cERMIT

cERMIT identifies regulatory sequence motifs by integrating genome-wide quantitative regulatory evidence from large-scale ChIP, ChIP-seq, ChIP-chip, and RNA immunoprecipitation datasets.

Key Features:

• Computationally efficient algorithm: Employs suffix arrays to process tens of thousands of sequences from ChIP and RNA immunoprecipitation experiments.
• Rank-order statistics: Incorporates rank-order statistics to prioritize motifs by considering their relative positions across sequences.
• Quantitative information integration: Accounts for quantitative data associated with each sequence during motif scoring.
• Scalability: Scales to current mammalian ChIP-seq experiments involving thousands of non-coding regions.

Scientific Applications:

• Direct binding and overexpression studies: Applied to direct binding and overexpression datasets for motif discovery.
• ChIP-chip dataset analysis: Demonstrates superior performance compared to state-of-the-art approaches on curated ChIP-chip datasets.
• Genome-wide regulatory motif discovery: Enables discovery of regulatory motifs across genome-wide quantitative datasets from ChIP, ChIP-seq, and RNA immunoprecipitation experiments.

Methodology:

Uses suffix arrays and rank-order statistics, integrates quantitative sequence-associated data, and searches all sequence regions without pre-selecting candidate sequences to identify high-scoring motifs.