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RhythmicDB: A Database of Predicted Multi-Frequency Rhythmic Transcripts.

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Summary
This summary is machine-generated.

Researchers analyzed time-dependent gene expression data to identify rhythmic transcripts. This analysis, using BioCycle and MetaCycle, revealed circadian and ultradian patterns across species, now available in RhythmicDB.

Keywords:
circadian regulationclock genesgene expressionperiodicitytime-course data

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Area of Science:

  • Chronobiology
  • Genomics
  • Bioinformatics

Background:

  • Organismal physiology and behavior exhibit temporal variations driven by evolved molecular clocks.
  • High-throughput experiments have generated extensive data on time-dependent gene expression across diverse species and conditions.

Purpose of the Study:

  • To systematically analyze existing high-throughput gene expression data.
  • To identify and catalog circadian and ultradian transcripts across different species.
  • To make this curated data publicly accessible.

Main Methods:

  • Data retrieval and manual curation of time-dependent gene expression experiments.
  • Analysis using two specialized software packages: BioCycle and MetaCycle.
  • Inference of rhythmic transcripts (circadian and ultradian).

Main Results:

  • Identification of numerous time-dependent gene expression patterns.
  • Inference of circadian and ultradian transcripts across various species.
  • Establishment of the RhythmicDB database for public access.

Conclusions:

  • A comprehensive resource of rhythmic transcripts has been compiled.
  • The findings facilitate further research into the mechanisms and roles of biological rhythms.
  • Public availability of RhythmicDB promotes reproducibility and discovery in chronobiology.