Deep learning-based variant classifier

A non-transitory computer readable storage medium storing instructions that, when executed by at least one processor, cause a computing system to:

• identify a group of reads aligned with a reference genome and spanning a candidate variant at a target base position;
• provide, to a convolutional neural network, an array of input features generated from a text file comprising sequencing data output by a sequencer instrument, the array of input features encoding:
• bases from the group of reads in the text file at the target base position,
• bases flanking each side of the target base position in the text file, and
• corresponding base features for bases within the group of reads;
• and
• generate, based on an analysis of the array of input features by the convolutional neural network, classification scores indicating likelihoods that the candidate variant at the target base position is a variant.

A computer-implemented method comprising:

• identifying a group of reads aligned with a reference genome and spanning a candidate variant at a target base position;
• providing, to a convolutional neural network, an array of input features generated from a text file comprising sequencing data output by a sequencer instrument, the array of input features encoding:
• bases from the group of reads in the text file at the target base position,
• bases flanking each side of the target base position in the text file, and
• corresponding base features for bases within the group of reads;
• and
• generating, based on an analysis of the array of input features by the convolutional neural network, classification scores indicating likelihoods that the candidate variant at the target base position is a variant.