BOMA

BOMA performs comparative gene expression alignment between brain tissues and organoids using global alignment and manifold learning to analyze developmental and single-cell RNA-seq datasets.

Key Features:

• Global Alignment: Performs comprehensive global alignment of developmental gene expression datasets from brain tissues and organoids.
• Manifold Learning for Local Refinement: Applies manifold learning for local refinement of alignments to resolve conserved and specific developmental trajectories across brain regions and organoids.
• Comparative Analysis Across Species: Aligns non-human primate and human brain datasets to reveal highly conserved gene expression profiles around birth.
• Integration with Single-Cell RNA Sequencing (scRNA-seq): Integrates scRNA-seq data from human brains and organoids to identify conserved and organoid-specific cell trajectories and clusters.
• Functional Insights through Enrichment Analyses: Identifies expressed genes within clusters and conducts enrichment analyses to infer brain- or organoid-specific developmental functions and pathways.
• Experimental Validation: Supports experimental validation of key findings using immunofluorescence.

Scientific Applications:

• Developmental biology: Compare developmental gene expression programs between brain tissues and organoids to study the molecular basis of brain development.
• Neuroscience: Identify region-specific gene expression programs and cellular trajectories relevant to brain region development.
• Regenerative medicine: Inform regenerative medicine research by comparing gene expression between brains and organoids to identify relevant developmental programs.
• Evolutionary conservation: Explore conserved gene regulation by aligning non-human primate and human datasets to detect conserved developmental processes.

Methodology:

BOMA implements a two-step computational procedure: global alignment of developmental gene expression datasets followed by local refinement via manifold learning, and integrates scRNA-seq data to identify clusters and perform gene-level enrichment analyses.