Cyclebase

Cyclebase integrates and standardizes genome-wide cell-cycle mRNA expression profiling data from synchronously growing cell cultures to enable comparative analysis of gene expression and regulatory events across organisms.

Key Features:

• Comprehensive Data Integration: Consolidates genome-wide mRNA expression profiling data from multiple cell-cycle experiments, including synchronously growing cell cultures.
• Normalization to Common Timescale: Normalizes experiments to a common temporal framework accounting for variations in interdivision times and relative duration of cell-cycle phases.
• Visualization of Gene-Centric Profiles: Generates gene-centric graphs that summarize cell-cycle experiments and expression profiles.
• Annotation of Regulatory Signals and Phenotypes: Provides annotations and derived analysis results including cyclin-dependent kinase (CDK) substrates, predicted degradation signals, and loss-of-function phenotypes from genome-wide screens.
• Cross-Species Linking: Links orthologous and paralogous genes to support comparative studies of cell-cycle regulation across species.

Scientific Applications:

• Cell-cycle mechanism analysis: Dissect molecular mechanisms of cell-cycle regulation by integrating expression profiles with regulatory annotations.
• Regulatory molecule identification: Identify key regulatory molecules and phase-specific regulators, including cyclin-dependent kinase (CDK) substrates.
• Comparative evolutionary studies: Compare cell-cycle dynamics across organisms to uncover conserved molecular pathways.
• Expression impact assessment: Analyze gene expression profiles to assess the impact of specific genetic modifications or environmental conditions on cell division.

Methodology:

Integrates genome-wide mRNA expression profiling experiments and aligns them to a common timescale by normalizing for interdivision time and relative phase-duration differences, and aggregates annotations and derived analysis results such as CDK substrates, predicted degradation signals, and loss-of-function phenotypes from genome-wide screens.