epigraHMM

epigraHMM provides a set of tools for the analysis of epigenomic data based on hidden Markov Models, containing two separate peak callers for consensus peaks from biological or technical replicates and for differential peaks from multi-replicate multi-condition experiments.

Key Features:

• Consensus peak calling: Identifies consensus peaks from biological or technical replicates using hidden Markov Models.
• Differential peak calling: Identifies differential peaks from multi-replicate multi-condition experiments using hidden Markov Models.
• Combinatorial posterior probabilities: Provides window-specific posterior probabilities associated with every possible combinatorial pattern of read enrichment across conditions in differential peak calling.

Scientific Applications:

• Epigenomic data analysis: Enables analysis of epigenomic data through probabilistic modeling of read enrichment patterns.
• Replicate integration: Supports integration of biological or technical replicates for robust peak detection.
• Multi-condition comparison: Facilitates comparison of read enrichment across multiple conditions with combinatorial pattern detection.

Methodology:

epigraHMM is based on hidden Markov Models.