DMINDA

DMINDA identifies and analyzes cis-regulatory motifs in DNA promoter sequences to elucidate transcriptional regulation and predict bacterial regulons using phylogenetic footprinting and comparative analyses across complete genomes from over 2125 species (animals, plants, and bacteria).

Key Features:

• De Novo Motif Finding: Performs de novo motif discovery on promoter sequences and assigns statistical scores for predicted motifs based on information derived from control sets.
• Motif Scanning: Scans genomic sequences to locate instances of query motifs within specified genomic contexts.
• Motif Comparison and Clustering: Compares and clusters identified motifs to assess motif similarity and functional relationships across datasets.
• Co-occurrence Analyses: Analyzes co-occurrence of motifs within promoter sequences to infer potential interactions between regulatory elements.
• Phylogenetic Footprinting Framework: Incorporates phylogenetic footprinting to enhance motif prediction by leveraging evolutionary conservation across species.
• Bacterial Regulon Prediction and Visualization: Predicts and visualizes bacterial regulons to support reconstruction and interpretation of gene regulatory networks.
• Extensive Genomic Support: Supports analyses using complete genomes from over 2125 species, including animals, plants, and bacteria.

Scientific Applications:

• Prokaryotic transcriptional regulation: Elucidating transcriptional regulation mechanisms in prokaryotic genomes by identifying regulatory motifs and regulons.
• Evolutionary conservation analysis: Exploring evolutionary conservation of regulatory elements via phylogenetic footprinting.
• Gene regulatory network prediction: Predicting bacterial gene regulatory networks through regulon prediction and motif co-occurrence analyses.
• Comparative motif discovery across taxa: Enabling comparative motif discovery and analysis across animals, plants, and bacteria using supported complete genomes.

Methodology:

De novo motif discovery with statistical scoring based on control sets; motif scanning; motif comparison and clustering; co-occurrence analyses; a phylogenetic footprinting framework; bacterial regulon prediction and visualization; analyses supported for complete genomes from over 2125 species; computations performed on a backend cluster with over 150 computing nodes.