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Summary: Efficient storage and querying of large amounts of genetic and phenotypic data is crucial to contemporary clinical genetic research. This introduces computational challenges for classical relational databases, due to the sparsity and sheer volume of the data. Our Java based solution loads annotated genetic variants and well phenotyped patients into a graph database to allow fast efficient storage and querying of large volumes of structured genetic and phenotypic data. This abstracts technical problems away and lets researchers focus on the science rather than the implementation. We have also developed an accompanying webserver with end-points to facilitate querying of the database. Availability and implementation: The Java and Python code are available at https://github.com/phenopolis/pheno4j. Contact: n.pontikos@ucl.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

Original publication

DOI

10.1093/bioinformatics/btx397

Type

Journal article

Journal

Bioinformatics

Publication Date

15/10/2017

Volume

33

Pages

3317 - 3319

Keywords

Computational Biology, Databases, Genetic, Genetic Variation, Humans, Phenotype, Software