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FuFiHLA: a tool for full-field HLA typing from long-read data
Jingqing Hu1, Qian Qin1,2,3, Heng Li1,2,4
1Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, United States.
Motivation:
Allele typing for Human Leukocyte Antigen (HLA) genes has many important clinical applications. Popular short-read typing can only accurately distinguish alleles at the coding sequence level, which potentially limit our understanding of the effect of variants in non-coding region. Long read data has been proved to be useful in typing HLA alleles in full resolution, but only a few tools are publicly available and with significant limitations in practical application.
Results:
We developed FuFiHLA, a lightweight open-source software, to type HLA alleles. Currently it supports typing alleles of six HLA genes (HLA-A, HLA-B, HLA-C, HLA-DRB1, HLA-DQA1, and HLA-DQB1) from long reads. Evaluation using 233 PacBio HiFi WGS samples from HPRC shows that FuFiHLA achieves 99.6% accuracy in the full field allele typing and QV as 51.8 for consensus allele sequence construction. Additional testing on four Nanopore R10 reads demonstrates slightly reduced accuracy in the fourth field.
Availability:
FuFiHLA is available at https://github.com/jingqing-hu/FuFiHLA under MIT License.
