GRIMM: GRaph IMputation and matching for HLA genotypes
| Autor: | Joel Schneider, Polina Lutsker, Loren Gragert, Pradeep Bashyal, Michael Halagan, Yoram Louzoun, Martin Maiers, Jason Brelsford |
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| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
Genotype Computer science Locus (genetics) Human leukocyte antigen computer.software_genre Biochemistry 03 medical and health sciences 0302 clinical medicine HLA Antigens Graph traversal Humans Molecular Biology Blossom algorithm 030304 developmental biology computer.programming_language 0303 health sciences Graph database Histocompatibility Testing Probabilistic logic Graph Tissue Donors Computer Science Applications Computational Mathematics Computational Theory and Mathematics Data mining Perl computer Imputation (genetics) 030215 immunology |
| Zdroj: | Bioinformatics (Oxford, England). 35(18) |
| ISSN: | 1367-4811 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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