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Methods detecting rhythmic gene expression are biologically relevant only for strong signal.

David Laloum1,2, Marc Robinson-Rechavi1,2

  • 1Department of Ecology and Evolution, Batiment Biophore, Quartier UNIL-Sorge, Université de Lausanne, Lausanne, Switzerland.

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The nycthemeral transcriptome, encompassing all 24-hour rhythmic gene expression, is biologically functional. Evaluating seven rhythm detection methods revealed similar performances, with ARS, empJTK, and GeneCycle recommended for robust analysis.

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Area of Science:

  • Genomics
  • Chronobiology
  • Bioinformatics

Background:

  • The nycthemeral transcriptome includes all genes with daily rhythmic mRNA variation, beyond just circadian genes.
  • Rhythmic gene expression is biologically functional and conserved in orthologous genes.
  • Accurate detection of rhythmic genes is crucial for understanding biological timing.

Purpose of the Study:

  • To evaluate the performance of seven statistical methods for detecting rhythmic gene expression.
  • To assess method consistency and reliability across different tissues and species.
  • To provide recommendations for experimental design and method selection in rhythmicity studies.

Main Methods:

  • Comparison of seven rhythm detection algorithms (ARSER, Lomb Scargle, RAIN, JTK, empirical-JTK, GeneCycle, meta2d) against a naive method.
  • Analysis considering tissue-specificity and inter-species comparisons of rhythmic gene expression.
  • Evaluation using real and randomized gene expression datasets.

Main Results:

  • No single method consistently outperformed others across all conditions; performances were similar.
  • Method consistency was high only for genes with strong rhythm signals; weak signals may be biologically irrelevant.
  • ARS, empJTK, and GeneCycle showed expected p-value distributions.
  • Prioritizing biological replicates over time points, or time points over technique quality, impacts efficiency.
  • GeneCycle and empirical-JTK are robust for low-quality datasets.

Conclusions:

  • Caution is advised when interpreting large sets of rhythmic genes due to potential inclusion of biologically irrelevant rhythms.
  • Method selection and experimental design significantly influence the reliability of rhythm detection.
  • Highly expressed genes are predominantly rhythmic, suggesting a link between expression level and rhythmicity.