Hapl-o-Mat: open-source software for HLA haplotype frequency estimation from ambiguous and heterogeneous data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Christian Schäfer, Alexander H. Schmidt, Jürgen Sauter

ABSTRACT

BACKGROUND: Knowledge of HLA haplotypes is helpful in many settings as disease association studies, population genetics, or hematopoietic stem cell transplantation. Regarding the recruitment of unrelated hematopoietic stem cell donors, HLA haplotype frequencies of specific populations are used to optimize both donor searches for individual patients and strategic donor registry planning. However, the estimation of haplotype frequencies from HLA genotyping data is challenged by the large amount of genotype data, the complex HLA nomenclature, and the heterogeneous and ambiguous nature of typing records. RESULTS: To meet these challenges, we have developed the open-source software Hapl-o-Mat. It estimates haplotype frequencies from population data including an arbitrary number of loci using an expectation-maximization algorithm. Its key features are the processing of different HLA typing resolutions within a given population sample and the handling of ambiguities recorded via multiple allele codes or genotype list strings. Implemented in C++, Hapl-o-Mat facilitates efficient haplotype frequency estimation from large amounts of genotype data. We demonstrate its accuracy and performance on the basis of artificial and real genotype data. CONCLUSIONS: Hapl-o-Mat is a versatile and efficient software for HLA haplotype frequency estimation. Its capability of processing various forms of HLA genotype data allows for a straightforward haplotype frequency estimation from typing records usually found in stem cell donor registries. More... »

PAGES

284

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12859-017-1692-y

DOI

http://dx.doi.org/10.1186/s12859-017-1692-y

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https://app.dimensions.ai/details/publication/pub.1085704911

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28558647


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