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GRIMM: GRaph IMputation and Matching for HLA Genotypes.

Bioinformatics 2019 January 29
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 allele 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: The Python, Perl and Neo4j code is available at https://github.com/nmdp-bioinformatics/grimm.

Supplementary information: Supplementary data are available at Bioinformatics online.

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