A system for identifying repeat patterns that cause sequence-specific errors in nucleotide sequencing data, comprising: one or more processors and one or more storage devices storing instructions that, when executed on the one or more processors cause the one or more processors to implement: an input preparation subsystem configured to: computationally overlay repeat patterns under test on numerous nucleotide sequences and produce overlaid samples, wherein each repeat pattern represents a particular nucleotide composition that has a particular length and appears in an overlaid sample at a particular offset position, wherein each overlaid sample has a target position considered to be a variant nucleotide, and wherein for each combination of the particular nucleotide composition, the particular length, and the particular offset position, a set of the overlaid samples is computationally generated; a pre-trained variant filter subsystem configured to: process the overlaid samples through a convolutional neural network and, based on detection of nucleotide patterns in the overlaid samples by convolution filters of the convolutional neural network, generate classification scores for likelihood that the variant nucleotide in each of the overlaid samples is a true variant or a false variant; a repeat pattern output subsystem configured to: output distributions of the classification scores generated by the pre-trained variant filter subsystem for the repeat factors that specify a number of repetitions of a single base (A, C, G, and T) or at least two bases in the repeat patterns; and a sequence-specific error correlation subsystem configured to: specify, based on a threshold, a subset of the classification scores in the distributions as indicative of false variant classifications, and classify those repeat patterns which are associated with the subset of the classification scores that are indicative of the false variant classifications as causing the sequence-specific errors. an input preparation subsystem configured to: computationally overlay repeat patterns under test on numerous nucleotide sequences and produce overlaid samples, wherein each repeat pattern represents a particular nucleotide composition that has a particular length and appears in an overlaid sample at a particular offset position, wherein each overlaid sample has a target position considered to be a variant nucleotide, and wherein for each combination of the particular nucleotide composition, the particular length, and the particular offset position, a set of the overlaid samples is computationally generated; computationally overlay repeat patterns under test on numerous nucleotide sequences and produce overlaid samples, wherein each repeat pattern represents a particular nucleotide composition that has a particular length and appears in an overlaid sample at a particular offset position, wherein each overlaid sample has a target position considered to be a variant nucleotide, and wherein for each combination of the particular nucleotide composition, the particular length, and the particular offset position, a set of the overlaid samples is computationally generated; a pre-trained variant filter subsystem configured to: process the overlaid samples through a convolutional neural network and, based on detection of nucleotide patterns in the overlaid samples by convolution filters of the convolutional neural network, generate classification scores for likelihood that the variant nucleotide in each of the overlaid samples is a true variant or a false variant; a repeat pattern output subsystem configured to: output distributions of the classification scores generated by the pre-trained variant filter subsystem for the repeat factors that specify a number of repetitions of a single base (A, C, G, and T) or at least two bases in the repeat patterns; and a sequence-specific error correlation subsystem configured to: specify, based on a threshold, a subset of the classification scores in the distributions as indicative of false variant classifications, and classify those repeat patterns which are associated with the subset of the classification scores that are indicative of the false variant classifications as causing the sequence-specific errors. specify, based on a threshold, a subset of the classification scores in the distributions as indicative of false variant classifications, and classify those repeat patterns which are associated with the subset of the classification scores that are indicative of the false variant classifications as causing the sequence-specific errors.