DEEP LEARNING-BASED FRAMEWORK FOR IDENTIFYING SEQUENCE PATTERNS THAT CAUSE SEQUENCE-SPECIFIC ERRORS (SSEs)

摘要

The technology disclosed presents a deep learning-based framework, DeepPOLY, which identifies sequence patterns that cause sequence-specific errors (SSEs). DeepPOLY trains a variant filter on large-scale variant data to learn causal dependencies between sequence patterns and false variant calls. The variant filter has a hierarchical structure built on deep neural networks such as convolutional neural networks and fully-connected neural networks. DeepPOLY implements a simulation that uses the variant filter to test known sequence patterns for their effect on variant filtering. The premise of the simulation is as follows: when a pair of a repeat pattern under test and a called variant is fed to the variant filter as part of a simulated input sequence and the variant filter classifies the called variant as a false variant call, then the repeat pattern is considered to have caused the false variant call and identified as SSE-causing.