1. GRIMM: GRaph IMputation and matching for HLA genotypes.
- Author
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Maiers M, Halagan M, Gragert L, Bashyal P, Brelsford J, Schneider J, Lutsker P, and Louzoun Y
- Subjects
- Genotype, Histocompatibility Testing, Humans, Tissue Donors, HLA Antigens genetics
- Abstract
Motivation: For over 10 years allele-level HLA matching for bone marrow registries has been performed in a probabilistic context. HLA typing technologies provide ambiguous results in that they could not distinguish among all known HLA alleles equences; therefore registries have implemented matching algorithms that provide lists of donor and cord blood units ordered in terms of the likelihood of allele-level matching at specific HLA loci. With the growth of registry sizes, current match algorithm implementations are unable to provide match results in real time., Results: We present here a novel computationally-efficient open source implementation of an HLA imputation and match algorithm using a graph database platform. Using graph traversal, the matching algorithm runtime is practically not affected by registry size. This implementation generates results that agree with consensus output on a publicly-available match algorithm cross-validation dataset., Availability and Implementation: The Python, Perl and Neo4j code is available at https://github.com/nmdp-bioinformatics/grimm., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
- Published
- 2019
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