Bharath Ananthasubramaniam

The paper benchmarks 12 differential rhythmicity methods using synthetic RNA-seq data to guide method selection for time series analysis.

rhythmCI provides an R package for detecting circadian rhythms using harmonic regression confidence intervals, improving rhythmicity analysis in small clinical datasets.

This study identifies divergent molecular and circuit mechanisms for light entrainment in retinal and SCN circadian clocks using mouse models and transcriptomic analysis.

COFE, a machine learning method, reconstructs circadian rhythms from single-omics samples in cancer data, revealing conserved gene expression patterns and drug target rhythms.

This study identifies PER proteins' role in regulating circadian alternative splicing in the liver using long-read RNA sequencing and mass spectrometry.

This study shows that dietary methionine/choline deficiency disrupts circadian rhythms and gene expression in mice, with effects dependent on circadian time.

The paper uses gene expression signatures to predict circadian rhythms in EGFR, PI3K, and p53 pathways in mouse liver across multiple datasets.

This study examines circadian gene expression variability in human skin layers and identifies layer-specific biomarkers for internal clock phase.

The paper shows that Venn Diagram analysis overestimates differentially rhythmic transcripts and introduces an R-package to improve accuracy in circadian rhythm studies.

The paper develops conceptual models of circadian entrainment using global optimization to explain phenotypic features like phase response curves and seasonal variability.

The paper shows that minimal clock models can reproduce essential circadian phenotypes like fast jetlag and seasonal shifts through amplitude effects and twist mechanisms.