UniFuncNet: a flexible network annotation framework

Abstract

Summary: Functional 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. Availability: Source code is available at https://github.com/PedroMTQ/UniFuncNet .

Integrating biological knowledge for mechanistic inference in the host-associated microbiome

“…The UniFuncNet framework can take as input a list of entities from different databases (e.g., proteins, genes, metabolites, etc) and output a network representation of all associations among such entities. UniFuncNet’s applications are demonstrated through two workflows which, for example, expanded existing GSMNs of Akkermansia muciniphila to include the biosynthesis and metabolism of glycans, or to relate compounds identified in a metabolomics dataset to relevant pathways and organisms ( Queirós et al, 2022 ).…”

“…3389/fmicb.2024.1351678 Frontiers in Microbiology 10 frontiersin.org examined flavonoid production pathways and an alkaloid pathway in Escherichia coli for the purposes of microbial engineering for chemical production (Carbonell et al, 2018). The Unified Functional Network (UniFuncNet) is another framework that integrates multiple resources necessary for the construction of GSMNs (Queirós et al, 2022). The UniFuncNet framework can take as input a list of entities from different databases (e.g., proteins, genes, metabolites, etc) and output a network representation of all associations among such entities.…”