SYSTEMS AND METHODS FOR CLASSIFYING, PRIORITIZING AND INTERPRETING GENETIC VARIANTS AND THERAPIES USING A DEEP NEURAL NETWORK

摘要

Described herein are systems and methods that receive as input a DNA or RNA sequence, extract features, and apply layers of processing units to compute one ore more condition-specific cell variables, corresponding to cellular quantities measured under different conditions. The system may be applied to a sequence containing a genetic variant, and also to a corresponding reference sequence to determine how much the condition-specific cell variables change because of the variant. The change in the condition-specific cell variables are used to compute a score for how deleterious a variant is, to classify a variant's level of deleteriousness, to prioritize variants for subsequent processing, and to compare a test variant to variants of known deleteriousness. By modifying the variant or the extracted features so as to incorporate the effects of DNA editing, oligonucleotide therapy, DNA- or RNA-binding protein therapy or other therapies, the system may be used to determine if the deleterious effects of the original variant can be reduced.

主权项

1. A method for computing variant-induced changes in one or more condition-specific cell variables for one or more variants, comprising:
a. computing a set of variant features from a DNA or RNA variant sequence; b. applying a deep neural network of at least two layers of processing units to the variant features to compute one or more condition-specific variant cell variables; c. computing a set of reference features from a DNA or RNA reference sequence; d. applying the deep neural network to the reference features to compute one or more condition-specific reference cell variables; e. computing a set of variant-induced changes in the one or more condition-specific cell variables by comparing the one or more condition-specific reference cell variables to the one or more condition-specific variant cell variables.