About BodyClocks

Overview

BodyClocks is an interactive database for exploring circadian transcriptomic time-series data. It was developed alongside a study of mouse primary articular chondrocytes in which heat shock, dexamethasone, and osmotic stress all reset the core molecular clock, but produced markedly different downstream rhythmic transcriptomes. That context shaped the purpose of the app: to move beyond checking whether a single gene is rhythmic and make it easier to inspect the biological pathways, interaction networks, and compare rhythmic gene lists between experimental conditions and tissues.

• Circadian Explorer — browse rhythmic genes in a chosen dataset, inspect expression patterns, highlight functional enrichment terms, and view rhythmic genes on STRING protein–protein interaction networks coloured by peak time.
• Dataset Comparison — compare rhythmic transcriptomes across tissues and conditions, test whether overlaps differ significantly from random chance, and examine whether shared genes retain coherent clock-relative phases.

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Data sources

BodyClocks contains pre-computed circadian transcriptomic datasets, including chondrocyte and cartilage time courses used to study how different zeitgebers shape clock-controlled gene output. Additional datasets are curated from public repositories such as NCBI GEO and EBI ArrayExpress. Accession numbers and original publications for included datasets are listed on the Available Datasets page.

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Rhythmicity analysis

Expression data are quality controlled and normalised before rhythmicity testing. For RNA-seq time courses processed as part of this project, low-expression genes are filtered and count data are normalised in R before transformation. Rhythmic genes are identified with RAIN using a 24-hour target period and the sampling design of each dataset. Multiple testing is controlled with the Benjamini-Hochberg procedure, and genes passing the selected BH.Q threshold are treated as rhythmic. For all the mouse datasets the BH.Q threshold was set at 0.05 while for the baboon datasets threshold was set at p value of 0.005 (BH.Q < 0.1036) due to smaller sample sizes as described in the original paper. Expression profiles in the app are fitted with nonlinear sine and cosine models to visualise oscillatory patterns and estimate peak timing.

Pathway enrichment

Functional enrichment and protein–protein interaction networks are pre-computed with STRINGdb. Enrichment is assessed across GO Biological Process, KEGG, Reactome, and WikiPathways categories, with terms linked back to rhythmic genes in the network. Selecting an enriched term highlights the corresponding genes, making it possible to see whether a pathway is represented by connected proteins and whether those genes peak at similar circadian times.

Overlap statistics

The Dataset Comparison module tests whether overlap between two rhythmic gene lists is greater than expected by chance using one-sided hypergeometric tests. It reports observed and expected overlap, fold enrichment, and related summary statistics. For shared genes, peak phases are compared on a circular 24-hour scale and can also be expressed relative to Bmal1 to evaluate whether common rhythmic genes retain coherent clock-relative timing.

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How to cite

If you use BodyClocks in your research, please cite it. A manuscript describing the chondrocyte synchronisation study and the BodyClocks platform is in preparation. Until it is published, please cite the GitHub repository:

For data and figures generated using BodyClocks, please also credit the original dataset publication(s) listed on the Available Datasets page.

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License

BodyClocks uses two licenses depending on the type of content:

Note that the underlying datasets are subject to the terms of their original publications and repositories. BodyClocks does not redistribute raw data.

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Contact

This project was develped by the Meng Lab at the University of Manchester. Questions, bug reports, or collaboration enquiries can be sent using the form below.
Issues and feature requests can also be submitted via GitHub Issues.

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