ArticleScientific reports2020
Deep learning approach for predicting functional Z-DNA regions using omics data.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- An updated overview of experimental and computational approaches to identify non-canonical DNA/RNA structures with emphasis on G-quadruplexes and R-loops.Briefings in bioinformatics · 2022Pooled it
- Z-DNA-associated genomic instability in the human pangenome.Research square · 2026Article
- Identification of methylation-sensitive human transcription factors using meSMiLE-seq.Nature communications · 2026Article
- Z-DNA-induced genomic instability in the human pangenome.Research square · 2026Article
- Nonconsensus flanking sequence of hundreds of base pairs around in vivo binding sites: statistical beacons for transcription factor scanning.Nucleic acids research · 2026Article
- Alexidine, identified as a Z-DNA inducer by the NanoZ screening platform, acts as a transcriptional regulator.Nucleic acids research · 2026Article
- Non-B DNA structures and their contributions to genetic diversity, aging, and disease.Nucleic acids research · 2026Review
- Formation and biological implications of Z-DNA.Trends in genetics : TIG · 2026Review
- Alternative nucleic acid structures in microbial biology: regulatory functions and antimicrobial opportunities.Frontiers in cellular and infection microbiology · 2026Review
- Control of Gene Expression by Proteins That Bind Many Alternative Nucleic Acid Structures Through the Same Domain.International journal of molecular sciences · 2025Article
- Z-DNA Hunter tool for straightforward detection of Z-DNA forming regions and a case study inNAR genomics and bioinformatics · 2025Article
- Review
- Advances in Artificial Intelligence (AI) Models and Generative Algorithms Represent a New Paradigm for Genomics Research.International journal of molecular sciences · 2025Review
- Z-GENIE: a user-friendly R/Shiny resource for predicting Z-DNA forming regions in DNA.BMC genomics · 2025Article
- Deep learning deciphers the related role of master regulators and G-quadruplexes in tissue specification.Scientific reports · 2025Article
- ZSeeker: an optimized algorithm for Z-DNA detection in genomic sequences.Briefings in bioinformatics · 2025Article
- Global identification and functional characterization of Z-DNA in rice.Plant biotechnology journal · 2025Article
- Kolmogorov-Arnold networks for genomic tasks.Briefings in bioinformatics · 2025Article
- Synchrotron radiation FTIR microspectroscopy enables measuring dynamic cell identity patterning during human 3D differentiation.Frontiers in cell and developmental biology · 2025Article
- Z-DNA formation in promoters conserved between human and mouse are associated with increased transcription reinitiation rates.Scientific reports · 2024Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational methods to predict Z-DNA regions are in high demand to understand the functional role of Z-DNA. The previous state-of-the-art method Z-Hunt is based on statistical mechanical and energy considerations about B- to Z-DNA transition using sequence information. Z-DNA CHiP-seq experiment results showed little overlap with Z-Hunt predictions implying that sequence information only is not sufficient to explain emergence of Z-DNA at different genomic locations. Adding epigenetic and other functional genomic mark-ups to DNA sequence level can help revealing the functional Z-DNA sites. Here we take advantage of the deep learning approach that can analyze and extract information from large volumes of molecular biology data. We developed a machine learning approach DeepZ that aggregates information from genome-wide maps of epigenetic markers, transcription factor and RNA polymerase binding sites, and chromosome accessibility maps. With the developed model we not only verify the experimental Z-DNA predictions, but also generate the whole-genome annotation, introducing new possible Z-DNA regions, which have not yet been found in experiments and can be of interest to the researchers from various fields.
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