Modular discovery of monomeric and dimeric transcription factor binding motifs for large data sets

Research output: Contribution to journalArticleScientificpeer-review

Researchers

  • Jarkko Toivonen
  • Teemu Kivioja
  • Arttu Jolma
  • Yimeng Yin
  • Jussi Taipale
  • Esko Ukkonen

Research units

  • Helsinki Institute for Information Technology HIIT
  • Aalto University
  • University of Helsinki
  • Karolinska Institutet
  • University of Cambridge

Abstract

In some dimeric cases of transcription factor (TF) binding, the specificity of dimeric motifs has been observed to differ notably from what would be expected were the two factors to bind to DNA independently of each other. Current motif discovery methods are unable to learn monomeric and dimeric motifs in modular fashion such that deviations from the expected motif would become explicit and the noise from dimeric occurrences would not corrupt monomeric models. We propose a novel modeling technique and an expectation maximization algorithm, implemented as software tool MODER, for discovering monomeric TF binding motifs and their dimeric combinations. Given training data and seeds for monomeric motifs, the algorithm learns in the same probabilistic framework a mixture model which represents monomeric motifs as standard position-specific probability matrices (PPMs), and dimeric motifs as pairs of monomeric PPMs, with associated orientation and spacing preferences. For dimers the model represents deviations from pure modular model of two independent monomers, thus making co-operative binding effects explicit. MODER can analyze in reasonable time tens of Mbps of training data. We validated the tool on HT-SELEX and ChIP-seq data. Our findings include some TFs whose expected model has palindromic symmetry but the observed model is directional.

Details

Original languageEnglish
Number of pages16
JournalNucleic Acids Research
Volume46
Issue number8
Publication statusPublished - 4 May 2018
MoE publication typeA1 Journal article-refereed

    Research areas

  • CHIP-SEQ DATA, EXPECTATION MAXIMIZATION ALGORITHM, EM ALGORITHM, DNA-BINDING, HUMAN GENOME, SITES, SEQUENCE, IDENTIFICATION, SPECIFICITIES, ALIGNMENT

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