CircaVal: a gene-centric multi-omics platform for exploring circadian regulatory features

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ID: 338118
2026
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Abstract
The circadian clock orchestrates daily physiological processes through coordinated regulation of clock-controlled gene expression across multiple molecular layers. However, systematic characterization of this multilayered rhythmic regulation remains challenging because relevant information is distributed across independent datasets and rhythmicity is evaluated using diverse analytical approaches. Here, we developed CircaVal, a gene-centric multi-omics platform for the integrated exploration and interpretation of circadian regulatory features in humans, mice, and baboons. To support this framework, CircaVal processes public datasets using standardized, data-type-specific pipelines, and gene rhythmicity is integrated with tissue-specific expression, cross-species orthology, functional annotations, and multi-omics regulatory features. CircaVal further introduces an integrated rhythmicity score (IRS), which combines concordance across multiple rhythm-detection methods with reproducibility across independent datasets to provide a quantitative basis for prioritizing candidate rhythmic genes. The platform enables users to examine gene rhythmicity across datasets, identify tissue-enriched rhythmic genes, compare rhythmicity across species, and explore associated regulatory features. Additional functions include interactive gene-centric exploration, genome-browser visualization, rhythmicity analysis of user-uploaded time-series data, and programmatic data access. Together, these capabilities provide an integrated framework for rhythmic gene prioritization and gene-level interpretation of circadian regulatory features across tissues, species, datasets, and omics data types. CircaVal is freely available at https://CircaVal.com.
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Authors Guohao Han, Xiaodie Wu, Qian Li, Runhe Shi, Xiaoyun Xiao, Jijun Liu, Haisen Zhang, Dengke Gao, Chao Li, Aihua Wang, Yaping Jin, Trees‐Juen Chuang, Hsu‐Wen Chao, Huatao Chen
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Journal Nucleic Acids Research
Year 2026
DOI
10.1093/nar/gkag1090
URL
https://doi.org/10.1093/nar/gkag1090
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