Systems and methods for identification of structural variants based on an autoencoder

A system for structural variation identification using a machine-learning model, the system comprising:

• one or more processing devices;
• one or more computer-readable storage media storing instructions that, when executed by the one or more processing devices, cause the system to perform operations including:
• obtaining an original read depth profile for a candidate structural variant region of a sample obtained from a subject having a candidate structural variant;
• generating a reconstructed read depth profile for the candidate structural variant region of the sample using a machine-learning model;
• calculating a score, wherein the score is calculated based at least in part on differences between the reconstructed read depth profile and the original read depth profile;
• determining whether the score satisfies a score threshold;
• and
• in an instance in which the score satisfies the score threshold, reporting the candidate structural variant as real.

A computer program product for structural variation identification using a machine-learning model, the computer program product comprising at least one non-transitory computer-readable storage medium storing software instructions that, when executed, cause an apparatus to perform operations including:

• obtaining an original read depth profile for a candidate structural variant region of a sample obtained from a subject having a candidate structural variant;
• generating a reconstructed read depth profile for the candidate structural variant region of the sample using the machine-learning model;
• calculating a score, wherein the score is calculated based at least in part on differences between the reconstructed read depth profile and the original read depth profile;
• determining whether the score satisfies a score threshold;
• and
• in an instance in which the score satisfies the score threshold, reporting the candidate structural variant as real.