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The Bioinformatics Research team specializes in developing and utilizing software tools and analytical methods to facilitate data exchange, interpret information, understand patterns and predict factors to save and improve lives. At the intersection of science and technology, this team pursues high-impact and innovative research and produces strategic applications for the business to bridge the transition from research to operations. CIBMTR's Bioinformatics Research Program moves in the direction of Computational Biomedicine with activities in three main areas: Genomics/Omics & High-throughput Bioanalytics, Machine Learning & Clinical Predictions, and Cellular Therapy Matching & Donor Registry Modeling.
Program staff develop processing and annotation workflows to characterize variation in donors and cellular therapy products, patients, and transplant donor-recipient pairs. The team leverages technology platforms that enable integrated, scalable data analysis and high-throughput bioanalytics on a variety of omics sources, including whole-genome, exome, protein, and methylation sequencing and microarrays. Patterns in donors and recipients are analyzed to identify associations with transplant outcomes and factors that contribute to event-free survival.
The Bioinformatics Research Program prepares platforms for data science applications and builds and trains models for analysis of business and clinical data collected in daily operations at CIBMTR and NMDP®/Be The Match® and through network partners and research trials. Applications from search archives and donor availability, for example, provide insight into areas of future focus and improvement for NMDP®/Be The Match® operations. Collation and integration of 1) provider-reported clinical data and electronic medical records, 2) patient-reported data on the five areas of financial, cognitive, physical, sexual, and emotional health, and 3) in-depth collection of omics data from therapy sources and recipients, set the stage for clinical predictions and applications that the Bioinformatics Research Program investigates and develops for improving survival outcomes and quality of life for all.
The Bioinformatics Research Program investigates algorithms to improve the prediction of missing data and the selection of cellular therapies for patients toward best survival and quality of life outcomes. Program researchers improve the collection, analysis, validation, and utilization of data on donors and patients with diverse ancestry for feature improvements. Fresh approaches leveraging graph imputation and matching are tested for accuracy, flexibility, and scalability to produce applications for NMDP®/Be The Match® and ensure new clinical results can be incorporated into the matching algorithm as soon as possible. Finally, the translation of research results and evidence-based guidelines to user interfaces by the Bioinformatics Research team helps to optimize cellular therapy matching and donor selection for physicians and transplant centers.
To determine how to best meet the needs of all patients in need of cellular therapy, the Bioinformatics Research Program models the composition of the Donor Registry and Biobank in order to project and optimize the need and availability of cellular therapies for patients in need. These and related projects help to increase the likelihood of finding a match for patients who have HLA types more commonly found outside the US and seek to prepare a ready source of cellular therapy in case of acute radiation emergency. The Bioinformatics Research Program ensures that CIBMTR and NMDP®/Be The Match® are at the forefront of research and that new technologies and clinical findings can be incorporated into the operational side of CIBMTR and NMDP®/Be The Match® as swiftly and seamlessly as possible.
Multiplicative fitness, rapid haplotype discovery, and fitness decay explain evolution of human MHC. Lobkovsky AE, Levi L, Wolf YI, Maiers M, Gragert L, Alter I, Louzoun Y, Koonin EV. Proc Natl Acad Sci U S A. 2019 Jun 21. pii: 201714436. doi: 10.1073/pnas.1714436116. [Epub ahead of print]
The association between HLA and non-Hodgkin lymphoma subtypes, among a transplant-indicated population. Zhong C, Gragert L, Maiers M, Hill BT, Garcia-Gomez J, Gendzekhadze K, Senitzer D, Song J, Weisenburger D, Goldstein L, Wang SS. Leuk Lymphoma. 2019 Jun 19:1-10. doi: 10.1080/10428194.2019.1617858. [Epub ahead of print]
Regarding "Recipients Receiving Better HLA-Matched Hematopoietic Cell Transplantation Grafts, Uncovered by a Novel HLA Typing Method, Have Superior Survival: A Retrospective Study". Hurley CK, Spellman S, Dehn J, Barker JN, Devine S, Fernandez-Vina M, Gautreaux M, Logan B, Maiers M, Mueller C, Perales MA, Yu N, Pidala J. Biol Blood Marrow Transplant. 2019 May 27. pii: S1083-8791(19)30337-4. doi: 10.1016/j.bbmt.2019.05.026. [Epub ahead of print]
High resolution HLA allele and haplotype frequencies for Arab donors in the Hadassah bone marrow donor registry. Bishara A, Halagan M, Brautbar C, Israel S, Maiers M, Madbouly A. Hum Immunol. 2019 May 21. pii: S0198-8859(18)31222-9. doi: 10.1016/j.humimm.2019.05.003. [Epub ahead of print]
Reducing ethnic disparity in access to high-quality HLA-matched cord blood units for transplantation: analysis of the Canadian Blood Services' Cord Blood Bank inventory. Allan D, Kiernan J, Gragert L, Dibdin N, Bartlett D, Campbell T, Mostert K, Halpenny M, Ganz K, Maiers M, Petraszko T, Elmoazzen H. Transfusion. 2019 Apr 19. doi: 10.1111/trf.15313. [Epub ahead of print]
GRIMM: GRaph IMputation and Matching for HLA Genotypes. Maiers M, Halagan M, Gragert L, Bashyal P, Brelsford J, Schneider J, Lutsker P, Louzoun Y. Bioinformatics. 2019 Jan 28. doi: 10.1093/bioinformatics/btz050. [Epub ahead of print]