Detecting correlations among functional-sequence motifs

Davide Pirino, Jacopo Rigosa, Alice Ledda, Luca Ferretti

    Research output: Contribution to journalArticleResearchpeer-review


    Sequence motifs are words of nucleotides in DNA with biological functions, e.g., gene regulation. Identification of such words proceeds through rejection of Markov models on the expected motif frequency along the genome. Additional biological information can be extracted from the correlation structure among patterns of motif occurrences. In this paper a log-linear multivariate intensity Poisson model is estimated via expectation maximization on a set of motifs along the genome of E. coli K12. The proposed approach allows for excitatory as well as inhibitory interactions among motifs and between motifs and other genomic features like gene occurrences. Our findings confirm previous stylized facts about such types of interactions and shed new light on genome-maintenance functions of some particular motifs. We expect these methods to be applicable to a wider set of genomic features. © 2012 American Physical Society.
    Original languageEnglish
    Article number066124
    JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
    Issue number6
    Publication statusPublished - 19 Jun 2012


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