A computational HLA allele-typing protocol to de-noise and leverage nanopore amplicon data
Abstract Background Rapid turnaround time for a third-field resolution deceased donor human leukocyte antigen (HLA) typing is critical to improve organ transplantation outcomes. Third generation DNA sequencing platforms such as Oxford Nanopore (ONT) offer the opportunity to deliver rapid results at...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-04-01
|
| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12864-025-11547-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract Background Rapid turnaround time for a third-field resolution deceased donor human leukocyte antigen (HLA) typing is critical to improve organ transplantation outcomes. Third generation DNA sequencing platforms such as Oxford Nanopore (ONT) offer the opportunity to deliver rapid results at single nucleotide level resolution, in particular sequencing data that could be denoised computationally. Here we present a computational pipeline for up-to third-field HLA allele typing following ONT sequencing. Results From a R10.3 flow cell batch of 31 samples of known HLA allele types, up to 10,000 ONT reads were aligned using BWA aligner to reference allele sequences from the IPD-IMGT/HLA database. For each gene, the top two hits to reference alleles at the third field were selected. Using our pipeline, we obtained the following percent concordance at the 1st, 2nd and 3rd field: HLA-A (98.4%, 98.4%, 98.4%), HLA-B (100%, 96.8%, 96.8%), HLA-C (100%, 98.4%, 98.4%), HLA-DPA1 (100%, 96.8%, 96.8%), HLA-DPB1 (100%, 100%, 98.4%), HLA-DQA1 (100%, 98.4%, 98.4%), HLA-DQB1 (100%, 98.4%, 98.4%), HLA-DRB1 (83.9%, 64.5%, 64.5%), HLA-DRB3 (82.6%, 73.9%, 73.9%), HLA-DRB4 (100%, 100%, 100%) and HLA-DRB5 (100%, 100%, 100%). By running our pipeline on an additional R10.3 flow cell batch of 63 samples, the following percent concordances were obtained:: HLA-A (100%, 96.8%, 88.1%), HLA-B (100%, 90.5.4%, 88.1%), HLA-C (100%, 99.2%, 99.2%), HLA-DPA1 (100%, 98.4%, 97.6%), HLA-DPB1 (98.4%, 97.6%, 92.9%), HLA-DQA1 (100%, 100%, 98.4%), HLA-DQB1 (100%, 97.6%, 96.0%), HLA-DRB1 (88.9%, 68.3%, 68.3%), HLA-DRB3 (81.0%, 61.9%, 61.9%), HLA-DRB4 (100%, 97.4%, 94.7%) and HLA-DRB5 (73.3%, 66.7%, 66.7%). In addition, our pipeline demonstrated significantly improved concordance compared to publicly available pipeline HLA-LA and concordances close to Athlon2 in commercial development. Conclusion Our algorithm had a > 96% concordance for non-HLA-DRB genes at 3rd field on the first batch and > 88% concordance for non-HLA-DRB genes at 3rd field and > 90% at 2nd field on the second batch tested. In addition, it out-performs HLA-LA and approaches the performance of the Athlon2. This lays groundwork for better utilizing Nanopore sequencing data for HLA typing especially in improving organ transplant outcomes. |
|---|---|
| ISSN: | 1471-2164 |