cycle

cycle evaluates the statistical significance of periodic gene expression by applying Fourier transformation and comparing statistical background models.

Key Features:

• Fourier Analysis: Utilizes Fourier transformation techniques to detect periodic patterns in time-series gene expression data.
• Background Models Comparison: Employs different statistical background models to assess the significance of detected periodic expressions and to address inconsistencies in previous analyses.
• Microarray Reanalysis: Reanalyzes existing microarray datasets, including yeast cell cycle studies, to evaluate how background model choice affects reported periodicity.

Scientific Applications:

• Cell Cycle and Circadian Rhythms: Identifies genes exhibiting periodic expression associated with the cell cycle and circadian clock processes.
• System-wide Transcript Screening: Facilitates genome- or transcriptome-scale screening of microarray time-series to detect periodically expressed genes.

Methodology:

Applies Fourier transformation to time-series expression data, compares statistical significance across alternative background models, and performs comparative reanalysis of microarray datasets such as yeast cell cycle studies.