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InCURA: integrative gene clustering based on transcription factor binding sites

Biologically meaningful interpretation of transcriptomic datasets remains challenging, particularly when context-specific gene sets are either unavailable or too generic to capture the underlying biology. We here present InCURA, an integrative clustering strategy based on transcription factor (TF) m...

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Hauptverfasser: Wessels, Lorna (VerfasserIn) , Flores, Ricardo O Ramirez (VerfasserIn) , Sáez Rodríguez, Julio (VerfasserIn) , Singhal, Mahak (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: December 2025
In: Nucleic acids research
Year: 2025, Jahrgang: 53, Heft: 22, Pages: 1-16
ISSN:1362-4962
DOI:10.1093/nar/gkaf1377
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.1093/nar/gkaf1377
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Verfasserangaben:Lorna Rinck, Ricardo O. Ramirez Flores, Julio Saez-Rodriguez, Mahak Singhal
Beschreibung
Zusammenfassung:Biologically meaningful interpretation of transcriptomic datasets remains challenging, particularly when context-specific gene sets are either unavailable or too generic to capture the underlying biology. We here present InCURA, an integrative clustering strategy based on transcription factor (TF) motif occurrence patterns in gene promoters. InCURA takes as input lists of (i) all expressed genes, used solely to identify dataset-specific expressed TFs, and (ii) differentially regulated genes (DRGs) used for clustering. Promoter sequences of DRGs are scanned for TF binding motifs, and the resulting counts are compiled into a gene-by-TFBS matrix. InCURA then uses unsupervised clustering to infer gene modules with shared predicted regulatory input. Applying InCURA to diverse biological datasets, we uncovered functionally coherent gene modules revealing upstream regulators and regulatory programs that standard enrichment or co-expression analyses fail to detect. In summary, InCURA provides a user-friendly, regulation-centric tool for dissecting transcriptional responses, particularly in settings lacking context-specific gene sets.
Beschreibung:Veröffentlicht: 11. Dezember 2025
Gesehen am 09.02.2026
Beschreibung:Online Resource
ISSN:1362-4962
DOI:10.1093/nar/gkaf1377

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