A trained variant classifier, including: numerous processors operating in parallel and coupled to memory; a convolutional neural network running on the numerous processors, trained on at least 50000 training examples for groups of reads spanning candidate variant sites labeled with true variant classifications, using a backpropagation-based gradient update technique that progressively matches outputs of the convolutional neural network with corresponding ground truth labels; wherein each of the training examples used in the training included data for a group of reads, the data for the group of reads including raw read fragments aligned to a reference sequence, each of the raw read fragments including a target base position flanked by or padded to at least 110 bases on each side, the data for each of the bases in the raw read fragments including a corresponding reference base in the reference sequence, a base call accuracy score of reading the base, a strandedness of reading the base, an insertion count of insertion changes adjoining a position of the base, and a deletion flag identifying a deletion from the reference sequence at the position of the base; an inference input module of the convolutional neural network which runs on at least one of the numerous processors, after training, and feeds the group of reads for evaluation of a candidate variant at the target base position; and an inference output module of the convolutional neural network which runs on at least one of the numerous processors and translates output from the convolutional neural network into classification scores for likelihood that each candidate variant at the target base position is a true variant or a false variant. numerous processors operating in parallel and coupled to memory; a convolutional neural network running on the numerous processors, trained on at least 50000 training examples for groups of reads spanning candidate variant sites labeled with true variant classifications, using a backpropagation-based gradient update technique that progressively matches outputs of the convolutional neural network with corresponding ground truth labels; wherein each of the training examples used in the training included data for a group of reads, the data for the group of reads including raw read fragments aligned to a reference sequence, each of the raw read fragments including a target base position flanked by or padded to at least 110 bases on each side, the data for each of the bases in the raw read fragments including a corresponding reference base in the reference sequence, a base call accuracy score of reading the base, a strandedness of reading the base, an insertion count of insertion changes adjoining a position of the base, and a deletion flag identifying a deletion from the reference sequence at the position of the base; a corresponding reference base in the reference sequence, a base call accuracy score of reading the base, a strandedness of reading the base, an insertion count of insertion changes adjoining a position of the base, and a deletion flag identifying a deletion from the reference sequence at the position of the base; an inference input module of the convolutional neural network which runs on at least one of the numerous processors, after training, and feeds the group of reads for evaluation of a candidate variant at the target base position; and an inference output module of the convolutional neural network which runs on at least one of the numerous processors and translates output from the convolutional neural network into classification scores for likelihood that each candidate variant at the target base position is a true variant or a false variant. <EOS>