WebMOTIFS

WebMOTIFS facilitates discovery, scoring, analysis, and visualization of DNA-sequence motifs to support studies of transcriptional regulation.

Key Features:

• Integrated de novo motif discovery: Combines multiple de novo motif discovery programs including AlignACE, MDscan, MEME, and Weeder.
• Ensemble algorithm approach: Employs multiple motif discovery algorithms simultaneously to improve motif identification precision.
• Randomized control calculations: Uses randomized control calculations to assess motif significance and reduce false positives.
• Bayesian motif analysis: Applies Bayesian methods to infer the most likely class of transcription factors regulating a set of sequences.
• THEME integration for ChIP-chip analysis: Integrates the THEME program to analyze genome-wide chromatin-immunoprecipitation (ChIP-chip) data with constrained local optimization and cross-validation.
• Scoring and visualization: Provides scoring, analysis, and visualization of identified DNA-sequence motifs.
• Cross-species motif identification: Default settings have been shown to identify biologically relevant motifs across diverse species.

Scientific Applications:

• De novo motif discovery: Identification of sequence motifs in genomic datasets using multiple discovery algorithms.
• Transcription factor class inference: Determination of likely transcription factor classes regulating input sequences via Bayesian analysis.
• Genome-wide ChIP-chip analysis: Analysis and motif hypothesis refinement for ChIP-chip datasets using THEME.
• Protein–DNA specificity refinement: Refinement of hypotheses about protein sequence specificity through constrained local optimization and cross-validation.
• Cross-species motif analysis: Detection of biologically relevant motifs across species, as demonstrated on 14 human and mouse proteins spanning 36 domain families.

Methodology:

Integrates de novo motif discovery programs AlignACE, MDscan, MEME, and Weeder; employs randomized control calculations and Bayesian methods for motif analysis; and applies THEME with constrained local optimization and cross-validation for ChIP-chip data.