GRIMM: GRaph IMputation and matching for HLA genotypes

• Published in: Bioinformatics Vol. 35; no. 18; pp. 3520 - 3523
• Main Authors: Maiers, Martin, Halagan, Michael, Gragert, Loren, Bashyal, Pradeep, Brelsford, Jason, Schneider, Joel, Lutsker, Polina, Louzoun, Yoram
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 15.09.2019
• ISSN: 1367-4803; 1367-4811; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btz050

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.