An open-source python library for detection of known and novel Kell, Duffy and Kidd variants from exome sequencing
| Autor: | Katherine Dura, Nena C Wendzel, James Long, Debrean A. Loy, Nasha Elavia, Divya Gandla, Katie L. Lewis, Harvey G. Klein, Sharon Adams, Harold E. Smith, John D. Roback, Uma S Krishnan, Alexandra Simone, Robert Rivera, Spencer E Grissom, Marina U Bueno, Steven McLaughlin, Maxim Tynuv, Bhavesh Delvadia, Rizaldi Cacanindin, Leslie G. Biesecker, Shahin Shahsavari, Panagiota Karagianni, Patricia A. R. Brunker, Oscar A. Montemayor, Celina Montemayor |
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| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
Genotyping Techniques Genomics Receptors Cell Surface Computational biology 030204 cardiovascular system & hematology Biology DNA sequencing Serology Frameshift mutation 03 medical and health sciences 0302 clinical medicine Exome Sequencing medicine Humans Allele Genotyping Exome sequencing Alleles Membrane Glycoproteins Genetic Variation High-Throughput Nucleotide Sequencing Membrane Transport Proteins Metalloendopeptidases Transfusion medicine Hematology General Medicine Blood Group Antigens Duffy Blood-Group System Software 030215 immunology |
| Zdroj: | Vox sanguinisReferences. 116(4) |
| ISSN: | 1423-0410 |
| Popis: | BACKGROUND AND OBJECTIVES Next generation sequencing (NGS) has promising applications in transfusion medicine. Exome sequencing (ES) is increasingly used in the clinical setting, and blood group interpretation is an additional value that could be extracted from existing data sets. We provide the first release of an open-source software tailored for this purpose and describe its validation with three blood group systems. MATERIALS AND METHODS The DTM-Tools algorithm was designed and used to analyse 1018 ES NGS files from the ClinSeq® cohort. Predictions were correlated with serology for 5 antigens in a subset of 108 blood samples. Discrepancies were investigated with alternative phenotyping and genotyping methods, including a long-read NGS platform. RESULTS Of 116 genomic variants queried, those corresponding to 18 known KEL, FY and JK alleles were identified in this cohort. 596 additional exonic variants were identified KEL, ACKR1 and SLC14A1, including 58 predicted frameshifts. Software predictions were validated by serology in 108 participants; one case in the FY blood group and three cases in the JK blood group were discrepant. Investigation revealed that these discrepancies resulted from (1) clerical error, (2) serologic failure to detect weak antigenic expression and (3) a frameshift variant absent in blood group databases. CONCLUSION DTM-Tools can be employed for rapid Kell, Duffy and Kidd blood group antigen prediction from existing ES data sets; for discrepancies detected in the validation data set, software predictions proved accurate. DTM-Tools is open-source and in continuous development. |
| Databáze: | OpenAIRE |
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