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bioRxiv · 10.1101/2022.03.15.484380

UniFuncNet: a flexible network annotation framework

Queiros, P.·Hickl, O.·Wilmes, P.·May, P.·Martinez Arbas, S.
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Abstract

SummaryFunctional 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, UniFuncNets data integration methodology allows for the output of a non-redundant composite network and associated metadata. In addition, a workflow exporting UniFuncNets output to the graph database management system Neo4j was implemented, which allows for efficient querying and analysis. AvailabilitySource code is available at https://github.com/PedroMTQ/UniFuncNet.

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Queiros, P., Hickl, O., Wilmes, P., May, P., Martinez Arbas, S.. 2022-03-17. UniFuncNet: a flexible network annotation framework. https://doi.org/10.1101/2022.03.15.484380

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