{"title":"NGSMHC:一个简单的生物信息学工具,利用下一代测序数据全面分型非人类物种的MHC基因。","authors":"Mingue Kang, Byeongyong Ahn, Jae Yeol Shin, Jongan Lee, Eun Seok Cho, Chankyu Park","doi":"10.5713/ab.25.0468","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":7825,"journal":{"name":"Animal Bioscience","volume":" ","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"NGSMHC: A simple bioinformatics tool for comprehensively typing MHC genes in non-human species using next-generation sequencing data.\",\"authors\":\"Mingue Kang, Byeongyong Ahn, Jae Yeol Shin, Jongan Lee, Eun Seok Cho, Chankyu Park\",\"doi\":\"10.5713/ab.25.0468\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>\",\"PeriodicalId\":7825,\"journal\":{\"name\":\"Animal Bioscience\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2025-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Animal Bioscience\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.5713/ab.25.0468\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, DAIRY & ANIMAL SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Animal Bioscience","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.5713/ab.25.0468","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, DAIRY & ANIMAL SCIENCE","Score":null,"Total":0}
NGSMHC: A simple bioinformatics tool for comprehensively typing MHC genes in non-human species using next-generation sequencing data.
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.