bioRxiv · 10.1101/2024.09.13.612391
FunCoup 6: advancing functional association networks across species with directed links and improved user experience
Abstract
FunCoup 6 (https://funcoup6.scilifelab.se/, will be https://funcoup.org after publication) represents a significant advancement in global functional association networks, aiming to provide researchers with a comprehensive view of the functional coupling interactome. This update introduces novel methodologies and integrated tools for improved network inference and analysis. Major new developments in FunCoup 6 include vastly expanding the coverage of gene regulatory links, a new framework for bin-free Bayesian training, and a new website. FunCoup 6 integrates a new tool for disease and drug target module identification using the TOPAS algorithm. To expand the utility of the resource for biomedical research, it incorporates pathway enrichment analysis using the ANUBIX and EASE algorithms. The unique comparative interactomics analysis in FunCoup provides insights of network conservation, now allowing users to align orthologs only or query each species network independently. Bin-free training was applied to 23 primary species, and in addition networks were generated for all remaining 618 species in InParanoiDB 9. Accompanying these advancements, FunCoup 6 features a new redesigned website, together with updated API functionalities, and represents a pivotal step forward in functional genomics research, offering unique capabilities for exploring the complex landscape of protein interactions. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=66 SRC="FIGDIR/small/612391v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1cf674eorg.highwire.dtl.DTLVardef@18737f0org.highwire.dtl.DTLVardef@699d88org.highwire.dtl.DTLVardef@1e57910_HPS_FORMAT_FIGEXP M_FIG C_FIG
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Buzzao, D., Persson, E., Guala, D., Sonnhammer, E. L. L.. 2024-09-18. FunCoup 6: advancing functional association networks across species with directed links and improved user experience. https://doi.org/10.1101/2024.09.13.612391
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