Unraveling the palindromic and nonpalindromic motifs of retroviral integration site sequences by statistical mixture models

  1. Jiří Hejnar1
  1. 1Laboratory of Viral and Cellular Genetics, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague 4, 142 20, Czech Republic;
  2. 2Pattern Recognition Department, Institute of Information Theory and Automation of the Czech Academy of Sciences, Prague 8, 182 08, Czech Republic
  • Corresponding author: jiri.hejnar{at}img.cas.cz
  • Abstract

    A weak palindromic nucleotide motif is the hallmark of retroviral integration site alignments. Given that the majority of target sequences are not palindromic, the current model explains the symmetry by an overlap of the nonpalindromic motif present on one of the half-sites of the sequences. Here, we show that the implementation of multicomponent mixture models allows for different interpretations consistent with the existence of both palindromic and nonpalindromic submotifs in the sets of integration site sequences. We further show that the weak palindromic motifs result from freely combined site-specific submotifs restricted to only a few positions proximal to the site of integration. The submotifs are formed by either palindrome-forming nucleotide preference or nucleotide exclusion. Using the mixture models, we also identify HIV-1-favored palindromic sequences in Alu repeats serving as local hotspots for integration. The application of the novel statistical approach provides deeper insight into the selection of retroviral integration sites and may prove to be a valuable tool in the analysis of any type of DNA motifs.

    Footnotes

    • [Supplemental material is available for this article.]

    • Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.277694.123.

    • Freely available online through the Genome Research Open Access option.

    • Received January 13, 2023.
    • Accepted July 12, 2023.

    This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

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