[en] AbstractSummaryFunctional annotation is an integral part in the analysis of organisms, as well as of multi-species communities. A common way to integrate such information is using biological networks. However, current data integration network tools are heavily dependent on a single source of information, which might strongly limit the amount of relevant data contained within the network. Here we present UniFuncNet, a network annotation framework that dynamically integrates data from multiple biological databases, thereby enabling data collection from various sources based on user preference. This results in a flexible and comprehensive data retrieval framework for network based analyses of omics data. Importantly, UniFuncNet’s data integration methodology allows for the output of a non-redundant composite network and associated metadata. In addition, a workflow exporting UniFuncNet’s output to the graph database management system Neo4j was implemented, which allows for efficient querying and analysis.AvailabilitySource code is available at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/PedroMTQ/UniFuncNet">https://github.com/PedroMTQ/UniFuncNet</jats:ext-link>.
Research center :
ULHPC - University of Luxembourg: High Performance Computing
Disciplines :
Life sciences: Multidisciplinary, general & others
Author, co-author :
Queirós, Pedro
HICKL, Oskar ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core
MARTINEZ ARBAS, Susana ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine > Systems Ecology > Team Paul WILMES
WILMES, Paul ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Ecology