Starr

Starr provides R/Bioconductor functions to process, analyze, and visualize ChIP-chip (Chromatin Immunoprecipitation microarray) data for identification of protein–DNA binding sites and chromatin or histone modifications.

Key Features:

• Data Import and Quality Assessment: Imports data from various microarray platforms into standard Bioconductor classes and performs rigorous quality assessment.
• Data Visualization and Exploration: Provides visualization functions to explore ChIP-chip signal distributions and binding patterns.
• Alignment with Annotated Features: Aligns ChIP signals along annotated genomic features to contextualize binding events.
• Correlation Analysis: Computes correlations between ChIP signals and complementary genomic datasets such as gene expression profiles.
• Peak-Finding (CMARRT): Detects peaks in ChIP-chip data using the CMARRT algorithm to identify regions of significant binding or modification.
• Comparative Display: Enables comparative analysis and visualization of multiple clusters of binding profiles across experiments, genetic backgrounds, or experimental conditions.
• Microarray Probe Annotation Update: Remaps probe sequences to specified genome versions and updates microarray probe annotation files.

Scientific Applications:

• Binding Site Identification: Identify and localize genomic regions bound by specific proteins or marked by chromatin or histone modifications.
• Comparative Binding Profiling: Compare binding profiles across related proteins, different experimental conditions, or genetic backgrounds.
• Regulatory Mechanism Investigation: Investigate relationships between DNA-binding events and gene expression to study regulatory effects.
• Functional Genomics Integration and Group Analysis: Integrate and statistically analyze binding profiles with complementary functional genomics data for systematic assessment of binding behavior across groups of genes aligned to arbitrary genomic features.

Methodology:

Computational methods explicitly include data import into Bioconductor classes, quality assessment, visualization, alignment of ChIP signals to annotated genomic features, correlation analysis with gene expression, peak detection using CMARRT, comparative clustering and display of binding profiles, and remapping of microarray probe sequences to specified genome versions.