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

  • Guliang Wang
  • , Ioannis Mouratidis
  • , Kimonas Provatas
  • , Nikol Chantzi
  • , Michail Patsakis
  • , Ilias Georgakopoulos-Soares
  • , Karen M. Vasquez

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article numberbbaf240
JournalBriefings in bioinformatics
Volume26
Issue number3
DOIs
StatePublished - May 1 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Molecular Biology

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