GREAT:SCAN:multipatterns

GREAT:SCAN:multipatterns analyzes periodic arrangements of regulatory elements across multiple chromosomes to detect and report periodic regions of target genes across transcription factors and regulatory conditions.

Key Features:

• Automated Analysis: Automates detection of regular genomic patterns across multiple chromosomes and experimental conditions.
• Periodic Region Identification: Identifies and reports periodic regions of target genes for different transcription factors or regulatory conditions on each chromosome.
• Integration with GREAT: Integrates with the Genome REgulatory Architecture Tools (GREAT) framework to relate genome architecture to gene expression.
• Genome Layout Analysis: Examines positioning of co-functional genes and their relationship to chromosome architecture.
• Pattern Detection: Systematically detects regular patterns along genomic features using automated analytical methods.
• Machine Learning Integration: Employs a multi-view machine learning approach that leverages periodicity and positional information to enhance prediction of transcription factor binding sites.

Scientific Applications:

• Regulatory periodicity discovery: Identification and reporting of periodic patterns associated with gene regulation on each chromosome.
• Comparative analysis across factors and conditions: Evaluation of periodic regions of target genes across different transcription factors and regulatory conditions concurrently.
• Genome architecture studies: Assessment of relationships between genome layout, positioning of co-functional genes, and gene expression using the GREAT framework.
• Transcription factor binding site prediction: Improvement of transcription factor binding site prediction by integrating periodicity and positional information in machine learning models.

Methodology:

Computational steps include genome layout analysis to examine positioning of co-functional genes, systematic pattern detection along genomic features, and a multi-view machine learning approach that leverages periodicity and positional information to predict transcription factor binding sites.