Nucleic acids researchNov 2, 2026

CircaVal: a gene-focused platform for studying daily biological rhythms using multiple data types

Circadian Biology Weekly Brief ↗PubMed ↗DOI ↗OA ↗

Updated Nov 3, 2026

Abstract

CircaVal is a gene-centric multi-omics platform designed to explore and interpret circadian regulatory features across humans, mice, and baboons.

CircaVal integrates public datasets using standardized pipelines, facilitating the analysis of circadian gene expression.
It combines gene rhythmicity with tissue-specific expression and multi-omics regulatory features to enhance understanding of circadian regulation.
An integrated rhythmicity score (IRS) is introduced to prioritize candidate rhythmic genes based on multiple rhythm-detection methods and dataset reproducibility.
The platform allows users to identify tissue-enriched rhythmic genes and compare rhythmicity across different species.
CircaVal offers interactive exploration, genome-browser visualization, and analysis capabilities for user-uploaded time-series data.

Simplified

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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CircaVal: a gene-focused platform for studying daily biological rhythms using multiple data types

Abstract

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