CMF

CMF identifies motifs that are differentially enriched between two datasets of DNA binding sequences to reveal context-dependent transcription factor (TF) binding signals.

Key Features:

• Differential Enrichment Detection: Contrasts two sets of bound sequences to pinpoint motifs that are differentially enriched.
• Context-Dependent Motif Identification: Detects motifs recognized by a TF only in the presence of specific co-regulators by analyzing datasets from collaborative TFs.
• De novo Motif Discovery: Employs a de novo approach to motif identification rather than relying solely on known motif libraries.
• False Positive Mitigation: Accounts for false positive sites when updating position weight matrices (PWMs) and other model parameters.
• High Accuracy and Precision: Demonstrated superior accuracy compared to several established motif finding methods on mouse embryonic stem cell binding datasets.
• Competitive Binding Analysis: Identifies subtle motif signals associated with competitive binding scenarios, exemplified by Sox2 versus Tcf3.

Scientific Applications:

• TF regulatory mechanism analysis: Dissects gene regulation by transcription factors by comparing bound-sequence datasets.
• Context-dependent motif investigation: Investigates how different conditions or co-regulators influence TF binding specificity and affinity.
• Competitive binding dynamics: Detects motifs indicative of competitive interactions between TFs such as Sox2 and Tcf3.
• Motif discovery in mouse embryonic stem cells: Applied to mouse embryonic stem cell binding datasets to reveal regulatory motifs.

Methodology:

Contrasts sequences bound by distinct sets of transcription factors using a de novo motif identification approach and updates PWMs and other model parameters while accounting for false positive sites.