chromstaR

chromstaR infers combinatorial chromatin state dynamics from ChIP-seq data using a multivariate Hidden Markov Model (HMM) to assign discrete chromatin states based on the presence or absence of histone modifications across multiple conditions.

Key Features:

• Multivariate HMM: Employs a multivariate Hidden Markov Model (HMM) to identify distinct combinatorial chromatin states from multiple ChIP-seq experiments.
• Combinatorial State Inference: Assigns genomic regions to discrete chromatin states by evaluating the presence or absence of post-translational histone modifications across conditions such as cell types, treatments, and developmental stages.
• Single-tissue Analysis: Characterizes how different histone modifications combine to form chromatin states within a single tissue type.
• Comparative Analysis: Compares genome-wide patterns of combinatorial state differences between two cell types or conditions.
• Developmental Dynamics: Analyzes temporal changes in chromatin states across multiple time points in complex processes such as tissue differentiation.
• Sparsity and Temporal Dynamics Detection: Reveals sparsity in the organization and temporal dynamics of chromatin state maps to inform regulatory mechanism studies.

Scientific Applications:

• Epigenetics: Infers combinatorial histone modification patterns to study epigenetic regulation of gene expression.
• Developmental Biology: Tracks chromatin state dynamics during tissue differentiation and developmental time courses.
• Disease Research: Compares chromatin state differences between conditions to investigate disease-associated regulatory changes.

Methodology:

Processes ChIP-seq data using a multivariate Hidden Markov Model (HMM) to infer discrete combinatorial chromatin states by evaluating presence or absence of histone modifications across multiple conditions.