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HLA typing from RNA-seq data using hierarchical read weighting [corrected].

Hyunsung John Kim1, Nader Pourmand

  • 1Biomolecular Engineering Department, Baskin School of Engineering, University of California, Santa Cruz, Santa Cruz, California, United States of America. hyjkim@soe.ucsc.edu

Plos One
|July 11, 2013
PubMed
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HLAforest accurately predicts human leukocyte antigen (HLA) haplotypes using high throughput transcriptome sequencing. This method improves upon current techniques for donor-recipient matching in stem cell transplantation.

Area of Science:

  • Genomics and Bioinformatics
  • Immunogenetics
  • Computational Biology

Background:

  • Accurate human leukocyte antigen (HLA) haplotype matching is critical for successful allogeneic hematopoietic stem cell transplantation.
  • Existing HLA typing methods are often limited by targeted testing, and high throughput transcriptome sequencing remains underutilized due to challenges in aligning highly variable HLA sequences.
  • The variability within the HLA region poses a significant hurdle for accurate sequence alignment and haplotyping.

Purpose of the Study:

  • To introduce HLAforest, a novel computational method for accurate HLA haplotyping.
  • To leverage high throughput transcriptome sequencing data for improved HLA haplotype prediction.
  • To address the limitations of current HLA typing methods in handling highly variable genetic regions.

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Main Methods:

  • Developed HLAforest, a method employing hierarchical read weighting and an iterative, greedy, top-down pruning technique.
  • Simulated sequencing data with varying read lengths and error rates to model current technologies.
  • Applied HLAforest to cell line trio data, HapMap samples, and RNA-seq samples with limited HLA coverage.

Main Results:

  • HLAforest achieved >99% accuracy for allele group level (2-digit) and 93% for peptide-level (4-digit) HLA haplotype prediction in simulations.
  • The method demonstrated robustness to sequencing errors, predicting 99% of allele-group level haplotypes with up to 8.8% substitution rates.
  • In real-world data, HLAforest showed high concordance with PCR-based methods, accurately predicting major class I and II genes.

Conclusions:

  • HLAforest accurately predicts HLA haplotypes from transcriptome sequencing data, offering a powerful alternative to current methods.
  • The method is robust to sequencing errors and performs well even with limited sequencing coverage.
  • HLAforest has the potential to enhance donor-recipient matching for hematopoietic stem cell transplantation by improving HLA typing accuracy and throughput.