一种用于去噪和利用纳米孔扩增子数据的计算性HLA等位基因分型方案。
A computational HLA allele-typing protocol to de-noise and leverage nanopore amplicon data.
作者信息
Siddiqui Jalal, Sinha Rohita, Grantham James, LaCombe Ronnie, Alonzo Judith R, Cowden Scott, Kleiboeker Steven
机构信息
Eurofins Viracor Clinical Diagnostics, 18000 W 99th St, Lenexa, KS, 66219, United States of America.
出版信息
BMC Genomics. 2025 Apr 8;26(1):356. doi: 10.1186/s12864-025-11547-4.
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.
背景
快速周转时间以实现三字段分辨率的已故供体人类白细胞抗原（HLA）分型对于改善器官移植结果至关重要。第三代DNA测序平台，如牛津纳米孔（ONT），提供了在单核苷酸水平分辨率下快速得出结果的机会，特别是可以通过计算进行去噪的测序数据。在此，我们展示了一种用于ONT测序后进行高达三字段HLA等位基因分型的计算流程。
结果
从一批包含31个已知HLA等位基因类型样本的R10.3流动槽中，使用BWA比对器将多达10,000条ONT读数与IPD-IMGT/HLA数据库中的参考等位基因序列进行比对。对于每个基因，选择在第三字段中与参考等位基因匹配度最高的前两个结果。使用我们的流程，在第一、第二和第三字段获得了以下一致性百分比：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%）和HLA-DRB5（100%、100%、100%）。通过在另一批63个样本的R10.3流动槽上运行我们的流程，获得了以下一致性百分比：HLA-A（100%、96.8%、88.1%），HLA-B（100%、90.5%、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%）和HLA-DRB5（73.3%、66.7%、66.7%）。此外，与公开可用的流程HLA-LA相比，我们的流程显示出显著提高的一致性，并且在商业开发中与Athlon2的一致性相近。
结论
在第一批测试中，我们的算法在第三字段对于非HLA-DRB基因的一致性>96%，在第二批测试中，在第三字段对于非HLA-DRB基因的一致性>88%，在第二字段的一致性>90%。此外，它优于HLA-LA，并接近Athlon2的性能。这为更好地利用纳米孔测序数据进行HLA分型奠定了基础，特别是在改善器官移植结果方面。
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