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Cited by 29DNA and Nucleic Acid ChemistryMachine Learning in BioinformaticsGenomics and Chromatin Dynamics

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Prediction of DNA i-motifs via machine learning

Open access
Yang, Bibo, Guneri, Dilek, Yu, Haopeng, Wright, Elisé P, Chen, Wenqian, Waller, Zoë A E, Ding, Yiliang·2024·Nucleic Acids Research
DOI 10.1093/nar/gkae092

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Abstract

i-Motifs (iMs), are secondary structures formed in cytosine-rich DNA sequences and are involved in multiple functions in the genome. Although putative iM forming sequences are widely distributed in the human genome, the folding status and strength of putative iMs vary dramatically. Much previous research on iM has focused on assessing the iM folding properties using biophysical experiments. However, there are no dedicated computational tools for predicting the folding status and strength of iM structures. Here, we introduce a machine learning pipeline, iM-Seeker, to predict both folding status and structural stability of DNA iMs. The programme iM-Seeker incorporates a Balanced Random Forest classifier trained on genome-wide iMab antibody-based CUT&Tag sequencing data to predict the folding status and an Extreme Gradient Boosting regressor to estimate the folding strength according to both literature biophysical data and our in-house biophysical experiments. iM-Seeker predicts DNA iM folding status with a classification accuracy of 81% and estimates the folding strength with coefficient of determination (R2) of 0.642 on the test set. Model interpretation confirms that the nucleotide composition of the C-rich sequence significantly affects iM stability, with a positive correlation with sequences containing cytosine and thymine and a negative correlation with guanine and adenine.

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Metadata

Authors
Yang, B., Guneri, D., Yu, H., Wright, E. P., Chen, W., Waller, Z. A. E., & Ding, Y.
Affiliations
  • John Innes Centre (GB)
  • UCL School of Pharmacy (GB)
  • Western Sydney University (AU)
Corresponding author
  • Yu, Haopeng
  • Waller, Zoë A E
  • Ding, Yiliang
ORCID
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2024-02-14
Volume
52
Issue
5
Pages
2188-2197
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
DOI
10.1093/nar/gkae092
OpenAlex
W4391886086
Cited by
29
References
5
Citations by year
2026: 10 · 2025: 12 · 2024: 7
FWCI
3.94
Type
journal article
Language
en
Open access
Licence
Locations
  • Nucleic Acids Research (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion, cc-by)
Funders
  • BBSRC (BB/X01102X/1, BB/W000962/1)
  • BBSRC Norwich Research Park Biosciences Doctoral Training Partnership (2578674)
  • BBSRC Horizon Europe Guarantee (EP/Y009886/1)
  • Human Frontier Science Program Fellowship (LT001077/2021-L)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › DNA and Nucleic Acid Chemistry
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
Keywords
i-motif, balanced random forest, extreme gradient boosting, nucleotide composition, structural stability
MeSH terms
Machine Learning, Base Sequence, Cytosine, DNA, Humans, Nucleotide Motifs

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