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A novel convolution attention model for predicting transcription factor binding sites by combination of sequence and shape.

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  • Additional Information
    • Source:
      Publisher: Oxford University Press Country of Publication: England NLM ID: 100912837 Publication Model: Print Cited Medium: Internet ISSN: 1477-4054 (Electronic) Linking ISSN: 14675463 NLM ISO Abbreviation: Brief Bioinform Subsets: MEDLINE
    • Publication Information:
      Publication: Oxford : Oxford University Press
      Original Publication: London ; Birmingham, AL : H. Stewart Publications, [2000-
    • Subject Terms:
      Binding Sites*; Computational Biology/*methods ; DNA-Binding Proteins/*chemistry ; Transcription Factors/*chemistry; Algorithms ; Chromatin Immunoprecipitation Sequencing ; DNA/chemistry ; Humans ; Neural Networks, Computer ; Protein Binding ; Software ; Transcription Factors/metabolism
    • Abstract:
      The discovery of putative transcription factor binding sites (TFBSs) is important for understanding the underlying binding mechanism and cellular functions. Recently, many computational methods have been proposed to jointly account for DNA sequence and shape properties in TFBSs prediction. However, these methods fail to fully utilize the latent features derived from both sequence and shape profiles and have limitation in interpretability and knowledge discovery. To this end, we present a novel Deep Convolution Attention network combining Sequence and Shape, dubbed as D-SSCA, for precisely predicting putative TFBSs. Experiments conducted on 165 ENCODE ChIP-seq datasets reveal that D-SSCA significantly outperforms several state-of-the-art methods in predicting TFBSs, and justify the utility of channel attention module for feature refinements. Besides, the thorough analysis about the contribution of five shapes to TFBSs prediction demonstrates that shape features can improve the predictive power for transcription factors-DNA binding. Furthermore, D-SSCA can realize the cross-cell line prediction of TFBSs, indicating the occupancy of common interplay patterns concerning both sequence and shape across various cell lines. The source code of D-SSCA can be found at https://github.com/MoonLord0525/.
      (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)
    • Contributed Indexing:
      Keywords: DNA shape profiles; attention mechanism; convolutional neural network; transcription factor binding sites prediction
    • Accession Number:
      0 (DNA-Binding Proteins)
      0 (Transcription Factors)
      9007-49-2 (DNA)
    • Publication Date:
      Date Created: 20211220 Date Completed: 20220311 Latest Revision: 20220311
    • Publication Date:
      20250114
    • Accession Number:
      10.1093/bib/bbab525
    • Accession Number:
      34929739