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BioinformaticsBioinformaticsPeer-reviewed journal article

Pheno4J: A Gene To Phenotype Graph Database

Sajid Mughal, Ismail MoghuliD, Jing YuiD, UKIRDC, Graeme Charles M. BlackiD, Georgina Hall, Stuart IngramiD, Rachel L. Gillespie, Forbes D. C. MansoniD, Panagiotis I. SergouniotisiD, Chris F. InglehearniD, Carmel ToomesiD, Manir AliiD, Martin McKibbiniD, James A. PoulteriD, Kamron N. KhaniD, Emma C. LordiD, Andrea Hilary NemethiD, Susan M. DownesiD, Stephanie HalfordiD, Stefano LiseiD, Gavin ArnoiD, Alessia FiorentinoiD, Nikolas PontikosiD, Vincent PlagnoliD, , David S Gregory

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Abstract

SummaryEfficient 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.\n\nAvailability and ImplementationThe Java code and python code is available at https://github.com/phenopolis/pheno4i\n\nContactn.pontikos@ucl.ac.uk

Citations
13
FWCI
1.09
Field percentile
top 22%

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