HLA Haplotyping from RNA-seq Data Using Hierarchical Read Weighting

Abstract

Correctly matching the HLA haplotypes of donor and recipient is essential to the success of allogenic hematopoietic stem cell transplantation. Current HLA typing methods rely on targeted testing of recognized antigens or sequences. Despite advances in Next Generation Sequencing, general high throughput transcriptome sequencing is currently underutilized for HLA haplotyping due to the central difficulty in aligning sequences within this highly variable region. Here we present the method, HLAforest, that can accurately predict HLA haplotype by hierarchically weighting reads and using an iterative, greedy, top down pruning technique. HLAforest correctly predicts >99% of allele group level (2 digit) haplotypes and 93% of peptide-level (4 digit) haplotypes of the most diverse HLA genes in simulations with read lengths and error rates modeling currently available sequencing technology. The method is very robust to sequencing error and can predict 99% of allele-group level haplotypes with substitution rates as high as 8.8%. When applied to data generated from a trio of cell lines, HLAforest corroborated PCR-based HLA haplotyping methods and accurately predicted 16/18 (89%) major class I genes for a daughter–father-mother trio at the peptide level. Major class II genes were predicted with 100% concordance between the daughter–father-mother trio. In fifty HapMap samples with paired end reads just 37 nucleotides long, HLAforest predicted 96.5% of allele group level HLA haplotypes correctly and 83% of peptide level haplotypes correctly. In sixteen RNAseq samples with limited coverage across HLA genes, HLAforest predicted 97.7% of allele group level haplotypes and 85% of peptide level haplotypes correctly.

Expression estimation and eQTL mapping for HLA genes with a personalized pipeline

“…The concordance was higher than 97% for all of the 5 HLA genes compared (Table S1). This is consistent with previous results showing that RNA-seq provides reliable HLA alleles calls [30,37,38,39,40].…”

Immunoregulatory functions of VISTA

“…As such, there have now been several tools developed to aid in the identification of neoantigens from individual tumor DNA sequencing data. These tools typically perform one of three steps necessary in neoantigen identification: somatic mutation identification, [110][111][112][113][114][115][116] HLA typing, [117][118][119][120][121][122] and neoantigen-MHC binding affinity prediction. 123 In addition, some pipelines have been created such as, pVAC-Seq, to integrate several of these steps together while also accounting for the coverage and expression information that may confound sequencing based studies.…”

Computational genomics tools for dissecting tumour–immune cell interactions

“…With respect to standard algorithms for read mapping, methods for HLA typing must extract reads that originate from the HLA locus and assign them to the correct gene and allele. For this purpose, all algorithms rely on well-annotated reference sequences derived from the IMGT/HLA database and either implement a sensitive mapping strategy [57][58][59][60][61][62][63] or combine read mapping and assembly 57,64,65 .…”