Asymmetric conservation within pairs of co-occurred motifs mediates weak direct binding of transcription factors in chip-seq data

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(1) Background: Transcription factors (TFs) are main regulators of eukaryotic gene expression. The cooperative binding to genomic DNA of at least two TFs is the widespread mechanism of transcription regulation. Cooperating TFs can be revealed through the analysis of co-occurrence of their motifs. (2) Methods: We applied the motifs co-occurrence tool (MCOT) that predicted pairs of spaced or overlapped motifs (composite elements, CEs) for a single ChIP-seq dataset. We improved MCOT capability for the prediction of asymmetric CEs with one of the participating motifs possessing higher conservation than another does. (3) Results: Analysis of 119 ChIP-seq datasets for 45 human TFs revealed that almost for all families of TFs the co-occurrence with an overlap between motifs of target TFs and more conserved partner motifs was significantly higher than that for less conserved partner motifs. The asymmetry toward partner TFs was the most clear for partner motifs of TFs from the ETS (E26 Transformation Specific) family. (4) Conclusion: Co-occurrence with an overlap of less conserved motif of a target TF and more conserved motifs of partner TFs explained a substantial portion of ChIP-seq data lacking conserved motifs of target TFs. Among other TF families, conservative motifs of TFs from ETS family were the most prone to mediate interaction of target TFs with its weak motifs in ChIP-seq.

Original languageEnglish
Article number6023
Pages (from-to)1-22
Number of pages22
JournalInternational Journal of Molecular Sciences
Issue number17
Publication statusPublished - 21 Aug 2020


  • Chromatin immunoprecipitation with massively parallel sequencing
  • Classification of transcription factors
  • Composite elements
  • Cooperative binding of transcription factors
  • Direct binding of transcription factors
  • ETS transcription factor family
  • Motifs conservation
  • Overlap of motifs
  • Transcription factors binding sites prediction




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