MEME

MEME discovers recurring sequence motifs in groups of related DNA or protein sequences and represents them as position-dependent letter-probability matrices to support motif discovery and regulatory-element analysis.

Key Features:

• Motif representation: Represents motifs as position-dependent letter-probability matrices that quantify the likelihood of each nucleotide or amino acid at each motif position.
• MCAST algorithm for CRM identification: Uses the MCAST algorithm to identify cis-regulatory modules (CRMs) in genomic sequences.
• Hidden Markov model with P-value scoring: MCAST employs a hidden Markov model combined with a P-value-based scoring scheme to pinpoint candidate CRMs.
• Dynamic background model: Adapts the background sequence model dynamically to improve motif detection accuracy.
• Statistical confidence estimates: Provides false discovery rate estimation to quantify statistical confidence in identified CRMs.
• Integration with epigenomic data: Incorporates epigenomic priors such as DNase I sensitivity and histone modification data to refine CRM predictions.
• Improved graphical output: Generates enhanced visualizations for motif and CRM results.

Scientific Applications:

• Motif discovery: Detects recurring sequence patterns in DNA and protein sequence datasets for downstream functional analysis.
• CRM identification: Identifies candidate cis-regulatory modules (CRMs) that may coordinate transcriptional regulation.
• Regulatory element prediction: Integrates sequence-based motifs with epigenomic priors to improve prediction of regulatory elements.
• Gene regulation studies: Supports investigation of transcriptional control mechanisms via motif and CRM analysis.

Methodology:

Represents motifs with position-dependent letter-probability matrices; MCAST applies a hidden Markov model combined with a P-value-based scoring scheme; uses a dynamic background model; estimates statistical significance via false discovery rate; integrates epigenomic priors such as DNase I sensitivity and histone modification data.