Recon2Neo4j: Applying graph database technologies for managing comprehensive genome-scale networks

Irina Balaur*, Alexander Mazein, Mansoor Saqi, Artem Lysenko, Christopher J. Rawlings, Charles Auffray

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

26 Citations (Scopus)

Abstract

The goal of this work is to offer a computational framework for exploring data from the Recon2 human metabolic reconstruction model. Advanced user access features have been developed using the Neo4j graph database technology and this paper describes key features such as efficient management of the network data, examples of the network querying for addressing particular tasks, and how query results are converted back to the Systems Biology Markup Language (SBML) standard format. The Neo4j-based metabolic framework facilitates exploration of highly connected and comprehensive human metabolic data and identification of metabolic subnetworks of interest. A Java-based parser component has been developed to convert query results (available in the JSON format) into SBML and SIF formats in order to facilitate further results exploration, enhancement or network sharing. Availability and Implementation: The Neo4j-based metabolic framework is freely available from: https://diseaseknowledgebase.etriks.org/metabolic/browser/. The Java code files developed for this work are available from the following url: https://github.com/ibalaur/MetabolicFramework.

Original languageEnglish
Pages (from-to)1096-1098
Number of pages3
JournalBioinformatics
Volume33
Issue number7
DOIs
Publication statusPublished - 1 Apr 2017
Externally publishedYes

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