Z-Calling: a tool for A/Z (2,6-diaminopurine) base calling and dZ-DNA detection using PacBio HiFi reads

The natural occurrence of 2,6-diaminopurine (Z) as a substitute for adenine (A) in certain bacteriophage genomes has profound evolutionary implications and promising biotechnological potential. Progress in this field, however, has been stymied by the absence of reliable methods to detect dZ-DNA, particularly in mixed samples, and to distinguish A from Z at the single-nucleotide level. Here, we introduce Z-Calling, a machine learning-based tool designed to identify dZ-DNA and discriminate A/Z bases directly from PacBio Circular Consensus Sequencing (CCS) reads without additional processing. By analyzing sequence context-dependent kinetic signal changes induced by Z/A substitution, Z-Calling achieves exceptional sensitivity, reliably detecting dZ-DNA even in samples with as little as ~1% dZ-DNA content. Its A/Z base-calling module demonstrates robust performance, with AUC scores of 0.942-0.952 across diverse DNA sequence contexts. Z-Calling represents a significant advancement in accessible and accurate dZ-DNA sequencing, paving the way for its broader application in biotechnology. Z-Calling is freely available at https://github.com/xiaochuanle/Z-Calling .

Authors

Institutions

Publication Details

Journal
Communications Biology
Published
2026-03-13
DOI
https://doi.org/10.1038/s42003-026-09849-8
Primary Topic
DNA and Nucleic Acid Chemistry
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Z-Calling: a tool for A/Z (2,6-diaminopurine) base calling and dZ-DNA detection using PacBio HiFi reads

Chuan‐Le Xiao, Suwen Zhao, Wei Chi, Yan Zhang et al.
Communications Biology
DNA and Nucleic Acid Chemistry
article

Z-Calling: a tool for A/Z (2,6-diaminopurine) base calling and dZ-DNA detection using PacBio HiFi reads

Chuan‐Le Xiao, Suwen Zhao, Wei Chi, Yan Zhang, Yating Li, Longjian Niu, Ying Chen, Bo Wu, Yan Zhou, He-Xu Chen, Jia-Yong Zhong
article en

Abstract

The natural occurrence of 2,6-diaminopurine (Z) as a substitute for adenine (A) in certain bacteriophage genomes has profound evolutionary implications and promising biotechnological potential. Progress in this field, however, has been stymied by the absence of reliable methods to detect dZ-DNA, particularly in mixed samples, and to distinguish A from Z at the single-nucleotide level. Here, we introduce Z-Calling, a machine learning-based tool designed to identify dZ-DNA and discriminate A/Z bases directly from PacBio Circular Consensus Sequencing (CCS) reads without additional processing. By analyzing sequence context-dependent kinetic signal changes induced by Z/A substitution, Z-Calling achieves exceptional sensitivity, reliably detecting dZ-DNA even in samples with as little as ~1% dZ-DNA content. Its A/Z base-calling module demonstrates robust performance, with AUC scores of 0.942-0.952 across diverse DNA sequence contexts. Z-Calling represents a significant advancement in accessible and accurate dZ-DNA sequencing, paving the way for its broader application in biotechnology. Z-Calling is freely available at https://github.com/xiaochuanle/Z-Calling .

Communications Biology
Tianjin University of Science and Technology (CN), Sun Yat-sen University (CN), Tianjin University (CN), Shanghai Jiao Tong University (CN), ShanghaiTech University (CN), Southern Medical University Shenzhen Hospital (CN), He Eye Hospital (CN), Shanghai Clinical Research Center (CN), Yangzhou University (CN)
Openalex Percentile: Top 24%
DNA and Nucleic Acid Chemistry
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.