Filtering Noise in RNAseq Data of the HLA Genes

The HLA, Human Leukocyte Antigens, are encoded by a polymorphic set of genes where even a single base change can impact the function of the body’s immune response to foreign antigens [1]. Although many methods exist to type these alleles using whole-genome sequencing (WGS), few can use RNA sequencing (RNA-seq) to show the functional expression of the alleles with its inconsistency in coverage, and none of these allow for novel allele discovery. We present an approach using partially ordered graphs to project sequenced data onto the known alleles allowing for accurate and efficient typing of the HLA genes with flexibility for discovering new alleles and tolerance for poor sequence quality. This graph-guided approach to assembling and typing the HLA genes from RNA-seq has applications throughout precision medicine, facilitating the prevention and treatment of autoimmune diseases where allele expression can change. It is also a necessary step for determining donors for organ transplants with the least likelihood of rejection. This novel approach of combining database matching with partially ordered graphs for assembling genetic sequences of RNA-seq data could be applied towards typing other alleles.