DGW

DGW performs simultaneous alignment and clustering of multiple epigenomic marks to improve analysis and representation of complex sequencing-based datasets such as ChIP-seq.

Key Features:

• Simultaneous alignment and clustering: Performs joint alignment and clustering of multiple epigenomic marks across genomic regions.
• Dynamic Time Warping (DTW): Uses Dynamic Time Warping to adaptively rescale and align genomic distances.
• Shape-based grouping: Groups regions of interest by similarity of signal shape to capture intrinsic structure of epigenomic marks.
• Handling high-dimensional, multi-modal data: Targets high-dimensional, sequencing-based epigenomic datasets with multi-modal peaks that extend over extensive genomic regions.
• Feature recognition in real data: Recognizes and aligns genomic features such as transcription start sites and splicing sites based on histone mark profiles from ENCODE data.
• Representation and exploration: Provides a more nuanced representation and exploration of complex epigenomic signal features than standard visualization approaches.
• Validation: Demonstrated effectiveness through simulation studies.

Scientific Applications:

• ChIP-seq analysis: Analysis of ChIP-seq datasets to study DNA–protein interactions using aligned epigenomic mark profiles.
• Genomic feature identification: Identification and alignment of transcription start sites and splicing sites from histone mark signals in ENCODE and similar datasets.
• Peak characterization: Characterization and grouping of complex, multi-modal peak shapes across extended genomic regions.
• Method validation and benchmarking: Validation and benchmarking of epigenomic analysis approaches using simulation studies.

Methodology:

Applies Dynamic Time Warping (DTW) to adaptively rescale and align genomic distances and performs simultaneous alignment and clustering of multiple epigenomic marks; validated on simulation studies and ENCODE histone mark data.