4.7 Article

RBCeq: A robust and scalable algorithm for accurate genetic blood typing

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EBIOMEDICINE
卷 76, 期 -, 页码 -

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ELSEVIER
DOI: 10.1016/j.ebiom.2021.103759

关键词

Alloimmunization; Blood groups; Next-generation sequencing; Population genomics; Red blood cell antigens; Single nucleotide polymorphism; Transfusion

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The study developed a novel genetic blood typing algorithm RBCeq to accurately identify 36 blood group systems, predict complex blood types, and report variants with potential clinical relevance. RBCeq can assist blood banks and laboratories in overcoming methodological limitations in multi-ethnic populations.
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 polymor-phisms. Despite the promise, user friendly methods to accurately identify blood types from next-generation sequenc-ing 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 cent 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 vali-dation 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 method-ological 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. Copyright (c) 2021 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/) eBioMedicine ebiom.2021.103759 Superscript/Subscript Available

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