coMOTIF: a mixture framework for identifying transcription factor and a coregulator motif in ChIP-seq data

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• ChIP-seq data analysis often requires identifying not only the primary protein binding sites but also those of associated coregulatory factors.
• Understanding these co-occurrences is crucial for deciphering complex gene regulatory networks.

Purpose of the Study:

• To develop and validate a novel computational framework for detecting the simultaneous presence of two transcription factor binding motifs within ChIP-seq sequences.
• To improve the accuracy of motif discovery in ChIP-seq data by considering motif co-occurrence.

Main Methods:

• A finite mixture framework utilizing an expectation-maximization algorithm was developed to jointly analyze two motifs.
• The method was tested on simulated ChIP-seq datasets and a real mouse liver Foxa2 ChIP-seq dataset.

Main Results:

• The proposed method, coMOTIF, demonstrated superior performance compared to repeated MEME analysis in predicting sequences containing both motifs.
• Application to a mouse liver Foxa2 ChIP-seq dataset revealed co-occurrence of Foxa2 motifs with Hnf4a, Cebpa, E-box, Ap1/Maf, or Sp1 motifs in 6-33% of sequences.
• The identified co-occurring motifs are associated with liver-specific functions and transcription factors.

Conclusions:

• coMOTIF provides an effective approach for identifying co-occurring motifs in ChIP-seq data, enhancing the biological interpretation of regulatory elements.
• This method aids in understanding the combinatorial binding of transcription factors and their role in cellular function, particularly in liver-specific gene regulation.