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Web-MCOT Server for Motif Co-Occurrence Search in ChIP-Seq Data.

Victor G Levitsky1,2, Alexey M Mukhin1, Dmitry Yu Oshchepkov1

  • 1Department of System Biology, Institute of Cytology and Genetics, 630090 Novosibirsk, Russia.

International Journal of Molecular Sciences
|August 26, 2022
PubMed
Summary

We developed Web-MCOT, a tool to find co-occurring transcription factor motifs in ChIP-seq data. This helps identify cis-regulatory modules and plan future experiments.

Keywords:
chromatin immunoprecipitation with massively parallel sequencingco-binding of transcription factorscomposite elementsmotifs conservationoverlap of motifstranscription factor binding sitestranscription factors binding sites prediction

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • ChIP-seq technology is widely used for genome-wide transcription factor binding site identification.
  • Annotation of cis-regulatory modules relies on identifying co-occurring DNA motifs.
  • Existing methods may not fully capture the complex relationships between motifs.

Purpose of the Study:

  • To introduce the Web-MCOT (Web-Motifs Co-Occurrence Tool) web server.
  • To detect composite elements (CEs) representing overrepresented motif pairs in ChIP-seq data.
  • To uncover motif similarities and structural relationships within pairs.

Main Methods:

  • Web-MCOT analyzes single ChIP-seq datasets to find homo- and heterotypic motif pairs.
  • It considers motifs with spacers, overlaps, and various orientations.
  • The tool allows user-defined or library-based partner motifs, anchored by the ChIP-seq target transcription factor.

Main Results:

  • Web-MCOT calculates CE significance, with and without motif conservation.
  • Results include histograms of CE abundance by orientation and length, motif overlap logos, and conservation heatmaps.
  • The tool visualizes CE structural types and abundance based on motif conservation.

Conclusions:

  • Web-MCOT offers novel capabilities for extracting maximal information on motif co-occurrence from ChIP-seq data.
  • It aids in understanding cis-regulatory module organization.
  • The tool can inform the design of subsequent ChIP-seq experiments.