S3V2-IDEAS

S3V2-IDEAS performs normalization, denoising, and integration of epigenomic data to standardize average read-count signals, reduce technical noise, and assign epigenetic states across multiple cell types.

Key Features:

• Normalization: Applies S3norm ver2 to standardize average read-count signals across epigenomic datasets.
• Denoising and Integration: Uses IDEAS (Integrative Dimensionality Reduction for Epigenetic States) to reduce noise and integrate multi-dimensional epigenomic signals to learn and assign epigenetic states.
• Genome Segmentations and Master Peak Lists: Performs genome segmentation and generates master peak lists across multiple datasets to identify consistent signal patterns and regulatory elements.
• Input Flexibility: Accepts bigWig files as input for processing genomic signal data.

Scientific Applications:

• Large-scale epigenomic studies: Facilitates normalization and integration in projects such as VISION (ValIdatedSystematicIntegratiON) focused on hematopoiesis.
• Epigenetic state identification: Identifies epigenetic states across multiple features or signal-intensity states.
• Master peak list generation: Produces master peak lists for single features across different cell types to support comparative regulatory analyses.

Methodology:

Normalization using S3norm ver2 followed by integrative dimensionality reduction with IDEAS to learn and assign epigenetic states.