An open‐source python library for detection of known and novel Kell, Duffy and Kidd variants from exome sequencing

• DOI: 10.1111/vox.13035
• Publication Date: 2021-02-11T05:56:02Z
• Authors: Celina Montemayor; Alexandra Simone; James Long; Oscar Montemayor; Bhavesh Delvadia; Robert Rivera; Katie L Lewis; Shahin Shahsavari; Divya Gandla; Katherine Dura; Uma S Krishnan; Nena C Wendzel; Nasha Elavia; Spencer Grissom; Panagiota Karagianni; Marina Bueno; Debrean Loy; Rizaldi Cacanindin; Steven McLaughlin; Maxim Tynuv; Patricia A R Brunker; John Roback; Sharon Adams; Harold Smith; Leslie Biesecker; Harvey G Klein
• Keywords: Exome; Python

Abstract Background and objectives Next generation sequencing (NGS) has promising applications in transfusion medicine. Exome (ES) is increasingly used the clinical setting, blood group interpretation an additional value that could be extracted from existing data sets. We provide first release of open‐source software tailored for this purpose describe its validation with three systems. Materials methods The DTM‐Tools algorithm was designed to analyse 1018 ES NGS files ClinSeq ® cohort. Predictions were correlated serology 5 antigens a subset 108 samples. Discrepancies investigated alternative phenotyping genotyping methods, including long‐read platform. Results Of 116 genomic variants queried, those corresponding 18 known KEL, FY JK alleles identified 596 exonic ACKR1 SLC14A1, 58 predicted frameshifts. Software predictions validated by participants; one case cases discrepant. Investigation revealed these discrepancies resulted (1) clerical error, (2) serologic failure detect weak antigenic expression (3) frameshift variant absent databases. Conclusion can employed rapid Kell, Duffy Kidd antigen prediction sets; detected set, proved accurate. continuous development.