RNAactDrug 2.0: an updated resource for systematically exploring molecular features associated with drug sensitivity across multi-omics datasets

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ID: 338108
2026
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Abstract
RNAactDrug 2.0 (available at https://bio-bigdata.hrbmu.edu.cn/RNAactDrug or https://bio-datamining.hrbmu.edu.cn/RNAactDrug) is an updated resource for systematically exploring molecular features associated with drug sensitivity across multi-omics datasets. We have updated the RNAactDrug 2.0 database with significantly expanded data and improved features, including (i) ∼30 000 drugs, 38 908 RNA molecules, and 22 800 375 drug-RNA associations from large-scale pharmacogenomic databases, covering four molecular layers: expression, copy number variation (CNV), mutation, and methylation; (ii) 11 099 cases from The Cancer Genome Atlas across 33 cancer types are integrated to support clinical and pan-cancer analyses of drug sensitivity-associated RNA features; (iii) ∼3.8 million cells from >200 single-cell RNA sequencing datasets, spanning 34 cancer types and 38 tissues, enable analysis of cell type-specific patterns and intratumoral heterogeneity; (iv) 26 440 enhancers and 788 444 drug-enhancer associations, combined with potential target prediction, provide an exploratory epigenetic regulatory dimension; (v) enhanced search and browsing functionalities, together with seven tools for functional annotation, pan-cancer analysis, tumor microenvironment profiling, single-cell trajectory and CNV analysis, and enhancer-related analyses. In summary, RNAactDrug 2.0 is a crucial resource for the multi-omics dissection of drug sensitivity-associated molecules, providing important support for the discovery of drug resistance biomarkers and the advancement of personalized therapeutic strategies.
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openalex_W7225077009 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Feng Li, Liying Pei, Jiangtao Xing, 康安辉, Zhe Chen, Wensong Liu, Siyao Lu, Longqi Xu, Xinyi Wang, Chunlong Zhang, Yanjun Xu
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Journal Nucleic Acids Research
Year 2026
DOI
10.1093/nar/gkag1117
URL
https://doi.org/10.1093/nar/gkag1117
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