ChroMo

ChroMo performs unsupervised analysis of chromosome movements (CMs) during meiotic prophase to characterize temporal dynamics, recurring motifs, and causal relationships from high-throughput live-cell imaging datasets.

Key Features:

• Unsupervised analysis: Performs label-free exploration of CMs to discover patterns and relationships without pre-labeled data.
• High-throughput live-cell imaging support: Processes large datasets derived from high-throughput live-cell imaging experiments of chromosome dynamics.
• Time-series segmentation: Implements time-series segmentation methods to dissect temporal phases of chromosome trajectories.
• Motif discovery: Identifies recurring movement motifs in chromosome trajectories using motif discovery algorithms.
• Causality network assessment: Assesses causality networks to evaluate potential causal links between chromosome morphology and trajectory.
• Visualization of time-series and trajectories: Provides visualization tools for interpreting time-series data and relationships among variables in chromosome movement datasets.

Scientific Applications:

• Modeling chromosome movements in model organisms: Analyzes CMs in organisms such as fission yeast to model movement dynamics and variations caused by genetic mutations or environmental stressors relevant to homologous chromosome pairing and recombination.
• Understanding gametogenesis: Uncovers features of CMs that inform how defects in chromosome dynamics affect gametogenesis, fertility, and genetic stability.
• Cross-species comparative analysis: Applies methodologies to a broad range of opisthokonts and plants for comparative studies of meiotic chromosome behavior across species.

Methodology:

Processes large datasets from high-throughput live-cell imaging using unsupervised learning approaches, time-series segmentation, motif discovery algorithms, and causality network assessments.