Bioinformatics Strategies, Challenges, and Opportunities for Next Generation Sequencing-Based HLA Genotyping

Abstract

The advent of next generation sequencing (NGS) has altered the face of genotyping the human leukocyte antigen (HLA) system in clinical, stem cell donor registry, and research contexts. NGS has led to a dramatically increased sequencing throughput at high accuracy, while being more time and cost efficient than precursor technologies. This has led to a broader and deeper profiling of the key genes in the human immunogenetic make-up. The rapid evolution of sequencing technologies is evidenced by the development of varied short-read sequencing platforms with differing read lengths and sequencing capacities to long-read sequencing platforms capable of profiling full genes without fragmentation. Concomitantly, there has been development of a diverse set of computational analyses and software tools developed to deal with the various strengths and limitations of the sequencing data generated by the different sequencing platforms. This review surveys the different modalities involved in generating NGS HLA profiling sequence data. It systematically describes various computational approaches that have been developed to achieve HLA genotyping to different degrees of resolution. At each stage, this review enumerates the drawbacks and advantages of each of the platforms and analysis approaches, thus providing a comprehensive picture of the current state of HLA genotyping technologies.

Keywords: Genotyping; Human leukocyte antigen; Next generation sequencing.

Fig. 1

A generalised workflow for NGS-based HLA genotyping. A Sequence data are generated by HLA-specific target enrichment, mostly short- or long-range PCR. Long PCR products are fragmented for sequencing by short-read technologies. B Reads specific to the HLA genes are retrieved by mapping. C Three examples of HLA genotyping algorithms: read mapping to the reference allele repository, as implemented by PHLAT or OptiType; consensus-sequence-based typing, as implemented by HLA-VBSeq; and graph-based typing, as implemented by HLA*PRG-LA or Kourami.