GRIMM: GRaph IMputation and matching for HLA genotypes | Zendy

Martin Maiers | Zendy; Michael Halagan | Zendy; Loren Gragert | Zendy; Pradeep Bashyal | Zendy; Jason Brelsford | Zendy; Joel Schneider | Zendy; Polina Lutsker | Zendy; Yoram Louzoun | Zendy

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GRIMM: GRaph IMputation and matching for HLA genotypes

Author(s) -

Martin Maiers,

Michael Halagan,

Loren Gragert,

Pradeep Bashyal,

Jason Brelsford,

Joel Schneider,

Polina Lutsker,

Yoram Louzoun

Publication year - 2019

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btz050

Subject(s) - computer science , python (programming language) , matching (statistics) , human leukocyte antigen , perl , source code , graph traversal , open source , graph , data mining , software , theoretical computer science , programming language , biology , mathematics , genetics , statistics , antigen

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.

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