RT Journal
A1 Xin, Ying
A1 Amanullah, Md
A1 Qian, Cheng
A1 Zhou, Chingmo
A1 Qian, Jiang
T1 Lignature provides a curated resource of ligand induced transcriptomic signatures for signaling inference
JF Genome Research 
JO Genome Research 
YR 2026 
FD April 08 
DO 10.1101/gr.281287.125 
SP gr.281287.125 
UL http://genome.cshlp.org/content/early/2026/04/08/gr.281287.125.abstract 
AB Ligand-receptor interactions mediate intercellular communication, inducing transcriptional changes that regulate physiological and pathological processes. Ligand-induced transcriptomic signatures can be used to infer ligand activity; however, the absence of a comprehensive set of ligand-response signatures has limited their practical application in predicting ligand-receptor interactions. To bridge this gap, we develop Lignature, a curated database encompassing intracellular transcriptomic signatures for 362 human ligands, significantly expanding the repertoire of ligands with available intracellular response signatures such as CytoSig and ImmuneDictionary. Lignature compiles signatures from published transcriptomic datasets, generating both gene- and pathway-based signatures for each ligand. We apply Lignature to prioritize ligand-associated transcriptional activity in controlled in vitro experiments and real-world single-cell sequencing datasets. Across these settings, Lignature consistently improves the prioritization of experimentally supported ligands compared with existing approaches. We additionally develop a regression-based framework to model combinatorial regulation by multiple ligands. These results establish Lignature as a robust platform for ligand signaling inference, providing a powerful tool to explore ligand-receptor interactions across diverse experimental and physiological contexts.