HATSEQ

HATSEQ identifies functional regions of interest (ROIs) in tiling-array and next-generation sequencing data by applying hypergeometric analysis to detect genomic signals that significantly deviate from genome-wide behavior.

Key Features:

• Enhanced specificity: Offers increased specificity in detecting ROIs and precise peak-boundary delineation compared with alternative methods, including analyses of ChIP-Seq data.
• Built-in post-analyses: Provides post-analytical routines for gene pathway associations and de-novo motif analysis to assign biological meaning to detected ROIs.
• Comprehensive visualizations and summaries: Produces visualization outputs and statistical summaries for detected ROIs to support interpretation.
• Integration with external annotations: Relates detected ROIs to external databases for gene ontology, pathway annotation, and genomic content enrichment.

Scientific Applications:

• Protein–DNA interaction mapping: Identification of ROIs from tiling-array and ChIP-Seq experiments to map protein–DNA interactions.
• Transcription factor binding analysis: Detection of ROIs and discovery of de-novo motifs for transcription factor binding studies.
• Gene regulation studies: Delineation of genomic regions implicated in the molecular mechanisms regulating gene expression.
• Annotation and enrichment analysis: Association of ROIs with gene ontology terms, pathways, and genomic content enrichment databases.

Methodology:

HATSEQ applies a hypergeometric analysis to identify regions where genomic signals deviate from genome-wide patterns and implements built-in post-analyses including gene pathway association and de-novo motif analysis; it has been applied to STAT1 ChIP-Seq data to identify ROIs associated with expected binding motifs.