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FuFiHLA: A tool for Full-Field HLA typing from long reads data
Jingqing Hu1, Qian Qin1,2,3, Heng Li1,2,4
1Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02115, USA.
Biorxiv : the Preprint Server for Biology
|November 24, 2025
Summary
FuFiHLA software accurately types Human Leukocyte Antigen (HLA) alleles using long reads, achieving 99.57% accuracy. This advances HLA typing beyond peptide-level analysis for better variant understanding.
Area of Science:
- Genomics
- Immunogenetics
Background:
- Human Leukocyte Antigen (HLA) allele typing is crucial for clinical applications.
- Short-read sequencing limits HLA typing to the peptide level, potentially missing non-coding variant effects.
Purpose of the Study:
- To develop an accurate HLA typing method directly from long reads.
- To overcome limitations of short-read sequencing in HLA allele identification.
Main Methods:
- Development of FuFiHLA, a lightweight, open-source software.
- Utilizing long-read sequencing data (PacBio HiFi and Nanopore R10).
- Focusing on six key HLA genes: HLA-A, HLA-B, HLA-C, HLA-DRB1, HLA-DQA1, and HLA-DQB1.
Main Results:
- FuFiHLA achieved 99.57% accuracy for full-field HLA allele typing on PacBio HiFi samples.
- Consensus allele sequence construction yielded a QV of 50.1.
- Slightly reduced accuracy was observed for the fourth field with Nanopore R10 reads.
Conclusions:
- FuFiHLA provides an accurate method for full-field HLA allele typing directly from long reads.
- The software enhances the understanding of HLA variants, including those in non-coding regions.
- FuFiHLA is publicly available on GitHub under the MIT License.
