Systems and Methods for Correcting for Noise and Systemic Variations in Sequencing Data

A computer-implemented method for determining a genotype of a repeat rich gene, the method implemented using a system including a computing device including a processor communicatively coupled to memory device, the method comprising:

• receiving an input set of genetic sequencing data;
• measuring at least one repeat structure of at least one sequence read of the genetic sequencing data and remove sequence reads with abnormal repeat structures to generate a first dataset, the first dataset excluding sequence reads with sequence artifacts;
• filtering sequence reads of the first dataset to generate a second dataset, the second dataset excluding sequences with structural artifacts;
• applying input data to a model to generate a third dataset, the input data comprising sequence reads of the second dataset, and the third dataset including sequencing reads categorized by allele;
• generating a consensus sequence for each allele in the third dataset to generate a fourth dataset of consensus reads for all alleles in the third dataset;
• translating each allele of the fourth dataset to generate a fifth dataset, the fifth dataset including a corresponding amino-acid sequence of each allele;
• and
• determining a protein-altering variant status based on the amino-acid sequences of the fifth dataset.

At least one non-transitory computer-readable storage medium having computer-executable instructions embodied thereon, wherein when executed by at least one processor, the computer-executable instructions cause the at least one processor to:

• receive an input set of genetic sequencing data;
• measure at least one repeat structure of at least one sequence read of the genetic sequencing data and remove sequence reads with abnormal repeat structures to generate a first dataset, the first dataset excluding sequence reads with sequence artifacts;
• filter sequence reads of the first dataset to generate a second dataset, the second dataset excluding sequences with structural artifacts;
• apply input data to a model to generate a third dataset, the input data comprising sequence reads of the second dataset, and the third dataset including sequencing reads categorized by allele;
• generate a consensus sequence for each allele in the third dataset to generate a fourth dataset of consensus reads for all alleles in the third dataset;
• translate each allele of the fourth dataset to generate a fifth dataset, the fifth dataset including a corresponding amino-acid sequence of each allele;
• and
• determine a protein-altering variant status based on the amino-acid sequences of the fifth dataset.