DREME: motif discovery in transcription factor ChIP-seq data.

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Transcription factor (TF) ChIP-seq datasets present unique challenges for motif discovery.
• Existing algorithms often struggle with large datasets and identifying cofactor motifs.

Purpose of the Study:

• To develop a motif discovery algorithm optimized for large ChIP-seq datasets.
• To identify short, core DNA-binding motifs of eukaryotic TFs and their cofactors.

Main Methods:

• Development and application of the DREME (Direct Running-Efficient Motif) algorithm.
• Analysis of mouse ES cell (mESC), mouse erythrocyte, and human cell line ChIP-seq datasets.
• Utilizing discriminative motif discovery to investigate TF interactions.

Main Results:

• DREME rapidly identifies TF and cofactor binding motifs in large ChIP-seq datasets.
• DREME discovered eight cofactor motifs for Esrrb in mESC data, surpassing other algorithms.
• Discriminative motif discovery provided evidence against obligate Sox2 and Oct4 heterodimerization in mES cells.

Conclusions:

• DREME is a fast, scalable, and effective tool for motif discovery in large ChIP-seq data.
• DREME identifies multiple, non-redundant motifs with statistical significance.
• DREME is available as part of the MEME Suite.