NGSMHC: A simple bioinformatics tool for comprehensively typing MHC genes in non-human species using next-generation sequencing data

Kang, Mingue; Ahn, Byeongyong; Shin, Jae Yeol; Lee, Jongan; Cho, Eun Seok; Park, Chankyu

doi:.0.0.0

Article

https://doi.org/10.5713/ab.25.0468 [Accepted] Published online September 30, 2025.

NGSMHC: A simple bioinformatics tool for comprehensively typing MHC genes in non-human species using next-generation sequencing data

Mingue Kang¹

, Chankyu Park^1,*

¹Department of Stem Cell and Regenerative Biotechnology, Konkuk University, Seoul, Korea
²Animal Genome & Bioinformatics, National Institute of Animal Science, Rural Development Administration, Wanju 55365, Korea
³Swine Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000, Korea

Correspondence: Chankyu Park, Tel: +82-2-450-3697, Email: chankyu@konkuk.ac.kr

Received: 24 June 2025 • Revised: 26 August 2025 • Accepted: 23 September 2025

Abstract

Objective
Understanding the individual- and population-level polymorphisms of major histocompatibility complex (MHC) genes is crucial for identifying associations between MHC variations and immune phenotypes. To support this, we developed NGSMHC, a streamlined bioinformatics tool for efficient and accurate MHC genotyping using next-generation sequencing (NGS) data in non-human species.
Methods
NGSMHC constructs phased haplotype contigs of selected MHC genes from BAM-format mapping data and determines the best matching MHC alleles and genotypes via nucleotide BLAST analysis against a user-provided reference set of MHC alleles. We evaluated NGSMHC using short-read whole-genome sequencing (WGS) data from 12 pigs, focusing on swine leukocyte antigen (SLA) genes. The typing results from NGSMHC were compared to those obtained using polymerase chain reaction sequence-based typing (PCR-SBT). In addition, we tested NGSMHC on a publicly available long-read WGS dataset with known SLA genotypes.
Results
The short-read WGS data showed an average read depth of 20.9× across the SLA region, enabling typing of SLA-2, SLA-3, SLA-DRB1, and SLA-DQB1 using NGSMHC. The concordance rates between NGSMHC and PCR-SBT were 88% for SLA-3, 92% for SLA-DRB1, and 100% for SLA-DQB1. However, SLA-2 typing showed lower concordance (58%), likely due to its high sequence similarity with other SLA class I genes and complex intra-locus polymorphisms. In contrast, NGSMHC accurately identified all tested SLA genotypes—including SLA-1, SLA-2, SLA-3, SLA-DRA, SLA-DRB1, SLA-DQA, and SLA-DQB1—when applied to the long-read WGS data.
Conclusion
NGSMHC is a simple and effective tool for MHC genotyping using NGS data, particularly for non-human species. Its accuracy is significantly improved by long-read sequencing, underscoring the importance of read length in precise MHC allele determination.

Keywords: Allele typing; Major Histocompatibility Complex; NGS