| Autor: |
Sudhir Jadhao, Candice L. Davison, Eileen V. Roulis, Elizna M. Schoeman, Mayur Divate, Mitchel Haring, Chris Williams, Arvind Jaya Shankar, Simon Lee, Natalie M. Pecheniuk, David O Irving, Catherine A. Hyland, Robert L. Flower, Shivashankar H. Nagaraj |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
EBioMedicine, Vol 76, Iss , Pp 103759- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2352-3964 |
| DOI: |
10.1016/j.ebiom.2021.103759 |
| Popis: |
Summary: Background: While blood transfusion is an essential cornerstone of hematological care, patients requiring repetitive transfusion remain at persistent risk of alloimmunization due to the diversity of human blood group polymorphisms. Despite the promise, user friendly methods to accurately identify blood types from next-generation sequencing data are currently lacking. To address this unmet need, we have developed RBCeq, a novel genetic blood typing algorithm to accurately identify 36 blood group systems. Methods: RBCeq can predict complex blood groups such as RH, and ABO that require identification of small indels and copy number variants. RBCeq also reports clinically significant, rare, and novel variants with potential clinical relevance that may lead to the identification of novel blood group alleles. Findings: The RBCeq algorithm demonstrated 99·07% concordance when validated on 402 samples which included 29 antigens with serology and 9 antigens with SNP-array validation in 14 blood group systems and 59 antigens validation on manual predicted phenotype from variant call files. We have also developed a user-friendly web server that generates detailed blood typing reports with advanced visualization (https://www.rbceq.org/). Interpretation: RBCeq will assist blood banks and immunohematology laboratories by overcoming existing methodological limitations like scalability, reproducibility, and accuracy when genotyping and phenotyping in multi-ethnic populations. This Amazon Web Services (AWS) cloud based platform has the potential to reduce pre-transfusion testing time and to increase sample processing throughput, ultimately improving quality of patient care. Funding: This work was supported in part by Advance Queensland Research Fellowship, MRFF Genomics Health Futures Mission (76,757), and the Australian Red Cross LifeBlood. The Australian governments fund the Australian Red Cross Lifeblood for the provision of blood, blood products and services to the Australian community. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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