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Updated: May 20, 2025

Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

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Published on: September 6, 2017

Streaming Long-Read Sequence Alignments for HLA Predictions Using HLAminer.

René L Warren¹, Inanc Birol^1,2

¹BC Cancer Genome Sciences Centre, Vancouver, BC, Canada.

Current Protocols

|March 27, 2025

Summary

This summary is machine-generated.

This study introduces a streamlined protocol for predicting human leukocyte antigen (HLA) alleles from long-read sequencing data. The method efficiently analyzes complex genomic regions, even with lower coverage, improving immune gene analysis.

Keywords:

HLA inferences from sequencing data HLA typing human leukocyte antigen (HLA)long‐read sequencing for HLA prediction next‐generation sequencing‐based HLA analysis

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RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

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Published on: May 19, 2019

Area of Science:

Genomics
Immunogenetics
Bioinformatics

Background:

The human leukocyte antigen (HLA) gene cluster is crucial for immunity and highly polymorphic.
Analyzing HLA regions traditionally requires high-resolution data and complex methods.
Long-read sequencing offers potential for comprehensive HLA analysis.

Purpose of the Study:

To present a streamlined protocol for predicting HLA alleles from long-read sequencing data.
To demonstrate the efficiency and robustness of the HLAminer tool for HLA signature prediction.
To enable accessible HLA typing from whole-genome shotgun data.

Main Methods:

Directly streaming sequence alignments into HLAminer.
Utilizing long-read sequencing data from Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio).
Compatibility with any SAM file format-compatible read aligner, including minimap2.

Main Results:

The protocol successfully predicts HLA class I and class II alleles.
Robust HLA signature predictions were achieved even with older, lower-accuracy nanopore data.
Effective analysis was demonstrated with as low as 10× sequencing coverage.

Conclusions:

The presented method provides a simple, scalable, and efficient approach for HLA prediction from long-read sequencing data.
This protocol democratizes HLA typing by leveraging readily available whole-genome shotgun data.
The HLAminer tool facilitates accurate immune gene analysis across various sequencing datasets.