EpiAlign

EpiAlign aligns chromatin state sequences derived from multi-track epigenomic signals to detect locally recurrent patterns and compare epigenomes for functional or evolutionary insights.

Key Features:

• Local alignment algorithm: leverages dynamic programming to identify locally aligned regions between chromatin state sequences.
• Multi-track input: operates on chromatin state sequences learned from multi-track epigenomic signals.
• Variable-length and frequency handling: accounts for varying lengths and frequencies of chromatin states in alignment scoring.
• Recurrent pattern extraction: extracts recurrent patterns within a single epigenome, including cell-type-specific patterns.
• Cross-epigenome pattern detection: detects common chromatin state patterns across multiple epigenomes.
• Comparative grouping: enables grouping and distinguishing epigenomic samples based on genome-wide or localized chromatin state patterns.
• Validation: validated using extensive simulations and analyses of real data from the NIH Roadmap Epigenomics project.

Scientific Applications:

• Epigenetic pattern discovery: detect recurrent and cell-type-specific chromatin state patterns from genome-wide epigenomic data.
• Comparative epigenomics: compare chromatin state sequences across samples to identify functional or evolutionary similarities.
• Sample classification: group and distinguish epigenomic samples based on genome-wide or localized chromatin state patterns.
• Chromatin and gene regulation analysis: explore the interplay between epigenetic modifications, chromatin structures, and gene expression.

Methodology:

Compares chromatin state sequences learned from multi-track epigenomic signals using a local alignment algorithm implemented via dynamic programming and assesses results with simulations and analyses of NIH Roadmap Epigenomics project data.