MET

MET identifies transcription factor DNA-binding motifs statistically enriched in regulatory regions of user-defined gene sets to implicate transcriptional regulators across metazoan species including human, mouse, Drosophila, planaria, and flowering plants.

Key Features:

• User-defined gene set analysis: Analyzes regulatory regions surrounding each gene in a provided gene set to assess motif occurrences.
• Motif enrichment detection: Pinpoints transcription factor DNA-binding specificities (motifs) that are statistically overrepresented in analyzed regulatory regions.
• Integration of high-throughput datasets: Incorporates ChIP-seq and DNase-seq datasets from ENCODE and ModENCODE to inform regulatory target identification.
• Curated motif sources: Leverages curated transcription factor DNA-specificity motifs from literature, bacterial one-hybrid assays, high-throughput SELEX, and protein binding microarrays.
• Cross-species support: Supports motif enrichment analysis across multiple model organisms, including human, mouse, Drosophila (fruit fly), planaria, and flowering plants.

Scientific Applications:

• Transcriptional regulator identification: Implicates candidate transcription factors associated with co-regulated gene sets via motif enrichment.
• Regulatory network elucidation: Aids reconstruction of regulatory interactions and networks governing gene expression in diverse metazoans.
• Integration of functional genomics: Combines motif information with ChIP-seq and DNase-seq evidence to refine predictions of regulatory targets.

Methodology:

Analyzes regulatory regions around genes in user-defined sets, identifies motifs that are statistically overrepresented, incorporates ENCODE and ModENCODE ChIP-seq and DNase-seq datasets, and uses curated TF DNA-specificity motifs from literature, bacterial one-hybrid assays, high-throughput SELEX, and protein binding microarrays.