Integrated bioinformatics solution to improve compatibility testing for transfusion

AimThe overall objective of this study is to provide a new basis for pretransfusion testing by accurate characterization of the complete blood group variant profile from complex Next-Gen Sequencing (NGS) data.MethodWe are developing a novel algorithm for the typing of 36 blood group system using NGS data. The algorithm is divided into three steps:1) Extract single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) from NGS data; 2) Identify the known blood group (BG) alleles from SNPs and CNVs including those resulting from conversion, crossover and other recombination events; 3) Prediction of novel BG from rare variants without a current blood group phenotype association and variants that may encode novel antigens. Finally, all three steps of the algorithm will be integrated into a user-friendly software. We are using publically available and in-house sequenced genome data for the development of the software. The accuracy of the software will be assessed by comparing results with serologically predicted BGs.ResultsThe first step of software development is completed, wherein the software accepts fastq files with user-provided parameters and performs alignment, quality control, and detection, annotation and filtering of SNPs and CNVs. Additionally, we have created an in-house database, QUT BG, which contains the genetic profiles of known BGs obtained from experimentally validated online resources such as ISBT, RhesusBase, and Erythrogen. Currently we are working on predicting known BG using the identified genetic profiles and QUT DB.ConclusionsThe novel algorithm developed in this study will overcome the clinical limitations such as usability and accuracy of the existing methods for BG genotyping and matching

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

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.</p