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Discovering DNA shape motifs with multiple DNA shape features: generalization, methods, and validation

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Abstract

DNA motifs are crucial patterns in gene regulation. DNA-binding proteins (DBPs), including transcription factors, can bind to specific DNA motifs to regulate gene expression and other cellular activities. Past studies suggest that DNA shape features could be subtly involved in DNA–DBP interactions. Therefore, the shape motif annotations based on intrinsic DNA topology can deepen the understanding of DNA–DBP binding. Nevertheless, high-throughput tools for DNA shape motif discovery that incorporate multiple features altogether remain insufficient. To address it, we propose a series of methods to discover non-redundant DNA shape motifs with the generalization to multiple motifs in multiple shape features. Specifically, an existing Gibbs sampling method is generalized to multiple DNA motif discovery with multiple shape features. Meanwhile, an expectation-maximization (EM) method and a hybrid method coupling EM with Gibbs sampling are proposed and developed with promising performance, convergence capability, and efficiency. The discovered DNA shape motif instances reveal insights into low-signal ChIP-seq peak summits, complementing the existing sequence motif discovery works. Additionally, our modelling captures the potential interplays across multiple DNA shape features. We provide a valuable platform of tools for DNA shape motif discovery. © The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.
Original languageEnglish
Pages (from-to)4137-4150
JournalNucleic Acids Research
Volume52
Issue number8
Online published4 Apr 2024
DOIs
Publication statusPublished - 8 May 2024

Funding

National Natural Science Foundation of China [32170654]; Research Grants Council of the Hong Kong Special Administrative Region [CityU 11203723]; Innovation and Technology Commission [ITB/FBL/9037/22/S]; Strategic Interdisciplinary Research Grant of City University of Hong Kong [2021SIRG036]; City University of Hong Kong [CityU 11203221, CityU 9667265, CityU 11203221]. Funding for open access charge: National Natural Science Foundation of China [32170654]; Shenzhen Research Institute, City University of Hong Kong; Research Grants Council of the Hong Kong Special Administrative Region [CityU 11203723]; City University of Hong Kong [2021SIRG036, CityU 9667265, CityU 11203221].

Publisher's Copyright Statement

  • This full text is made available under CC-BY-NC 4.0. https://creativecommons.org/licenses/by-nc/4.0/

RGC Funding Information

  • RGC-funded

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