SimSearch

SimSearch identifies and compares (epi)genomic feature patterns across genome-wide browser tracks to detect and rank genomic regions similar to a query pattern for comparative genomics and epigenomics analyses.

Key Features:

• Pattern-search algorithm: An optimized pattern-search algorithm accepts a specific genomic region pattern and identifies similar patterns throughout the entire genome.
• Genome-wide extraction and ranking: Extracts and ranks genomic regions based on similarity to the query pattern to prioritize candidate loci.
• Cross-track comparison: Compares (epi)genomic feature patterns across multiple genome browser tracks to support comparative analyses.
• Visual analytics computation: Implements efficient visual analytics computation suitable for handling large datasets.
• Annotation and enrichment: Provides functions for annotating matched regions and performing enrichment analyses to enhance biological interpretation.
• Quickload repository access: Leverages a Quickload repository to access epigenomic feature datasets from ENCODE and Roadmap Epigenomics.

Scientific Applications:

• Genome-wide pattern discovery: Detects occurrences of a query genomic pattern across the whole genome for discovery of similar loci.
• Comparative epigenomics: Enables comparison of epigenomic feature patterns across tracks to identify shared or distinct regulatory signatures.
• Candidate region prioritization: Ranks matched regions to prioritize targets for downstream experimental validation or analysis.
• Annotation-driven interpretation: Facilitates annotation and enrichment analyses to interpret the biological relevance of matched regions.
• Integration with public epigenomic datasets: Uses ENCODE and Roadmap Epigenomics datasets to contextualize pattern matches within large reference epigenomic collections.

Methodology:

An optimized pattern-search algorithm scans the genome for regions similar to a supplied genomic region pattern; efficient visual analytics computation handles large datasets; functions for annotating and enriching results are applied; epigenomic feature datasets are accessed via a Quickload repository referencing ENCODE and Roadmap Epigenomics.