ZSeeker: an optimized algorithm for Z-DNA detection in genomic sequences

Z-deoxyribonucleic acid (Z-DNA) is an alternative left-handed DNA structure with a zigzag-shaped backbone that differs from the right-handed canonical B-DNA helix. Z-DNA has been implicated in various biological processes, including transcription, replication, and DNA repair, and can induce genetic instability. Repetitive sequences of alternating purines and pyrimidines have the potential to adopt Z-DNA structures. ZSeeker is a novel computational tool developed for the accurate detection of potential Z-DNA-forming sequences in genomes, addressing key limitations of prior methods, such as computational inefficiency, difficult interpretability and usability, and lack of experimentally generated data. By introducing a novel methodology informed and validated by experimental data, ZSeeker enables the refined detection of potential Z-DNA-forming sequences. Built both as a standalone Python package and as an accessible web interface, ZSeeker allows users to input genomic sequences, adjust detection parameters, and view potential Z-DNA sequence distributions and Z-scores via downloadable visualizations. Our web platform provides a no-code solution for Z-DNA identification, with a focus on accessibility, user-friendliness, speed, and customizability. By providing efficient, high-throughput analysis, and enhanced detection accuracy, ZSeeker has the potential to support significant advancements in understanding the roles of Z-DNA in normal cellular functions, genetic instability, and its implications in human diseases.

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Publication Details

Journal
Briefings in Bioinformatics
Published
2025-05-01
DOI
https://doi.org/10.1093/bib/bbaf240
Citations
9
Primary Topic
Advanced biosensing and bioanalysis techniques
Type
article
Field-Weighted Citation Impact
7.25

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article

ZSeeker: an optimized algorithm for Z-DNA detection in genomic sequences

Karen M. Vásquez, Guliang Wang, Ilias Georgakopoulos‐Soares, Nikol Chantzi et al.
9 citations
Briefings in Bioinformatics
Advanced biosensing and bioanalysis techniques
7.25
article

ZSeeker: an optimized algorithm for Z-DNA detection in genomic sequences

Karen M. Vásquez, Guliang Wang, Ilias Georgakopoulos‐Soares, Nikol Chantzi, Ioannis Mouratidis, Michail Patsakis, Kimonas Provatas
article en
9 citations

Abstract

Z-deoxyribonucleic acid (Z-DNA) is an alternative left-handed DNA structure with a zigzag-shaped backbone that differs from the right-handed canonical B-DNA helix. Z-DNA has been implicated in various biological processes, including transcription, replication, and DNA repair, and can induce genetic instability. Repetitive sequences of alternating purines and pyrimidines have the potential to adopt Z-DNA structures. ZSeeker is a novel computational tool developed for the accurate detection of potential Z-DNA-forming sequences in genomes, addressing key limitations of prior methods, such as computational inefficiency, difficult interpretability and usability, and lack of experimentally generated data. By introducing a novel methodology informed and validated by experimental data, ZSeeker enables the refined detection of potential Z-DNA-forming sequences. Built both as a standalone Python package and as an accessible web interface, ZSeeker allows users to input genomic sequences, adjust detection parameters, and view potential Z-DNA sequence distributions and Z-scores via downloadable visualizations. Our web platform provides a no-code solution for Z-DNA identification, with a focus on accessibility, user-friendliness, speed, and customizability. By providing efficient, high-throughput analysis, and enhanced detection accuracy, ZSeeker has the potential to support significant advancements in understanding the roles of Z-DNA in normal cellular functions, genetic instability, and its implications in human diseases.

Briefings in BioinformaticsVol. 26(3)
Pennsylvania State University (US), The University of Texas at Austin (US)
National Institute of General Medical Sciences
Openalex Percentile: Top 3%
Advanced biosensing and bioanalysis techniques
7.25
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