, hospitals and genetic testing service providers for variant detection

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