motif-x

motif-x identifies statistically significant motifs in large sequence datasets to detect overrepresented sequence patterns, including MS/MS post-translational modification data and protein groups with shared biological functions.

Key Features:

• Statistical motif discovery: Identifies statistically significant, overrepresented sequence motifs in large datasets.
• Support for MS/MS and protein groups: Processes MS/MS post-translational modification data and protein groups with shared biological functions.
• Iterative algorithm: Employs an iterative algorithmic strategy to construct successive motifs.
• Dynamic statistical background: Compares candidate motifs against a dynamically adjusted statistical background to assess significance.
• Output representations and statistics: Produces syntactic and image-based motif representations along with detailed statistical analyses.

Scientific Applications:

• Post-translational modification analysis: Identifying overrepresented motifs associated with MS/MS-detected PTMs to infer modification-specific sequence patterns.
• Protein functional group analysis: Detecting common sequence motifs among proteins sharing biological functions to highlight conserved regulatory or functional elements.
• Regulatory and mechanistic insight: Revealing overrepresented patterns that may indicate functional or regulatory elements relevant to molecular mechanisms and potential therapeutic targets.

Methodology:

Uses an iterative approach that progressively builds motifs by comparing candidate motifs against a dynamically adjusted statistical background to identify statistically significant motifs.