meme_meme

meme_meme identifies and analyzes sequence motifs in DNA and protein sequences to characterize binding sites and functional domains.

Key Features:

• MEME: Identifies motifs by fitting a two-component finite mixture model to sequence data.
• GLAM2: Discovers motifs that contain gaps to detect complex, gapped patterns.
• MAST, FIMO, GLAM2SCAN: Scans DNA and protein sequence databases for occurrences of discovered motifs.
• TOMTOM: Compares discovered motifs to entries in motif databases for similarity assessment.
• GOMO: Associates motifs with Gene Ontology (GO) terms to predict potential functions.
• Sequence LOGOs: Generates sequence LOGO visualizations for each discovered motif.
• Integration: Enables passing motifs from MEME or GLAM2 to MAST, FIMO, TOMTOM, or GOMO for downstream analyses.
• Web services: Implements tools as web services via Opal.

Scientific Applications:

• Motif discovery: Detection of conserved sequence motifs in DNA and protein datasets.
• Transcription factor binding site identification: Characterization of DNA motifs corresponding to transcription factor binding sites.
• Protein interaction domain detection: Identification of protein sequence motifs linked to interaction domains.
• Functional annotation: Prediction of motif-associated functions through Gene Ontology term associations.
• Genome- and proteome-wide scanning: Large-scale identification of motif occurrences across sequence databases.
• Comparative motif analysis: Matching experimentally or computationally discovered motifs against motif database entries.

Methodology:

Computational methods include fitting a two-component finite mixture model (MEME), gapped motif discovery (GLAM2), sequence scanning with MAST/FIMO/GLAM2SCAN, motif comparison with TOMTOM, GO association with GOMO, generation of sequence LOGOs, and deployment as web services via Opal.