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Decoding DNA metabolism and its clinical relevance through the lens of high-throughput sequencing assays

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Published/Copyright: April 3, 2026
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Medical Review
From the journal Medical Review

Abstract

Eukaryotic DNA metabolism, involving DNA replication and damage repair, ensures the faithful transmission of genetic information and is essential for maintaining genome integrity. Consequently, its dysregulation contributes to a broad spectrum of human diseases, including cancer and pregnancy loss. Recent advances in high-throughput sequencing (HTS) assays have enabled genome-wide, single-cell, and even single-molecule analyses of DNA metabolism dynamics within their native chromatin context, profoundly expanding our capacity to dissect these processes in vivo and to evaluate their clinical significance. In this review, we summarize HTS-based technologies that profile the entire DNA replication program, spanning initiation, elongation, termination, and replication timing, as well as the diverse pathways involved in DNA damage detection and repair. We further highlight how these approaches have been leveraged to investigate fundamental biological processes and translational applications, with particular emphasis on early embryonic development, cancer, and genome editing. Collectively, these advances illustrate how HTS has bridged molecular mechanisms with physiological and clinical insights, while pointing toward future directions including telomere-to-telomere genome analysis, single-cell multi-omics integration, and precision genomic medicine.

Keywords: DNA replication; DNA damage repair; high-throughput sequencing; early embryonic development; cancer; genome editing

Introduction

Deoxyribonucleic acid (DNA) metabolism encompasses the vital cellular processes that maintain genomic integrity and ensure faithful inheritance, including DNA replication and DNA damage repair [1], [2], [3], [4]. In eukaryotic cells, DNA is wrapped around histones and packaged into chromatin, which is further organized into highly ordered structures within the nucleus [5], 6]. All eukaryotic DNA metabolism processes occur within this chromatin framework. Therefore, DNA metabolism activities are shaped by the underlying chromatin landscape and, in turn, actively remodel it [7], [8], [9]. This reciprocal relationship creates a highly dynamic, spatially and temporally regulated nuclear environment and underscores the necessity of studying DNA metabolism processes within the native chromatin context.

Within this shared chromatin landscape, distinct DNA metabolism processes are tightly interconnected. The progression of DNA replication, for example, is intimately coordinated with surveillance and repair pathways that safeguard nascent DNA from endogenous and exogenous damage [10]. When replication forks stall or collapse, commonly due to DNA lesions or transcription-replication conflicts (TRCs), newly synthesized DNA becomes vulnerable, activating repair pathways that detect DNA damage, restore fork stability, and prevent the transmission of errors into daughter cells. The biological significance of these DNA replication-damage-repair interactions is profound. Proper coordination ensures faithful genome duplication, maintains chromatin organization and gene expression programs, and safeguards epigenetic inheritance. Conversely, the failure or imbalance of these processes leads to chromosomal aberrations, elevated mutation burdens, and diverse human diseases [11]. Replication stress and defective repair, in particular, are central drivers of tumorigenesis. Fork stalling, collapse into double-strand breaks (DSBs), and erroneous repair collectively fuel mutagenesis and genome instability [12]. Importantly, replication stress is not confined to cancer but also constitutes a key feature and challenge during early embryonic development, when rapid cell cycles and attenuated checkpoint control render the genome particularly susceptible to damage [13].

Understanding the integrated in vivo network of replication-damage-repair requires comprehensive and genome-wide approaches, which are fulfilled by high-throughput sequencing (HTS) technologies. HTS enables massively parallel sequencing of millions to billions of DNA or RNA molecules, providing an unprecedented view into genome-wide molecular events. Its evolution encompasses short-read platforms, represented by Illumina sequencing [14], and extends to single-molecule long-read technologies such as Pacific Biosciences (PacBio) [15], 16] and Oxford Nanopore [17]. The advent of HTS has transformed the study of DNA metabolism by enabling the mapping of these processes within their native chromatin and three-dimensional (3D) chromatin environment [18]. HTS-based methods now allow genome-wide profiling of replication initiation events, elongation dynamics, termination zones, and replication timing programs; quantitative assessment of replication stress and fork collapse; and high-resolution detection of DNA damage and repair events. Analyses of cancer genomes have further revealed mutational signatures that trace back to specific damage and repair pathways, linking long-term genomic outcomes to their mechanistic origins [19], [20], [21], [22], [23], [24]. More recently, single-cell and multi-omics HTS approaches have revealed cell-to-cell variation in DNA replication and repair, integrating these processes with transcriptional activity, chromatin state, and nuclear architecture [25], [26], [27], [28], [29], [30], [31].

Building on these advances, this review aims to provide a focused yet comprehensive account of how HTS technologies are employed to interrogate DNA replication, DNA damage repair, and their associated genome instability. We first survey the repertoire of HTS-based methods that directly profile replication dynamics and DNA damage repair processes, outlining their principles, recent developments, and emerging trends. We then examine how these approaches are applied to physiological and clinical contexts, including early embryonic development, prenatal diagnosis, tumor diagnosis and prognostication, and genome-editing-based therapeutics. By linking methodological innovation with biological and medical applications, this review highlights how sequencing-driven insights are reshaping our understanding of DNA metabolism and expanding its impact across diverse areas of biology and medicine.

HTS-based assays for dissecting DNA replication

The preparation of eukaryotic DNA replication begins in the late M and G1 phase, when the origin recognition complex (ORC) binds specific genomic sites termed replication origins (Figure 1a). ORC, together with additional initiation factors, recruits the mini-chromosome maintenance (MCM) complex to assemble the pre-replication complex (pre-RC), a process known as replication licensing [32], 33]. Upon entry into S phase, a subset of these licensed origins is activated, leading to the formation of Cdc45-MCM-GINS (CMG) replicative helicase and the establishment of two bidirectionally elongating replication forks at each origin, thereby generating a replication bubble [33], [34], [35]. Each fork produces one continuously synthesized leading strand and one discontinuously synthesized lagging strand composed of short Okazaki fragments [2]. Replication termination occurs when two convergent replication forks, originating from adjacent origins, complete the duplication of their intervening DNA [36], 37].

Figure 1: HTS-based technologies for profiling DNA replication dynamics. (A) The four steps of DNA replication include pre-RC assembly, initiation, elongation, and termination. (B) Replication licensing, marked by the loading of ORC and MCM complex onto chromatin, can be assessed using ChIP-seq. (C) Following licensing, replication initiation zones, replication fork dynamics, and replication timing can be profiled using nascent-DNA labeling strategies that incorporate nucleotide analogs into newly synthesized DNA. Additional approaches include Pu-seq, which exploits endogenous ribonucleotides (rNMP) incorporation to infer polymerase usage, and OK-seq, which sequences Okazaki fragments to determine replication fork directionality. SNS-seq identifies replication origins by isolating short nascent strands tagged by RNA primers, while bubble-seq captures intact replication bubble structures. (D) during elongation, key physiological features, including fork directionality and velocity, fork stability, replication-coupled chromatin shaping, and cell-to-cell heterogeneity, can be interrogated via different HTS-based approaches. Replication termination is characterized by regions of convergent fork polarity, which can be identified by OK-seq and FORK-seq. Created with BioRender.com.
Figure 1:

HTS-based technologies for profiling DNA replication dynamics. (A) The four steps of DNA replication include pre-RC assembly, initiation, elongation, and termination. (B) Replication licensing, marked by the loading of ORC and MCM complex onto chromatin, can be assessed using ChIP-seq. (C) Following licensing, replication initiation zones, replication fork dynamics, and replication timing can be profiled using nascent-DNA labeling strategies that incorporate nucleotide analogs into newly synthesized DNA. Additional approaches include Pu-seq, which exploits endogenous ribonucleotides (rNMP) incorporation to infer polymerase usage, and OK-seq, which sequences Okazaki fragments to determine replication fork directionality. SNS-seq identifies replication origins by isolating short nascent strands tagged by RNA primers, while bubble-seq captures intact replication bubble structures. (D) during elongation, key physiological features, including fork directionality and velocity, fork stability, replication-coupled chromatin shaping, and cell-to-cell heterogeneity, can be interrogated via different HTS-based approaches. Replication termination is characterized by regions of convergent fork polarity, which can be identified by OK-seq and FORK-seq. Created with BioRender.com.

Over the past decade, sequencing-based approaches have markedly expanded our ability to profile the entire DNA replication process. In this section, we highlight representative assays that profile DNA replication dynamics (Supplementary Table S1), as comprehensive methodological reviews are available elsewhere [1], [38], [39], [40], [41].

DNA replication initiation

A broad array of methods has been developed to identify replication origins by targeting certain protein factors or replication intermediates. Early genome-wide efforts relied primarily on bulk cell populations. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) has been widely used to map the binding sites of replication licensing factors such as ORC and MCM in both yeast and metazoans [42], 43] (Figure 1b). Upon entry into the S phase, replication initiation can be captured through enrichment of nascent DNA (Figure 1c). For instance, short nascent strand sequencing (SNS-seq) [44] enriches RNA-primed nascent DNA originating from fired origins after lambda exonuclease digestion, and initiation-site sequencing (Ini-seq and ini-seq2) isolates Br-dUTP or digoxigenin-dUTP labeled nascent DNA synthesized [45], 46]. Bubble-seq identifies replication origins by isolating replication bubbles from restriction endonuclease-digested chromatin, enabling genome-wide detection at moderate resolution [47]. Alternatively, other approaches infer replication initiation indirectly by monitoring fork polarity or fork progression (Figure 1d). Okazaki fragment sequencing (OK-seq) measures leading-vs. lagging-strand polarity to define initiation zones (IZs) and termination zones (TZs) by purifying Okazaki fragments [48]. Nucleoside analog incorporation loci sequencing (NAIL-seq), which sequentially labels nascent DNA with 5-ethynyl-2′-deoxyuridine (EdU) and 5-bromodeoxyuridine (BrdU), maps early replication initiation zones (ERIZs) with high resolution and minimal artifacts, as elongating forks at newly fired origins incorporate EdU first and then BrdU [49]. Polymerase-usage sequencing (Pu-seq) detects strand-specific usage of Polε (on the leading strand) and Polα/δ (on the lagging strand) to identify IZs and TZs [50] (Figure 1c), reducing biases associated with short-lived replication intermediates in OK-seq and SNS-seq [51]. Beyond methods applied in cultured cell lines, several HTS-based approaches have been employed in animal tissues, enabling the interrogation of DNA replication dynamics under physiological conditions in vivo. Origin-derived single-stranded DNA sequencing (Ori-SSDS) preserves the strand polarity of the RNA-primed nascent strand and has been used to map replication origins in mammalian testis [52]. Furthermore, EdU-sequencing combined with hydroxyurea treatment (EdU-seq-HU) has been applied to identify replication origins in regenerating mouse liver, where partial hepatectomy induces highly synchronized S-phase entry across hepatocytes [53], [54], [55] (Supplementary Table S1).

While traditional bulk approaches offer high-throughput and population-averaged maps of replication initiation, they exhibit limited concordance. Methods such as SNS-seq [44] and Bubble-seq [47] capture large numbers of discrete initiation sites, reporting over 320,000 and 100,000 sites, respectively. In contrast, OK-seq identified approximately 5,000–10,000 broad IZs spanning up to 150 kb [48], whereas NAIL-seq detects around 3,000 ERIZs with a median length of 70 kb [49]. These substantial differences in peak numbers likely reflect not only variations in spatial resolution but also the markedly different input requirements among methods. For instance, SNS-seq requires approximately 1 × 109 cells [45], while Bubble-seq demands ∼1 × 1010 cells [47]. OK-seq typically requires 0.2–1 × 109 cells depending on cell type [48], whereas NAIL-seq operates with fewer than 2 million cells [49] (Supplementary Table S1). Beyond technical biases, the inherent cell-to-cell heterogeneity and stochastic nature of origin firing further limit the interpretability of bulk measurements, driving a shift toward single-cell and single-molecule profiling. Long-read sequencing and single-cell nascent DNA sequencing now enable direct measurement of origin usage, fork progression, and termination on individual DNA molecules (Supplementary Table S1). Techniques such as Replicon-seq [25] and DNAscent [56], 57] delineate replicon boundaries at single-molecule resolution, leverage nanopore sequencing to simultaneously map initiation sites and track fork movement. Optical approaches provide complementary insights. Single-molecule analysis of replicated DNA (SMARD) allows locus-specific examination of replication dynamics but remains low-throughput [58], [59], [60], whereas optical replication mapping (ORM) [61] enables genome-wide, high-throughput visualization of initiation events on long individual DNA fibers. Of note, although bulk assays demonstrate that replication initiation tends to occur within broad zones, fewer than 20 % of initiation events identified at the single-molecule level fall within bulk-defined IZs [57], reinforcing the stochastic and heterogeneous nature of origin firing.

Despite the stochasticity of individual initiation events, when thousands of cells are considered collectively, they give rise to a highly reproducible and patterned replication timing program [62]. Replication timing is strongly correlated with certain chromatin features such as A/B compartments, topologically associating domains, and histone modifications. Genome-wide replication timing can be profiled using Repli-seq [63], a method that leverages nucleotide-analog labeling and sequential fractionation of S-phase cells to distinguish early-from late-replicating genomic regions. These profiles consistently show that early replication initiation events are enriched within early-replicating domains. High-resolution Repli-seq has enabled the identification of 16 S-phase fractions in mammalian cells [19]. Furthermore, Repli-seq methodologies have advanced to the single-cell level, allowing for more precise characterization of replication dynamics across individual cells [26], 64].

DNA replication elongation

Following initiation, eukaryotic replication forks traverse a complex chromatin environment, synthesizing nascent DNA while contending with nucleosomes, DNA secondary structures, transcription machinery, and topological constraints (Figure 1a and d). Key physiological aspects of the elongation phase include (i) fork directionality and velocity, (ii) fork stability and responses to impediments, and (iii) the interplay between fork progression and chromatin. HTS-based strategies now enable genome-wide interrogation of these parameters, thereby linking local chromatin context to replication dynamics.

Fork directionality and velocity constitute fundamental metrics of replication fork behavior. As mentioned above, OK-seq measures fork directionality through strand-specific sequencing of Okazaki fragments [21], 65], 66]. Single-molecule long-read approaches, such as FORK-seq [67], directly quantify individual fork track lengths from high-molecular-weight DNA, whereas single-cell assays, such as scEdU-seq [28], pulse-label nascent DNA with EdU and estimate fork speed by calculating replicated tract lengths within a defined time window. Although these approaches provide valuable insight into how fork kinetics vary across distinct chromatin environments, FORK-seq remains sensitive to DNA breakage during sample processing, and effective implementation of scEdU-seq requires sophisticated automated pipetting system.

Replication forks frequently stall when encountering difficult-to-replicate scenarios, including compact chromatin, collision with transcription machinery, or DNA secondary structures [68], 69]. Stalled forks are prone to reversal and generate exposed 3′ single-strand ends. Transferase-activated end-ligation sequencing (TrAEL-seq) captures these 3′ ends genome-wide in asynchronous cells [70] (Figure 1d). Complementarily, DNA structure-sensitive assays such as S1-END-seq [71], which employs S1 nuclease digestion of single-strand DNA regions followed by end mapping, reveal cruciforms, H-DNA, and other non-B DNA structures that arise ahead of or behind progressing forks. Enrichment of such structures marks regions of topological or structural impediment to elongation. Of note, both TrAEL-seq and S1-END-seq requires careful agarose embedding to minimize background arising from physical DNA breakage.

Another crucial aspect of elongation is the coordination between replication fork and the surrounding chromatin environment (Figure 1d and Supplementary Table S1). Enrichment and sequencing of protein-associated nascent DNA (eSPAN) [72] enables strand-specific profiling of histones and histone modifications associated with nascent DNA and can be performed with as few as 50,000–100,000 cells. In eSPAN, BrdU-labeled nascent DNA is isolated after immunoprecipitation of the protein of interest, followed by strand-specific library preparation, allowing quantification of strand-biased histone deposition or modification. Related approaches, including chromatin occupancy after replication (ChOR-seq) [73], 74] and sister chromatids after replication sequencing (SCAR-seq) [74], 75], profile histone or chromatin-factor occupancy on newly replicated DNA or sister chromatids. These approaches collectively reveal that failure in nucleosome reassembly, whether due to altered fork speed or impaired chromatin remodeling, can create structural vulnerabilities during elongation. Complementarily, nascent-DNA bisulfite-based methods extend fork-associated analyses to DNA methylation inheritance. NasBS-seq profiles CpG methylation on newly synthesized strands genome-wide, whereas nasChIP-BS-seq combines chromatin immunoprecipitation with bisulfite conversion to interrogate methylation within defined chromatin contexts [76], 77]. HAMMER-seq employs hairpin ligation to physically link complementary strands, enabling single-molecule resolution of parental-vs-daughter-strand methylation patterns [78], 79]. Together, these approaches provide complementary views of how methylation is re-established during replication, differing in chromatin specificity, molecular resolution, and directness of parental-nascent strand comparison.

Higher-order genome architecture further modulates replication fork dynamics (Figure 1d). Integration of replication assays with 3D chromatin conformation profiling has highlighted bidirectional influences between DNA replication and nuclear organization. For instance, cohesin-mediated chromatin loop extrusion can regulate both replication initiation and elongation [80], [81], [82]. In addition, Repli-HiC [83] uncovers “fountain-like” contact patterns between sister forks or convergent forks arising from adjacent origins, indicating that DNA replication actively reshapes local 3D chromatin topology. These observations underscore that replication fork elongation is not a merely linear process, but instead is profoundly influenced by the spatial organization and dynamic architecture of chromatin.

DNA replication termination

Replication termination occurs when two converging replication forks complete DNA synthesis, followed by replication machinery disassembly and the re-establishment of chromatin in the newly replicated region [36] (Figure 1a). Recent advances in HTS-based methods have made it possible to measure these events directly or infer them genome-wide. A key physiological hallmark of termination is the formation of a merge zone, where two opposing forks meet [37]. In genome-wide profiles generated by OK-seq, termination is detected as a downward shift in replication fork directionality (RFD): the transition from predominantly rightward-moving forks to leftward-moving forks delineates a fork-convergence region [48]. Beyond such population-level directionality transitions, the precision and efficiency of termination can be examined at single-molecule resolution with approaches such as FORK-seq [67] as well. Termination also requires efficient chromatin re-assembly behind the merged forks. Methods including ChOR-seq [74] and SCAR-seq [74] offer valuable insight into the fidelity and dynamics of chromatin restoration following fork convergence (Figure 1d).

Taken together, these HTS-based approaches allow comprehensive analysis of replication initiation, elongation, and termination across the genome. Because initiation is highly heterogeneous between individual cells, bulk sequencing can obscure critical cell-to-cell variability. Single-cell and single-molecule methods resolve rare events and reveal the full spectrum of replication dynamics. In addition, the availability of telomere-to-telomere genome assemblies now permits long-read sequencing assays to interrogate replication processes within repetitive regions that were previously inaccessible to short-read technologies [84], [85], [86]. Finally, integrating replication measurements with other omics layers in single cells, such as chromatin accessibility, transcriptional activity, and 3D genome architecture, provides a multi-dimensional framework for understanding how DNA replication is coordinated with chromatin state, epigenetic regulation, and chromatin architecture. These advances will therefore be crucial for dissecting the complexity of DNA replication in a heterogeneous and dynamic context.

HTS-based technologies for dissecting DNA damage and repair

DNA damage and repair constitute another major pillar of DNA metabolism. DNA damage refers to any structural alteration in DNA that may arise from endogenous processes or exogenous insults [87], 88]. For instance, any dysregulation in DNA replication can lead to fork stalling and replication stress, ultimately resulting in catastrophic forms of DNA damage [87]. Among these, DSBs represent the most cytotoxic lesions and pose the greatest threat to genome integrity. Accordingly, this review focuses primarily on DSBs and their repair. DSBs can occur physiologically during processes such as V(D)J recombination and meiosis [89], 90], and pathologically through replication stress at common fragile sites (CFSs), the breakage of alternative DNA secondary structures, or exposure to ionizing radiation and oxidative agents [91], [92], [93]. The formation of a DSB initiates a cascade of end-processing steps and repair intermediate formation [94], each of which can be interrogated using HTS-based methods (Figure 2 and Supplementary Table S2).

Figure 2: HTS-based technologies for detecting DSBs. Overview of representative HTS-based technologies that delineate distinct dimensions of the DSB-repair landscape, including (A) genome-wide DSB ends mapping (e.g., TrAEL-seq, END-seq, BLISS, and INDUCE-seq), (B) detection of pathway-specific repair factors or DNA intermediates (e.g., DISCOVER-seq, SSDS-seq, SPO11-oligo mapping), (C) profiling of post-repair products (e.g., PEM-seq), and replication-stress-induced mitotic DNA synthesis (MiDAS-seq). Each approach interrogates discrete biochemical intermediates or repair signatures with defined genomic resolution, strand specificity, and cell-cycle dependence, thereby enabling comprehensive identification of DSB hotspots and delineation of their repair trajectories at a genome-wide scale. Created with BioRender.com.
Figure 2:

HTS-based technologies for detecting DSBs. Overview of representative HTS-based technologies that delineate distinct dimensions of the DSB-repair landscape, including (A) genome-wide DSB ends mapping (e.g., TrAEL-seq, END-seq, BLISS, and INDUCE-seq), (B) detection of pathway-specific repair factors or DNA intermediates (e.g., DISCOVER-seq, SSDS-seq, SPO11-oligo mapping), (C) profiling of post-repair products (e.g., PEM-seq), and replication-stress-induced mitotic DNA synthesis (MiDAS-seq). Each approach interrogates discrete biochemical intermediates or repair signatures with defined genomic resolution, strand specificity, and cell-cycle dependence, thereby enabling comprehensive identification of DSB hotspots and delineation of their repair trajectories at a genome-wide scale. Created with BioRender.com.

The application of HTS to DSB biology began in the 2000s, with efforts to delineate DSB processing and repair outcomes. Early genome-wide view of DSBs relied on γ-H2AX, a hallmark that marks chromatin surrounding DNA damage sites, using chromatin immunoprecipitation with microarray (ChIP-chip), an approach with inherently limited resolution [95]. Shortly thereafter, Chiarle et al. [96] introduced high-throughput genome-wide translocation sequencing (HTGTS), which increased sensitivity and enabled direct detection of DSB-initiated chromosomal translocations. Subsequently, Pan et al. [97] inferred meiotic DSB sites by sequencing SPO11-released oligonucleotides. Direct detection of DSB termini advanced substantially with the development of breaks labeling and enrichment on streptavidin and sequencing (BLESS), which uses in situ ligation of a biotinylated linker to broken DNA ends, enabling precise detection of DSBs in 2013 [98]. Since then, numerous HTS-based methods have been developed for direct or indirect detection of DSBs and associated repair processes (Figure 2 and Supplementary Table S2). Broadly, these techniques fall into three classes based on their detection targets: (i) methods that directly map broken ends; (ii) techniques that detect repair proteins or DNA intermediates during end processing; and (iii) assays that identify post-repair products.

Direct detection of broken DNA ends

These methods preserve DSB ends in their native chromatin context and capture information regarding their genomic positions, abundance, and end structures. Despite methodological diversity, they can be grouped into two mechanistic classes according to the strategy used to tag break ends: (i) biotin-based labeling of DNA termini, and (ii) ligation of oligonucleotide adapters carrying a T7 promoter for linear RNA-based amplification.

A major class of direct DSB-detection methods installs biotin at DSB ends, followed by streptavidin-based affinity enrichment (Figure 2a). In one implementation, terminal deoxynucleotidyl transferase (TdT) adds nucleotides to the free 3′-OH ends. Methods such as TrAEL-seq [70], BrlTL [99], DSB-seq [100], and DBrIC [101] utilize TdT to append biotinylated oligonucleotides, whereas DEtail-seq [102] adds cytosine- and thymine-rich polynucleotide tails before splinter ligation with single-stranded DNA adapters. A complementary implementation introduces biotin through direct ligation or nucleotide incorporation. For instance, END-seq [103] and BLESS-based derivatives ligate biotinylated hairpin adapters to blunted ends [104], [105], [106], with END-seq providing largely unbiased, nucleotide-resolution maps of DSBs. Other approaches include S1-seq [107], which ligates 5′-biotinylated, end-blocked adapters to processed ends, and Break-seq [108], which incorporates biotin-14-dATP during repair-associated DNA synthesis. Though they differ in biochemical execution, all approaches converge on a workflow in which biotin-tagged DSB fragments are enriched and converted into sequencing libraries to map break locations, frequency, and end-processing signatures.

Breaks labeling in situ and sequencing (BLISS) and its optimized variant sBLISS [109], 110] ligate double-stranded oligonucleotide adapters containing a T7 promoter directly to DSB ends (Figure 2a). Subsequent T7 RNA polymerase-mediated in vitro transcription provides a linear amplification that enhances sensitivity and allows in situ profiling in fixed cells or tissues, making it suitable for samples with limited material or requiring spatial preservation. To further reduce PCR-associated amplification bias, INDUCE-seq employs a PCR-free, flow-cell-based enrichment strategy that directly labels native DNA break ends via engineered adapters, enabling sensitive detection of both high-frequency genome-editing-induced DSBs and low-frequency endogenous DNA breaks [111].

Detecting repair proteins or DNA intermediates

DSB can also be assayed indirectly by capturing repair proteins or DNA intermediates involved in specific repair pathways (Figure 2b). Once breaks are formed, they are primarily marked by γ-H2AX and then repaired by non-homologous end joining (NHEJ) or homologous recombination (HR), each marked by distinct repair factors and unique intermediates [112]. ChIP-seq targeting 53BP1, an essential NHEJ factor, is widely used to track DSBs. During HR, end resection initiated by the MRE11-RAD50-NBS1 (MRN) complex generates long stretches of single-stranded DNA (ssDNA) that become sequentially coated with RPA and RAD51 (or DMC1 in meiosis), which prevents degradation and mediates strand invasion [112], 113]. Single-strand DNA sequencing (SSDS) [114] enriches ssDNA bound by RPA, RAD51, or DMC1 and exploits its propensity to form hairpin for selective recovery, enabling detection of DSBs. Similarly, DISCOVER-seq detects DSBs by profiling MRN recruitment [115] and has been extended to longitudinal in vivo monitoring of DSB dynamics, including the tracking of etoposide-induced DNA damage in mouse liver [116].

Beyond DSB-associated protein markers, several key repair intermediates exist as covalent DNA-protein complexes (Figure 2b). SPO11, a topoisomerase-like enzyme that catalyzes meiotic DSB formation, becomes covalently attached to oligonucleotides during cleavage [97], 117]. These Spo11-bound oligos have long been used to map meiotic DSB sites in yeast and mammals [118], [119], [120], [121], though their low abundance demands substantial input. Building on this principle, covalent-complex sequencing (CC-seq), developed in 2019, directly captures Spo11-DNA conjugates and yields nucleotide-resolution maps of meiotic breaks [122], 123]. CC-seq also detects other topoisomerase cleavage complexes (TOPccs), including those formed upon treatment with topoisomerase inhibitors such as camptothecin and etoposide, without the need for antibody enrichment [122].

Detection of post-repair products

Another class of assays focuses on the genomic consequences of DSB repair (Figure 2c). Translocation-capture assays exploit the fact that DSB repair can generate chromosomal rearrangements [124]. HTGTS and related methods identify sequences that join to a defined “bait” DSB, thereby linking translocation junctions to endogenous break distributions [96], [125], [126], [127]. Building on this strategy, primer-extension-mediated sequencing (PEM-seq) introduced a quantitatively rigorous framework, enabling accurate measurement of junction frequency, microhomology usage, insertion-deletion patterns, and repair pathway bias [128], 129]. Additional approaches label repair products or newly synthesized DNA. GUIDE-seq detects CRISPR-Cas-induced DSBs by integration of double-stranded oligonucleotides (dsODNs) at break sites, enabling genome-wide off-target profiling [130]. Because many repair processes involve DNA synthesis, methods such as synthesis-associated with repair sequencing (SAR-seq) and mitotic DNA synthesis sequencing (MiDAS-seq) label nascent DNA with EdU and sequence the thymidine analog-enriched fragments to map repair- or mitosis-associated DNA synthesis genome-wide [131], [132], [133].

Collectively, these HTS-based approaches demonstrate that DSB formation and repair must be examined across multiple mechanistic layers. Methods that capture physical break ends elucidate where breaks arise and in what structural state; approaches that monitor repair proteins or DNA intermediates illuminate pathway choice, resection kinetics, and the temporal progression; and assays that map repair products report on the fidelity, efficiency, and genomic consequences of DSB repair. An integrated view, combining these orthogonal readouts, is essential for reconstructing not only the locations of DNA damage but also the molecular logic by which breaks are processed and how repair ultimately shapes genome stability. As in DNA replication research, DSB-focused HTS approaches are moving toward single-cell and single-molecule resolution, including Damage-seq and Repair-seq [27], 134], 135]. Multi-omics assays will be increasingly important for deciphering how DSB formation and repair are modulated by chromatin state, transcriptional activity, and 3D genome architecture. Furthermore, long-read sequencing combined with telomere-to-telomere (T2T) reference genomes offers a promising path toward comprehensive, genome-wide mapping of DSBs, including those in previously intractable regions.

These advances in HTS-based methodologies not only expand the resolution at which DNA metabolism processes can be interrogated but also bridge these technological capabilities to fundamental biological and clinical questions. By enabling precise measurement of DNA replication, damage, and repair dynamics, these approaches are illuminating how genome maintenance is orchestrated across physiological development and how its dysregulation contributes to pathological states. In the following sections, we outline how these technologies are being applied across key contexts of DNA metabolism, including early embryonic development, tumor diagnostics and treatment, and genome-editing-based interventions, thereby demonstrating their ability to generate mechanistic insights and drive translational progress.

DNA metabolism-related HTS assays in mammalian embryonic development

DNA metabolism plays a central role in early embryonic development, a period characterized by rapid cell divisions, extensive chromatin reorganization, and tightly coordinated transcriptional activation. Following fertilization, embryos undergo successive cleavage cycles driven by robust DNA replication [136]. Besides, zygotic genome activation (ZGA) initiates the first major transcriptional wave, establishing totipotency and guiding subsequent lineage specification [137], 138]. The DNA replication and transcription programs must be precisely coordinated to preserve genome integrity [49]. Indeed, the extremely short G1 phase and high density of origin firing in early embryos cause endogenous replication stress, likely reflecting insufficient transcription-mediated regulation of replication initiation [139], 140]. This intrinsic stress slows or stalls replication forks and predisposes embryos to chromosome mis-segregation and aneuploidy [141], 142]. Proper establishment of the replication timing program mitigates such stress and supports normal development progression [63], 141]. Recent studies have shown that the replication timing program is closely linked to gene expression, chromatin accessibility, histone modifications, and 3D genome structure [13], 63], [142], [143], [144], [145]. In parallel, DNA repair pathways remain active throughout early development to resolve replication-associated DNA damage and prevent genome instability [146], 147]. Disruption of the replication-repair interface often threatens embryo viability [139].

From the perspective of human health, dysregulation of DNA metabolism provides a mechanistic basis for idiopathic pregnancy loss and inherited developmental disorders. Pregnancy loss, including miscarriage and stillbirth, mainly arises from aneuploidy originating either in meiosis or during early postzygotic divisions [148], 149]. However, the causes of many miscarriages remain poorly understood. Recent genomic analyses of early pregnancy miscarriage revealed that pathogenic small-sequence variants and broader chromosomal abnormalities collectively account for a substantial fraction of cases, leading to one miscarriage in every 136 pregnancies, highlighting embryonic genome instability as a major contributor [150]. Furthermore, defects in DNA metabolism that permit live birth manifest as severe developmental syndromes. For instance, Meier-Gorlin syndrome arises from mutations in pre-RC components, leading to impaired replication licensing, replication stress, and microcephaly with primordial dwarfism [151]. Meanwhile, Fanconi Anemia, caused by faulty repair of DNA interstrand crosslinks, results in genomic instability and multisystem developmental defects [152]. Taken together, these observations underscore the profound influence of DNA metabolism on early embryonic development and its tight connection to human disease. In this section, we highlight how HTS-based assays have advanced our understanding of DNA replication, repair, and genome maintenance during early development, as well as their emerging clinical implications.

Current advances in DNA metabolism during early embryonic development

Understanding mammalian preimplantation development and embryonic stem cells (ESCs) biology requires a detailed view of DNA metabolism, particularly the establishment and maintenance of replication timing programs that safeguard genome stability. The faithful genome duplication is essential for successful development, yet replication timing, fork progression, and genome stability undergo profound reorganization from the zygote to the blastocyst stage (Figure 3). Recent advances in this field have largely centered on the replication timing program revealed by single-cell Repli-seq [13], 29].

Figure 3: DNA metabolism features during early embryonic development. HTS-based approaches have revealed dynamic alterations in DNA metabolism and genome stability throughout mammalian embryogenesis and in embryonic stem cells (ESCs). In mouse embryos, 4-cell stage embryos exhibit a replication timing pattern distinct from that of zygotes and 1-cell embryos. Replication fork speed also proceeds more slowly at this stage compared with later-stage embryos and ESCs, indicating a transitional remodeling of the DNA replication program. In human embryos, the accumulation of DNA damage increases progressively and peaks at the blastocyst stage, whereas in mouse embryos, DNA damage rises until the 4-cell stage. Clinically, HTS-based assays have become indispensable for preimplantation genetic testing, particularly in scenarios with limited input material such as non-invasive prenatal testing (NIPT). Created with BioRender.com.
Figure 3:

DNA metabolism features during early embryonic development. HTS-based approaches have revealed dynamic alterations in DNA metabolism and genome stability throughout mammalian embryogenesis and in embryonic stem cells (ESCs). In mouse embryos, 4-cell stage embryos exhibit a replication timing pattern distinct from that of zygotes and 1-cell embryos. Replication fork speed also proceeds more slowly at this stage compared with later-stage embryos and ESCs, indicating a transitional remodeling of the DNA replication program. In human embryos, the accumulation of DNA damage increases progressively and peaks at the blastocyst stage, whereas in mouse embryos, DNA damage rises until the 4-cell stage. Clinically, HTS-based assays have become indispensable for preimplantation genetic testing, particularly in scenarios with limited input material such as non-invasive prenatal testing (NIPT). Created with BioRender.com.

Human 1-cell embryos exhibit weakly defined replication timing and experience pronounced replication stress (Figure 3). Fork stalling and pervasive DNA synthesis extending into G2 phase give rise to chromosome breaks and aneuploidy during the first mitotic cell cycle [142]. The spontaneous chromosome breaks occur predominantly in gene-poor, late-replicating regions, suggesting the early establishment of late-replicating domains inherently predisposes these regions to fragility [139], 142], 144]. Consistently, mouse 1-cell and 2-cell embryos lack the conventional somatic replication timing program [13], 144]. Instead, replication proceeds gradually and uniformly due to extremely slow fork progression [13], 63]. A rudimentary late-replicating pattern emerges early, coinciding with lamina-associated regions (LARs) [143]. Moreover, DNA replication in mouse zygotes displays a striking sex-specific asymmetry, in which maternal pericentromeric regions replicate substantially later than their paternal counterparts [144].

A major developmental transition occurs at the 4-cell stage in mouse embryos, when a somatic-like replication timing program becomes evident [63]. This shift is accompanied by changes in nuclear compartmentalization [13], while replication fork speed remains slow and S phase is prolonged. This period features transient genomic instability, including elevated replication stress markers [13], culminating in a peak of break-type chromosome segregation errors during the 4-to-8-cell division [13]. Of note, the breakpoints of these errors localize consistently to late-replicating domains [13]. Regulatory factors such as RIF1 are central to this transition, evidenced by the observation that RIF1 depletion slows replication fork speed and alters replication timing pattern [153].

By the blastocyst stage and in derived mouse ESCs, the replication timing program becomes fully established and remarkably stable. Replication domain organization is highly conserved across individual mouse ESCs [29]. During differentiation, changes in chromosome architecture and A/B compartments closely parallel shifts in replication timing [145]. Human ESCs exhibit similar robust and well-defined replication timing, with minimal heterogeneity at the start and end of S phase [19], 29]. In human blastocysts, although acute replication stress diminishes, certain DNA damage repair structures emerge, suggesting that lesions accumulated earlier persist and continue to influence embryo developmental potential [142].

Together, these findings highlight the extensive reprogramming of DNA replication program as cells transition from the zygote to the pluripotent state. However, substantial gaps remain. While Repli-seq has provided detailed maps in human ESCs, direct high-throughput analyses of replication timing and genome stability in early human embryos are still lacking. This limitation constrains our understanding of replication dynamics and the origins of genomic instability in early human development. Future work should integrate HTS-based assays for DNA damage and repair, and employ single-cell multi-omics approaches to interrogate the interplay between DNA metabolism, chromatin architecture, and other nuclear processes during early embryogenesis.

DNA metabolism-associated HTS assays for clinical diagnosis during embryonic development

Based on these fundamental mechanism studies, the replication stress accumulated during rapid cell proliferation can lead to transient genomic instability [154]. This provides an important theoretical basis for detecting potential genomic abnormalities during development using HTS assays. Beyond basic research, HTS assays have been rapidly translated into clinical applications, particularly through preimplantation genetic testing (PGT) and parental genetic screening, enabling early detection of heritable and de novo genomic abnormalities [155], 156] (Figure 3). PGT involves the analysis of embryos cultivated through in vitro fertilization (IVF) prior to uterine transfer, with the goals of selecting chromosomally normal or mutation-free embryos, improving pregnancy success rates, and preventing transmission of genetic diseases [157]. Conventional PGT relies on biopsy of several trophectoderm cells from blastocyst-stage embryos [158]. The development of whole-genome amplification strategies, such as multiple annealing and looping based amplification cycles (MALBAC), has expanded PGT to the single-cell level, providing sufficient sensitivity to detect chromosomal aneuploidy or copy-number variants (CNVs) even when only a single cell is available [159], 160]. More recent approaches, such as mutated allele revealed by sequencing with aneuploidy and linkage analyses (MARSALA), further enable simultaneous detection of chromosomal abnormalities and single-nucleotide variants (SNVs) or monogenic mutations [161].

In parallel, analysis of cell-free DNA (cfDNA) has led to the development of non-invasive PGT (niPGT-A), which eliminates the risks associated with embryo biopsy (Figure 3). cfDNA consists of short DNA fragments released into extracellular fluids, including culture medium, blood plasma, or urine [162]. During preimplantation development, embryos release cfDNA into the spent culture medium, providing a non-invasive source of genetic material for HTS analysis. Because this cfDNA derives from both the trophectoderm and the inner cell mass [163], it may offer a more representative view of the embryonic genome than a trophectoderm biopsy with a few cells. Following implantation, HTS continues to play a transformative role through non-invasive prenatal testing (NIPT). By performing large-scale parallel sequencing of cfDNA in maternal plasma, this technology achieves highly accurate detection of fetal chromosomal abnormalities, such as trisomy 21 of Down syndrome [164]. The successful clinical implementation of cfDNA-based liquid biopsy approaches in prenatal diagnosis highlights their broader potential, including applications in oncology for early cancer detection, minimal residual disease monitoring, and treatment response assessment.

Application of DNA metabolism-related HTS technologies in cancer

In addition to early embryogenesis, DNA metabolism plays a central role in tumorigenesis and cancer progression. Embryogenesis and malignancy share striking molecular parallels, including rapid proliferation and elevated replication stress. In this context, malignant transformation can be conceptualized as a pathological recapitulation of early development. The elevated chronic replication stress underlies genomic instability, a defining hallmark of cancer [165], 166], arising from both endogenous processes, such as replication and repair errors, and exogenous insults, including oxidative stress, chemotherapeutic drugs, and ionizing radiation [87], 167] (Figure 4). Among these sources, aberrant DNA replication is the predominant endogenous driver of genome instability [10], 87], 168]. Oncogene activation, nucleotide insufficiency, and TRCs collectively induce replication stress, leading to fork stalling, collapse, and error-prone repair [10], 169]. These events generate copy-number alterations, chromosomal rearrangements, and widespread heterogeneity, which together fuel malignant evolution [12]. Understanding the replication-associated origins of instability, therefore, provides a crucial foundation for dissecting tumorigenic mechanisms and identifying therapeutic vulnerabilities.

Figure 4: Decoding DNA metabolism-associated genomic instability in cancers via HTS-based analyses. DNA replication dysregulation, defects in DNA damage repair pathways, and exposure to exogenous DNA-damaging agents collectively generate replication stress, a major driver of genomic instability in cancer. Damaged nuclear DNA, mtDNA, and aberrant DNA species, such as ecDNA and ctDNA, may subsequently enter the circulation and can be captured through liquid biopsy. HTS-based technologies enable mapping of replication timing, CFSs instability, and ecDNA dynamics across these molecular compartments, providing an integrated framework for understanding replication-driven tumor evolution and identifying biomarkers for cancer diagnosis, prognosis, and therapeutic response. Created with BioRender.com.
Figure 4:

Decoding DNA metabolism-associated genomic instability in cancers via HTS-based analyses. DNA replication dysregulation, defects in DNA damage repair pathways, and exposure to exogenous DNA-damaging agents collectively generate replication stress, a major driver of genomic instability in cancer. Damaged nuclear DNA, mtDNA, and aberrant DNA species, such as ecDNA and ctDNA, may subsequently enter the circulation and can be captured through liquid biopsy. HTS-based technologies enable mapping of replication timing, CFSs instability, and ecDNA dynamics across these molecular compartments, providing an integrated framework for understanding replication-driven tumor evolution and identifying biomarkers for cancer diagnosis, prognosis, and therapeutic response. Created with BioRender.com.

Given the diverse genomic contexts in which replication-associated instability manifests, a structured framework is necessary to clarify how distinct DNA components contribute to tumorigenesis and how they can be interrogated using HTS technologies (Figure 4). Below, we categorize these applications into several key dimensions, including autosomes and X chromosome, Y chromosome, mitochondrial DNA (mtDNA), and atypical DNA species such as extrachromosomal circular DNA (ecDNA) and circulating tumor DNA (ctDNA). For each category, we summarize the HTS-based methodologies employed, the biological insights they have revealed, and their translational implications for cancer diagnostics and therapy.

Autosomes and X chromosome

Nuclear DNA, comprising autosomes and sex chromosomes (X and Y), undergoes tightly regulated DNA replication to preserve genome stability. Its dysregulation is increasingly recognized as a central driving force of tumorigenesis [10], 12], 169]. HTS-based replication profiling, including Repli-seq, OK-seq, and Bubble-seq, has been extensively applied in cancer cell lines such as HeLa, U2OS, and HCT116 to delineate replication origin activation and timing regulation [19], 47], 48], 170]. These studies reveal that tumor cells frequently activate aberrant early-firing origins adjacent to oncogene clusters with gain of copy number or gene amplification (e.g., MYC, CCND1), indicating that deregulated replication initiation contributes to oncogene overexpression [23], 55], 171]. Complementary approaches such as RPA-ChIP, ssDNA-seq, and MiDAS-seq, applied in cells with hydroxyurea- or aphidicolin-induced replication stress, identify recurrent fork collapse and mitotic DNA synthesis regions [99], 132], 133], 172]. These analyses consistently pinpoint CFSs, frequently enriched for tumor suppressor genes (e.g., FHIT, WWOX), as hotspots of replication-associated breakage, linking replication stress to loss of tumor suppressor integrity [132], 133], 173]. Additionally, END-seq and TrAEL-seq applied in BRCA1/2-deficient and other HR-impaired cancer models map replication-associated DSBs at nucleotide resolution [70], 103], 174]. Collectively, these studies demonstrate that defective replication regulation drives extensive rearrangements and amplification events, providing mechanistic evidence that replication-associated DNA damage is a central force shaping oncogenic genome architectures.

Building upon these mechanistic insights, replication‐derived genomic features are beginning to inform clinical decision-making. Replication timing signatures stratify proliferative vs. quiescent tumor cell populations and correlate with chemoresistance in breast and colorectal cancers [26], 167], 175]. Replication fork asymmetry patterns detected by OK-seq and TrAEL-seq could serve as candidate biomarkers for tumors with replication stress dependency, suggesting synthetic lethal vulnerabilities to ATR or PARP inhibition [70], [176], [177], [178].

However, current replication-associated analysis remains limited at bulk-level resolution and incomplete characterization of intra-tumoral heterogeneity. Future integration of replication profiling with single-cell and multi-omics genomics will enable dynamic mapping of replication heterogeneity within tumor microenvironments and provide deeper insight into how timing plasticity and fragile-site instability drive clonal evolution and therapeutic resistance.

Y chromosome

The human Y chromosome exhibits a distinctive architecture enriched in palindromes, segmental duplications, and ampliconic gene clusters that are essential for male-specific functions yet confer intrinsic susceptibility to replication stress [86], 179], 180]. Owing to its largely haploid configuration and limited HR capacity, the Y chromosome exhibits replication-associated fragility that predisposes it to rearrangements within repetitive and heterochromatic domains [181], [182], [183]. HTS-based technologies are now illuminating the replication landscape of Y chromosome with increasing precision [184]. Repli-seq and OK-seq map asynchronous replication timing and aberrant fork polarity across euchromatic Y regions, whereas Strand-seq and ultra-long Nanopore sequencing resolve Y-specific replication intermediates and breakpoints, particularly within ampliconic loci such as DAZ, RBMY, and TSPY, which are frequently altered in germ cell tumors and prostate malignancies [19], 48], 86], 185], 186]. Integrative END-seq, TrAEL-seq, and ssDNA-seq analyses further identify palindromic and satellite-rich loci as hotspots of fork stalling and DSB formation [70], 71]. Of note, Y-linked replication stress propagates genome-wide instability, suggesting a broader “replication stress axis” that links Y fragility to global chromosomal maintenance [187], [188], [189].

These insights align with a growing appreciation that Y-chromosome instability contributes to male-biased tumorigenesis [190]. Loss of the Y chromosome (LOY), prevalent across both hematologic and solid tumors, is now mechanistically attributed to replication-induced fork collapse and chromothripsis-like events rather than solely mitotic errors [191], 192]. Replication-driven Y rearrangements can amplify oncogenic loci such as TSPY1 or dysregulate RNA-binding factors such as RBMY, thereby impairing DNA repair and promoting malignant progression in prostate and liver cancers [185], 190]. Replication stress-induced Y instability also reconfigures chromatin accessibility and transcriptional programs, potentially fostering oncogenic and immune-evasive states that underlie male-biased cancer susceptibility [193]. These findings carry emerging translational implications. cfDNA sequencing enables sensitive detection of LOY and Y-fragmentation patterns, offering potential biomarkers for aging, smoking-associated genomic damage, clonal hematopoiesis, and prostate cancer progression [194]. Tumors with Y chromosome loss or structural compromise exhibit heightened sensitivity to ATR/CHK1 inhibition due to impaired fork protection, suggesting that replication-based Y-chromosome instability may inform precision therapeutic strategies [195].

Mitochondrial DNA

Mammalian mtDNA is a 16.6 kb circular genome essential for oxidative phosphorylation [196]. Unlike nuclear DNA, mtDNA replicates independently of the cell cycle through an asynchronous strand-displacement mechanism governed by DNA polymerase γ (POLG), TWINKLE helicase, and mitochondrial single-stranded DNA-binding protein (mtSSB) [197], [198], [199]. This replication mode, coupled with high levels of reactive oxygen species (ROS), renders mtDNA particularly vulnerable to damage, replication errors, and structural alterations in cancer cells [200], 201].

HTS-based technologies have transformed the study of mtDNA replication. mtDNA-seq enables high-resolution detection of mtDNA replication intermediates, heteroplasmy, and structural variants [202], 203]. Recently, BrdU and EdU-based profiling have been adapted to capture nascent mtDNA synthesis, providing insights into replication origin activity and fork directionality in mitochondrial networks [204], 205]. Furthermore, assay for transposase-accessible chromatin with sequencing (ATAC-seq) and ChIP-seq analyses show that the mitochondrial transcription factor A (TFAM) not only packages mtDNA into nucleoids but also modulates replication initiation [206], 207]. Integrative studies of mtDNA replication and transcription reveal a tight coupling between initiation of replication and transcriptional activity in mitochondria, indicating that these processes are coordinated and that dysregulation of the replication-transcription balance can contribute to mtDNA instability; this coupling is reflected in mechanisms where transcriptional machinery influences replication primer formation and where factors such as TEFM act as molecular switches between transcription and replication, implying potential sites of TRCs within mtDNA [208], 209]. Together, these tools provide a systematic framework for dissecting how oncogenic stress perturbs mtDNA replication.

Mitochondrial replication stress acts as a metabolic and genomic hub linking mitochondrial dysfunction to tumor evolution [210]. Aberrant mtDNA copy number, either depletion or amplification, is reported in various malignancies, including hepatocellular carcinoma, breast cancer, and glioma [211], 212]. Mechanistically, polymerase-gamma (POLG) mutations and replication stress compromise mtDNA integrity, disrupt oxidative phosphorylation, and promote metabolic reprogramming toward aerobic glycolysis, the Warburg effect, thereby supporting tumor proliferation under hypoxia [213], 214]. Replication defects can trigger mitochondrial unfolded protein responses (UPRmt) and ROS overproduction, which in turn induce nuclear DNA damage and activate oncogenic signaling pathways such as PI3K/AKT and MAPK [215], 216]. Recent long-read Nanopore sequencing has revealed extensive mtDNA deletions arising from replication slippage and fork stalling in pancreatic and ovarian cancer, correlating with chemoresistance and metastasis [217].

Clinically, mtDNA copy number alterations, mutation spectra, and replication intermediates can be readily detected in cell-free mtDNA from plasma, saliva, and urine, providing biomarkers of tumor burden and mitochondrial activity [218], 219]. Features of mtDNA-associated replication stress are emerging as indicators of therapeutic response: preclinical evidence suggests that reduced mtDNA copy number and POLG defects can modulate cellular responses to perturbations in mitochondrial function and nucleotide homeostasis, and may influence sensitivity to agents targeting mitochondrial biogenesis or nucleotide salvage pathways, highlighting potential vulnerabilities linked to mitochondrial replication dysfunction [220], 221]. In addition, HTS-based monitoring of mtDNA replication recovery after chemotherapy may offer a dynamic readout of mitochondrial resilience and treatment outcome [222].

Extrachromosomal circular DNA

Beyond chromosomal and mtDNA, aberrant metabolism of atypical DNA also contributes to genome instability and tumor evolution (Figure 4). ecDNA is widespread in tumors and serves as a highly dynamic vehicle for oncogene amplification [223], [224], [225]. Unlike chromosomal DNA, ecDNA lacks histone-mediated packaging constraints, resulting in greater accessibility and dynamic replicative capacity [226]. ecDNA originates from aberrant replication origin firing, fork collapse, and error-prone DNA damage repair pathways such as microhomology-mediated end joining (MMEJ) [227]. Its open configuration and rapid replication confer strong cellular heterogeneity and evolutionary advantage, enabling high-level amplification of oncogenes such as EGFR, MYC, and MDM2, which promote tumor growth and drug resistance [223], 224].

HTS-based approaches such as scCircle-Seq, ATAC-seq, AmpliconArchitect, and long-read sequencing facilitate genome-wide identification and structural reconstruction of ecDNA [228], [229], [230]. Integration with replication timing profiling and fork directionality analyses suggests that ecDNA molecules exhibit replication dynamics distinct from canonical chromosomal DNA, including asynchronous or preferential replication behavior that may reflect an independent mode of replication regulation in tumor cells [231], 232]. High-resolution mapping methods further pinpoint replication-derived breakage and repair hotspots that can contribute to ecDNA biogenesis and structural evolution [229], 233], 234]. Functionally, ecDNA enhances transcription through replication-transcription coupling and undergoes asymmetric segregation, enabling rapid adjustment of oncogene copy numbers under chemotherapeutic stress, thereby conferring drug resistance [229], 235]. Clinically, ecDNA abundance predicts poor prognosis, and its amplified oncogenic units represent emerging therapeutic targets [229]. Current strategies aimed at disrupting ecDNA reintegration or its replication in tumor cells, such as targeting DNA replication machinery or associated stress response pathways, show promise in diminishing the selective advantage conferred by ecDNA [236].

Circulating tumor DNA

ctDNA retains structural and biochemical features of its genomic origin, including nucleosome positioning, fragment length periodicity, methylation-associated cleavage preferences, and signatures of accessible chromatin, providing a unique window into tumor DNA metabolism and chromatin organization from liquid biopsies [237], 238]. Replication stress produces characteristic fragmentation patterns via fork stalling, breakage, and mitotic mis-segregation, generating DNA fragments that can be released into the circulation following cell death or chromosomal instability events [239], 240]. HTS-based technologies such as ultra-deep WGS and advanced fragmentomic analyses reveal that ctDNA fragment lengths, nucleotide periodicity, and breakpoint topology encode information about replication origin proximity and replication timing, thereby capturing tumor-specific replication stress [239], 241], 242]. Foundational replication timing profiling using Repli-seq has established that temporal replication domains are associated with distinct chromatin states and genomic stability landscapes, providing a mechanistic context for interpreting ctDNA fragmentation in relation to replication stress [19], 243]. Although direct integration of OK-seq with ctDNA-seq in tumor studies has not yet been fully realized, principles from replication timing and chromatin structure research support the concept that circulating fragments can be enriched from early-replicating domains under oncogene-induced replication stress, connecting replication dynamics with downstream cfDNA fragmentation signatures [244], 245].

These replication-dependent features provide insights into tumor biology. ctDNA copy-number oscillations and fork-asymmetry signatures correlate with replication stress phenotypes and predict therapeutic response, as demonstrated in non-small cell lung cancer (NSCLC) treated with EGFR tyrosine kinase inhibitors [239], 246], 247]. Similarly, ctDNA methylation, fragment-end motifs, and phased nucleosome footprints aligned to replication timing domains improve early cancer detection and molecular subtyping in colorectal and breast cancers [239], 248]. The convergent features of ctDNA and ecDNA further highlight replication-driven genome instability in cancer, which contributes to tumor evolution and drug resistance. Clinically, replication-focused ctDNA profiling is becoming a powerful modality for early cancer diagnosis, minimal residual disease detection, and therapy stratification [249], 250]. Replication timing-derived fragmentomics enhances sensitivity for low-tumor-fraction cancers, while ctDNA-based repair-deficiency signatures and fork-collapse markers help identify patients most likely to benefit from ATR, CHK1, PARP, or replication stress-targeted therapies [251], 252]. Collectively, elucidating the metabolic and structural vulnerabilities of the cancer genome not only refines diagnostic strategies but also reveals actionable therapeutic targets for next-generation interventions, thereby advancing the frontier of precision oncology.

Application of DNA metabolism-related HTS technologies in DSB-based genome editing

Cancer therapies are evolving from traditional small-molecule inhibitors toward increasingly sophisticated combination strategies. Within this landscape, genome editing therapies have emerged as a powerful approach capable of directly rewiring cellular function or engineering therapeutic cells [253]. For instance, genome-engineered chimeric antigen receptor (CAR) T cells have shown remarkable efficacy in hematologic malignancies [254] and are now being explored in autoimmune disorders [255], 256], chronic infections [257] and so on [258].

Most widely used genome editing tools, such as CRISPR/Cas9 and its derivatives, introduce DSBs or single-strand nicks to stimulate endogenous DNA repair pathways [259], 260] (Figure 5). However, these lesions are themselves sources of genomic instability and can generate unintended mutations, structural alterations, and even oncogenic translocations [261], [262], [263], [264], [265], [266], [267]. Since DNA replication and the DNA damage response govern repair pathways choice or the spectrum of repair products [268], they critically influence editing efficiency, precision, and long-term safety. Consequently, the development and clinical application of genome editing therapies rely heavily on HTS to quantify on-target outcomes, identify off-target cleavage sites, and monitor structural variations and long-term genomic consequences resulting from DSB repair [269], [270], [271].

Figure 5: Application of HTS-based assays in DSB-mediated genome editing therapies. The upper panel outlines pre-treatment assessment, including detection of intended editing outcomes (such as deletions, insertions, and translocations) and comprehensive evaluation of off-target activity to ensure efficacy and specificity before therapeutic application. The lower-right panel summarizes delivery strategies, distinguishing in vivo delivery approaches (e.g., viral or nanoparticle-based systems) from ex vivo editing workflows in which cells are modified outside the body and subsequently reinfused, such as CAR-T. The lower-left panel illustrates post-treatment safety evaluation and long-term tracing, where HTS-based assays support monitoring of in vivo off-target events, clonal behavior, and the persistence of edited cell populations throughout follow-up. Created with BioRender.com.
Figure 5:

Application of HTS-based assays in DSB-mediated genome editing therapies. The upper panel outlines pre-treatment assessment, including detection of intended editing outcomes (such as deletions, insertions, and translocations) and comprehensive evaluation of off-target activity to ensure efficacy and specificity before therapeutic application. The lower-right panel summarizes delivery strategies, distinguishing in vivo delivery approaches (e.g., viral or nanoparticle-based systems) from ex vivo editing workflows in which cells are modified outside the body and subsequently reinfused, such as CAR-T. The lower-left panel illustrates post-treatment safety evaluation and long-term tracing, where HTS-based assays support monitoring of in vivo off-target events, clonal behavior, and the persistence of edited cell populations throughout follow-up. Created with BioRender.com.

HTS-based technologies for evaluating on-target editing outcomes

Accurate quantification of on-target editing efficiency and product spectrum, including base substitutions, small insertions and deletions (indels), and large deletions, is essential for both preclinical and clinical evaluation (Figure 5). Early studies and current clinical workflows commonly employ amplicon sequencing, targeted capture sequencing, or whole-genome sequencing (WGS) to measure on-target editing rates and detect unintended variants before and after product preparation or administration [272], [273], [274]. To resolve larger or more structurally complex outcomes, long-read sequencing technologies or long-distance amplification combined with next-generation sequencing are used to capture structural variations spanning kilobase-scale regions [275]. To quantitatively characterize the full spectrum of repair products, specialized assays have been developed. Among them, PEM-seq enables simultaneous detection of small variations at the on-target locus as well as large-scale rearrangements or translocations involving the on-target site, thereby providing a comprehensive depiction of Cas9-induced repair outcomes [128], 129].

HTS-based technologies for off-target detection

Off-target cleavage remains a critical safety concern in genome editing applications (Figure 5). In practical workflows, a common strategy is to use multiple complementary approaches. In silico prediction [276] and in vitro assays, such as CIRCLE-seq [277] and SITE-seq [278], are first employed to generate sensitive, genome-wide off-target maps. These candidates are then validated in cells or clinical samples using HTS-based methods, including LAM-HTGTS [279], GUIDE-seq [130], DISCOVER-seq [115], WGS [280], or targeted resequencing. For example, in manufacturing clinical-grade CAR-T products, GUIDE-seq, SITE-seq, and WGS have been combined to screen and validate candidate off-target sites, with site-specific amplification sequencing used for quantitative confirmation of overlapping sites [270]. This tiered strategy balances sensitivity with biological relevance and provides robust evidence for clinical safety assessments.

Applications of HTS-based technologies for long-term in vivo safety monitoring and clonal tracing

Beyond short-term on- and off-target evaluations, long-term genomic stability and clonal dynamics of edited cells are critical determinants of clinical safety (Figure 5). Key questions include whether edited cells persist or expand in vivo, whether editing-induced genomic instability diminishes over time, and whether they acquire new chromatin structural variations. HTS-based technologies play an irreplaceable role in monitoring these processes. One classic method to longitudinally trace the fate of edited cells is clonal tracking via barcoding and sequencing, such as BAR-Seq. Using this approach, Ferrari et al. demonstrated that genome editing triggers p53 activation, reducing clonal diversity. However, transient p53 inhibition and enhanced cell-cycle progression allowed edited clones to persist long-term with stable multilineage output [281]. Examining CRISPR/Cas9-induced structural variations (SVs), including chromosomal translocations and large deletions, in a mouse adoptive T-cell transfer model, Wu et al. reported that these SVs persisted for weeks to months post-infusion and underwent clonal expansion, raising important safety concerns [282]. On the clinical side, Edwards et al. reported that CRISPR-engineered T cells infused into cancer patients exhibited durable persistence. While some chromosomal translocations were initially detected, their frequencies declined over time, suggesting an initial risk but also indicating partial resolution or negative selection against deleterious variants [283].

Another key determinant of editing efficacy and safety is the delivery strategy used to introduce genome-editing components (Figure 5). Therapeutic genome editing currently relies on two primary paradigms: ex vivo editing, in which cells are isolated, engineered, and expanded outside the body before reinfusion, and in vivo delivery, where editing systems are directly introduced into tissues using via viral vectors (e.g., AAVs) [284] or non-viral formulations such as lipid nanoparticles (LNPs) [285]. As genome-editing strategies are shifting from ex vivo manipulation toward in vivo delivery, additional layers of safety assessment have become necessary. In vivo editing increases the potential for unintended modifications in off-target tissues or cell populations [263], 286], 287], necessitating sensitive HTS-based assays to evaluate genome-wide editing outcomes across diverse biological compartments, which is an ongoing technical challenge.

Traditional short-read amplicon sequencing excels at detecting small indels, but often fails to capture large deletions, insertions, inversions, complex rearrangements, or chromosomal breaks. Complementarily, long-read sequencing combined with structural variant analysis enables a comprehensive detection of these complex editing events [288]. For instance, Mary et al. used long-read sequencing to an AAV-CRISPR editing model of Duchenne muscular dystrophy (DMD), uncovering frequent on-target large insertions and inversions [289]. These findings underscore the importance of long-read technologies for robust safety evaluation of genome editing.

Conclusion and perspectives

A broad suite of HTS-based technologies has been developed to study DNA metabolism. This review has highlighted those designed to monitor DNA replication and DNA damage repair, outlining their principles, technical features, and applications across physiological development, pathological contexts, and emerging therapeutic strategies. As these HTS-based technologies continue to evolve, several trajectories are shaping their future impact.

Resolution is steadily advancing toward the single-cell and single-molecule scale, enabling detection of replication and repair heterogeneity that bulk assays obscure. Multi-omics integration is becoming routine, linking replication and repair readouts to the epigenome, transcriptome, proteome, and 3D genome organization. Improvements in quantitative precision and sensitivity are allowing increasingly accurate assessments of fork kinetics, damage occurrence, and repair efficiencies. In addition, combining spatially resolved transcriptomics, epigenomics, and proteomics with high-resolution profiling of DNA metabolism will allow DNA replication and damage repair processes to be mapped in situ, revealing how they vary across tissue niches and evolve over time. Such approaches promise to uncover clonal trajectories, microenvironment interactions, and spatially localized genomic perturbations. In parallel, enhanced compatibility with low-input samples is enabling applications to early embryos, rare tumor sub-populations, and clinically limited biopsies. Together, these methodological advances will deepen our understanding of genome stability maintenance mechanisms and accelerate the translation of genome-editing therapies and DNA-metabolism research into clinical practice.

Corresponding author: Yang Liu, Department of Medical Genetics, Center for Medical Genetics, School of Basic Medical Sciences, Peking University, Beijing, 100871, China, E-mail:
Jingzhi Luo, Fanyu Zhao and Shuyan Lin contributed equally to this work.

Funding source: Clinical Medicine Plus X-Young Scholars Project, Peking University

Funding source: The Fundamental Research Funds for the Central Universities

Funding source: Excellent Young Scientists Fund

Award Identifier / Grant number: 32522018

Acknowledgments

We thank all members of the Liu laboratory for their insightful comments and constructive feedback. We also apologize to colleagues whose valuable work could not be cited due to space constraints.

  1. Research ethics: IRB approval is not applicable to this work.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. Jingzhi Luo, Conceptualization, Writing original draft, Writing review & editing, Visualization. Fanyu Zhao, Writing review & editing, Visualization. Shuyan Lin, Writing review& editing, Visualization. Yang Liu, Conceptualization, writing review & editing, Supervision, Funding acquisition.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: This work was supported by the NSFC grant (32522018 to Y.L.) and the Clinical Medicine Plus X-Young Scholars Project, Peking University, the Fundamental Research Funds for the Central Universities.

  7. Data availability: Not applicable.

References

1. Li, Z, Zhang, Z. A tale of two strands: decoding chromatin replication through strand-specific sequencing. Mol Cell 2025;85:238–61. https://doi.org/10.1016/j.molcel.2024.10.035.Search in Google Scholar PubMed PubMed Central

2. Burgers, PMJ, Kunkel, TA. Eukaryotic DNA replication fork. Annu Rev Biochem 2017;86:417–38. https://doi.org/10.1146/annurev-biochem-061516-044709.Search in Google Scholar PubMed PubMed Central

3. Chiolo, I, Altmeyer, M, Legube, G, Mekhail, K. Nuclear and genome dynamics underlying DNA double-strand break repair. Nat Rev Mol Cell Biol 2025;26:538–57. https://doi.org/10.1038/s41580-025-00828-1.Search in Google Scholar PubMed PubMed Central

4. Aguilera, A, Gómez-González, B. Genome instability: a mechanistic view of its causes and consequences. Nat Rev Genet 2008;9:204–17. https://doi.org/10.1038/nrg2268.Search in Google Scholar PubMed

5. Bickmore, WA, van Steensel, B. Genome architecture: domain organization of interphase chromosomes. Cell 2013;152:1270–84. https://doi.org/10.1016/j.cell.2013.02.001.Search in Google Scholar PubMed

6. Wong, LH, Tremethick, DJ. Multifunctional histone variants in genome function. Nat Rev Genet 2025;26:82–104. https://doi.org/10.1038/s41576-024-00759-1.Search in Google Scholar PubMed

7. Groth, A, Rocha, W, Verreault, A, Almouzni, G. Chromatin challenges during DNA replication and repair. Cell 2007;128:721–33. https://doi.org/10.1016/j.cell.2007.01.030.Search in Google Scholar PubMed

8. Papamichos-Chronakis, M, Peterson, CL. Chromatin and the genome integrity network. Nat Rev Genet 2013;14:62–75. https://doi.org/10.1038/nrg3345.Search in Google Scholar PubMed PubMed Central

9. Alabert, C, Groth, A. Chromatin replication and epigenome maintenance. Nat Rev Mol Cell Biol 2012;13:153–67. https://doi.org/10.1038/nrm3288.Search in Google Scholar PubMed

10. Zeman, MK, Cimprich, KA. Causes and consequences of replication stress. Nat Cell Biol 2014;16:2–9. https://doi.org/10.1038/ncb2897.Search in Google Scholar PubMed PubMed Central

11. Técher, H, Koundrioukoff, S, Nicolas, A, Debatisse, M. The impact of replication stress on replication dynamics and DNA damage in vertebrate cells. Nat Rev Genet 2017;18:535–50. https://doi.org/10.1038/nrg.2017.46.Search in Google Scholar PubMed

12. Gaillard, H, García-Muse, T, Aguilera, A. Replication stress and cancer. Nat Rev Cancer 2015;15:276–89. https://doi.org/10.1038/nrc3916.Search in Google Scholar PubMed

13. Takahashi, S, Kyogoku, H, Hayakawa, T, Miura, H, Oji, A, Kondo, Y, et al.. Embryonic genome instability upon DNA replication timing program emergence. Nature 2024;633:686–94. https://doi.org/10.1038/s41586-024-07841-y.Search in Google Scholar PubMed PubMed Central

14. Bentley, DR, Balasubramanian, S, Swerdlow, HP, Smith, GP, Milton, J, Brown, CG, et al.. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 2008;456:53–9. https://doi.org/10.1038/nature07517.Search in Google Scholar PubMed PubMed Central

15. Travers, KJ, Chin, CS, Rank, DR, Eid, JS, Turner, SW. A flexible and efficient template format for circular consensus sequencing and SNP detection. Nucleic Acids Res 2010;38:e159. https://doi.org/10.1093/nar/gkq543.Search in Google Scholar PubMed PubMed Central

16. Eid, J, Fehr, A, Gray, J, Luong, K, Lyle, J, Otto, G, et al.. Real-time DNA sequencing from single polymerase molecules. Science 2009;323:133–8. https://doi.org/10.1126/science.1162986.Search in Google Scholar PubMed

17. Pennisi, E. Genome sequencing. Search for pore-fection. Science 2012;336:534–7. https://doi.org/10.1126/science.336.6081.534.Search in Google Scholar PubMed

18. Reuter, JA, Spacek, DV, Snyder, MP. High-throughput sequencing technologies. Mol Cell 2015;58:586–97. https://doi.org/10.1016/j.molcel.2015.05.004.Search in Google Scholar PubMed PubMed Central

19. Zhao, PA, Sasaki, T, Gilbert, DM. High-resolution repli-seq defines the temporal choreography of initiation, elongation and termination of replication in mammalian cells. Genome Biol 2020;21:76. https://doi.org/10.1186/s13059-020-01983-8.Search in Google Scholar PubMed PubMed Central

20. Rojas, P, Wang, J, Guglielmi, G, Sadurnì, MM, Pavlou, L, Leung, GHD, et al.. Genome-wide identification of replication fork stalling/pausing sites and the interplay between RNA pol II transcription and DNA replication progression. Genome Biol 2024;25:126. https://doi.org/10.1186/s13059-024-03278-8.Search in Google Scholar PubMed PubMed Central

21. Wu, X, Liu, Y, d’Aubenton-Carafa, Y, Thermes, C, Hyrien, O, Chen, CL, et al.. Genome-wide measurement of DNA replication fork directionality and quantification of DNA replication initiation and termination with okazaki fragment sequencing. Nat Protoc 2023;18:1260–95. https://doi.org/10.1038/s41596-022-00793-5.Search in Google Scholar PubMed

22. Pfeifer, GP, Jin, SG. Methods and applications of genome-wide profiling of DNA damage and rare mutations. Nat Rev Genet 2024;25:846–63. https://doi.org/10.1038/s41576-024-00748-4.Search in Google Scholar PubMed PubMed Central

23. Murat, P, Guilbaud, G, Sale, JE. DNA replication initiation drives focal mutagenesis and rearrangements in human cancers. Nat Commun 2024;15:10850. https://doi.org/10.1038/s41467-024-55148-3.Search in Google Scholar PubMed PubMed Central

24. Tomkova, M, Tomek, J, Kriaucionis, S, Schuster-Böckler, B. Mutational signature distribution varies with DNA replication timing and strand asymmetry. Genome Biol 2018;19:129. https://doi.org/10.1186/s13059-018-1509-y.Search in Google Scholar PubMed PubMed Central

25. Claussin, C, Vazquez, J, Whitehouse, I. Single-molecule mapping of replisome progression. Mol Cell 2022;82:1372–82 e4. https://doi.org/10.1016/j.molcel.2022.02.010.Search in Google Scholar PubMed PubMed Central

26. Josephides, JM, Chen, CL. Unravelling single-cell DNA replication timing dynamics using machine learning reveals heterogeneity in cancer progression. Nat Commun 2025;16:1472. https://doi.org/10.1038/s41467-025-56783-0.Search in Google Scholar PubMed PubMed Central

27. Bai, D, Cao, Z, Attada, N, Song, J, Zhu, C. Single-cell parallel analysis of DNA damage and transcriptome reveals selective genome vulnerability. Nat Methods 2025;22:962–72. https://doi.org/10.1038/s41592-025-02632-3.Search in Google Scholar PubMed PubMed Central

28. van den Berg, J, van Batenburg, V, Geisenberger, C, Tjeerdsma, RB, de Jaime-Soguero, A, Acebron, SP, et al.. Quantifying DNA replication speeds in single cells by scEdU-seq. Nat Methods 2024;21:1175–84. https://doi.org/10.1038/s41592-024-02308-4.Search in Google Scholar PubMed PubMed Central

29. Takahashi, S, Miura, H, Shibata, T, Nagao, K, Okumura, K, Ogata, M, et al.. Genome-wide stability of the DNA replication program in single mammalian cells. Nat Genet 2019;51:529–40. https://doi.org/10.1038/s41588-019-0347-5.Search in Google Scholar PubMed

30. Macaulay, IC, Teng, MJ, Haerty, W, Kumar, P, Ponting, CP, Voet, T. Separation and parallel sequencing of the genomes and transcriptomes of single cells using G&T-seq. Nat Protoc 2016;11:2081–103. https://doi.org/10.1038/nprot.2016.138.Search in Google Scholar PubMed

31. Shetty, AV, Steer, CJ, Low, WC. Single cell multiomics approach to analyze replication timing and gene expression in mouse preimplantation embryos. Commun Biol 2025;8:1245. https://doi.org/10.1038/s42003-025-08694-5.Search in Google Scholar PubMed PubMed Central

32. Hu, Y, Stillman, B. Origins of DNA replication in eukaryotes. Mol Cell 2023;83:352–72. https://doi.org/10.1016/j.molcel.2022.12.024.Search in Google Scholar PubMed PubMed Central

33. Costa, A, Diffley, JFX. The initiation of eukaryotic DNA replication. Annu Rev Biochem 2022;91:107–31. https://doi.org/10.1146/annurev-biochem-072321-110228.Search in Google Scholar PubMed

34. Lewis, JS, Gross, MH, Sousa, J, Henrikus, SS, Greiwe, JF, Nans, A, et al.. Mechanism of replication origin melting nucleated by CMG helicase assembly. Nature 2022;606:1007–14. https://doi.org/10.1038/s41586-022-04829-4.Search in Google Scholar PubMed PubMed Central

35. Wasserman, MR, Schauer, GD, O’Donnell, ME, Liu, S. Replication fork activation is enabled by a single-stranded DNA gate in CMG helicase. Cell 2019;178:600–11.e16. https://doi.org/10.1016/j.cell.2019.06.032.Search in Google Scholar PubMed PubMed Central

36. Dewar, JM, Walter, JC. Mechanisms of DNA replication termination. Nat Rev Mol Cell Biol 2017;18:507–16. https://doi.org/10.1038/nrm.2017.42.Search in Google Scholar PubMed PubMed Central

37. Dewar, JM, Budzowska, M, Walter, JC. The mechanism of DNA replication termination in vertebrates. Nature 2015;525:345–50. https://doi.org/10.1038/nature14887.Search in Google Scholar PubMed PubMed Central

38. Hulke, ML, Massey, DJ, Koren, A. Genomic methods for measuring DNA replication dynamics. Chromosome Res 2020;28:49–67. https://doi.org/10.1007/s10577-019-09624-y.Search in Google Scholar PubMed PubMed Central

39. Wheeler, E, Mickelson-Young, L, Wear, EE, Burroughs, M, Bass, HW, Concia, L, et al.. A comparison of genomic methods to assess DNA replication timing. Sci Rep 2025;15:17761. https://doi.org/10.1038/s41598-025-02699-0.Search in Google Scholar PubMed PubMed Central

40. Liu, Y, Zhangding, Z, Liu, X, Hu, J. Chromatin-centric insights into DNA replication. Trends Genet 2025;41:412–24. https://doi.org/10.1016/j.tig.2024.12.003.Search in Google Scholar PubMed

41. Fajri, N, Petryk, N. Monitoring and quantifying replication fork dynamics with high-throughput methods. Commun Biol 2024;7:729. https://doi.org/10.1038/s42003-024-06412-1.Search in Google Scholar PubMed PubMed Central

42. Sugimoto, N, Maehara, K, Yoshida, K, Ohkawa, Y, Fujita, M. Genome-wide analysis of the spatiotemporal regulation of firing and dormant replication origins in human cells. Nucleic Acids Res 2018;46:6683–96. https://doi.org/10.1093/nar/gky476.Search in Google Scholar PubMed PubMed Central

43. Reuter, LM, Khadayate, SP, Mossler, A, Liebl, K, Faull, SV, Karimi, MM, et al.. MCM2-7 loading-dependent ORC release ensures genome-wide origin licensing. Nat Commun 2024;15:7306. https://doi.org/10.1038/s41467-024-51538-9.Search in Google Scholar PubMed PubMed Central

44. Akerman, I, Kasaai, B, Bazarova, A, Sang, PB, Peiffer, I, Artufel, M, et al.. A predictable conserved DNA base composition signature defines human core DNA replication origins. Nat Commun 2020;11:4826. https://doi.org/10.1038/s41467-020-18527-0.Search in Google Scholar PubMed PubMed Central

45. Langley, AR, Graf, S, Smith, JC, Krude, T. Genome-wide identification and characterisation of human DNA replication origins by initiation site sequencing (ini-seq). Nucleic Acids Res 2016;44:10230–47. https://doi.org/10.1093/nar/gkw760.Search in Google Scholar PubMed PubMed Central

46. Guilbaud, G, Murat, P, Wilkes, HS, Lerner, LK, Sale, JE, Krude, T. Determination of human DNA replication origin position and efficiency reveals principles of initiation zone organisation. Nucleic Acids Res 2022;50:7436–50. https://doi.org/10.1093/nar/gkac555.Search in Google Scholar PubMed PubMed Central

47. Mesner, LD, Valsakumar, V, Cieslik, M, Pickin, R, Hamlin, JL, Bekiranov, S. Bubble-seq analysis of the human genome reveals distinct chromatin-mediated mechanisms for regulating early- and late-firing origins. Genome Res 2013;23:1774–88. https://doi.org/10.1101/gr.155218.113.Search in Google Scholar PubMed PubMed Central

48. Petryk, N, Kahli, M, d’Aubenton-Carafa, Y, Jaszczyszyn, Y, Shen, Y, Silvain, M, et al.. Replication landscape of the human genome. Nat Commun 2016;7:10208. https://doi.org/10.1038/ncomms10208.Search in Google Scholar PubMed PubMed Central

49. Liu, Y, Ai, C, Gan, T, Wu, J, Jiang, Y, Liu, X, et al.. Transcription shapes DNA replication initiation to preserve genome integrity. Genome Biol 2021;22:176. https://doi.org/10.1186/s13059-021-02390-3.Search in Google Scholar PubMed PubMed Central

50. Matsumoto, A, Daigaku, Y, Tsubouchi, T. Polymerase-usage sequencing identifies initiation zones with less bias across S phase in mouse embryonic stem cells. J Biochem 2025;177:213–23. https://doi.org/10.1093/jb/mvae097.Search in Google Scholar PubMed PubMed Central

51. Koyanagi, E, Kakimoto, Y, Minamisawa, T, Yoshifuji, F, Natsume, T, Higashitani, A, et al.. Global landscape of replicative DNA polymerase usage in the human genome. Nat Commun 2022;13:7221. https://doi.org/10.1038/s41467-022-34929-8.Search in Google Scholar PubMed PubMed Central

52. Pratto, F, Brick, K, Cheng, G, Lam, KG, Cloutier, JM, Dahiya, D, et al.. Meiotic recombination mirrors patterns of germline replication in mice and humans. Cell 2021;184:4251–67.e20. https://doi.org/10.1016/j.cell.2021.06.025.Search in Google Scholar PubMed PubMed Central

53. Rossetti, GG, Dommann, N, Karamichali, A, Dionellis, VS, Asensio Aldave, A, Yarahmadov, T, et al.. In vivo DNA replication dynamics unveil aging-dependent replication stress. Cell 2024;187:6220–34 e13. https://doi.org/10.1016/j.cell.2024.08.034.Search in Google Scholar PubMed

54. Macheret, M, Halazonetis, TD. Monitoring early S-phase origin firing and replication fork movement by sequencing nascent DNA from synchronized cells. Nat Protoc 2019;14:51–67. https://doi.org/10.1038/s41596-018-0081-y.Search in Google Scholar PubMed

55. Macheret, M, Halazonetis, TD. Intragenic origins due to short G1 phases underlie oncogene-induced DNA replication stress. Nature 2018;555:112–6. https://doi.org/10.1038/nature25507.Search in Google Scholar PubMed PubMed Central

56. Muller, CA, Boemo, MA, Spingardi, P, Kessler, BM, Kriaucionis, S, Simpson, JT, et al.. Capturing the dynamics of genome replication on individual ultra-long nanopore sequence reads. Nat Methods 2019;16:429–36. https://doi.org/10.1038/s41592-019-0394-y.Search in Google Scholar PubMed PubMed Central

57. Carrington, JT, Wilson, RHC, de La Vega, E, Thiyagarajan, S, Barker, T, Catchpole, L, et al.. Most human DNA replication initiation is dispersed throughout the genome with only a minority within previously identified initiation zones. Genome Biol 2025;26:122. https://doi.org/10.1186/s13059-025-03591-w.Search in Google Scholar PubMed PubMed Central

58. Madireddy, A, Gerhardt, J. Visualizing DNA replication by single-molecule analysis of replicated DNA. STAR Protoc 2023;4:102721. https://doi.org/10.1016/j.xpro.2023.102721.Search in Google Scholar PubMed PubMed Central

59. Norio, P, Schildkraut, CL. Visualization of DNA replication on individual epstein-barr virus episomes. Science 2001;294:2361–4. https://doi.org/10.1126/science.1064603.Search in Google Scholar PubMed

60. Gerhardt, J, Tomishima, MJ, Zaninovic, N, Colak, D, Yan, Z, Zhan, Q, et al.. The DNA replication program is altered at the FMR1 locus in fragile X embryonic stem cells. Mol Cell 2014;53:19–31. https://doi.org/10.1016/j.molcel.2013.10.029.Search in Google Scholar PubMed PubMed Central

61. Wang, W, Klein, KN, Proesmans, K, Yang, H, Marchal, C, Zhu, X, et al.. Genome-wide mapping of human DNA replication by optical replication mapping supports a stochastic model of eukaryotic replication. Mol Cell 2021;81:2975–88 e6. https://doi.org/10.1016/j.molcel.2021.05.024.Search in Google Scholar PubMed PubMed Central

62. Marchal, C, Sima, J, Gilbert, DM. Control of DNA replication timing in the 3D genome. Nat Rev Mol Cell Biol 2019;20:721–37. https://doi.org/10.1038/s41580-019-0162-y.Search in Google Scholar PubMed PubMed Central

63. Nakatani, T, Schauer, T, Altamirano-Pacheco, L, Klein, KN, Ettinger, A, Pal, M, et al.. Emergence of replication timing during early Mammalian development. Nature 2024;625:401–9. https://doi.org/10.1038/s41586-023-06872-1.Search in Google Scholar PubMed PubMed Central

64. Dileep, V, Gilbert, DM. Single-cell replication profiling to measure stochastic variation in mammalian replication timing. Nat Commun 2018;9:427. https://doi.org/10.1038/s41467-017-02800-w.Search in Google Scholar PubMed PubMed Central

65. Smith, DJ, Whitehouse, I. Intrinsic coupling of lagging-strand synthesis to chromatin assembly. Nature 2012;483:434–8. https://doi.org/10.1038/nature10895.Search in Google Scholar PubMed PubMed Central

66. McGuffee, SR, Smith, DJ, Whitehouse, I. Quantitative, genome-wide analysis of eukaryotic replication initiation and termination. Mol Cell 2013;50:123–35. https://doi.org/10.1016/j.molcel.2013.03.004.Search in Google Scholar PubMed PubMed Central

67. Hennion, M, Arbona, J-M, Lacroix, L, Cruaud, C, Theulot, B, Tallec, BL, et al.. FORK-seq: replication landscape of the Saccharomyces cerevisiae genome by nanopore sequencing. Genome Biol 2020;21:125. https://doi.org/10.1186/s13059-020-02013-3.Search in Google Scholar PubMed PubMed Central

68. Liu, Y, Wang, L, Xu, X, Yuan, Y, Zhang, B, Li, Z, et al.. The intra-S phase checkpoint directly regulates replication elongation to preserve the integrity of stalled replisomes. Proc Natl Acad Sci U S A 2021;118. https://doi.org/10.1073/pnas.2019183118.Search in Google Scholar PubMed PubMed Central

69. Wu, L, Liu, Y, Kong, D. Mechanism of chromosomal DNA replication initiation and replication fork stabilization in eukaryotes. Sci China Life Sci 2014;57:482–7. https://doi.org/10.1007/s11427-014-4631-4.Search in Google Scholar PubMed

70. Kara, N, Krueger, F, Rugg-Gunn, P, Houseley, J. Genome-wide analysis of DNA replication and DNA double-strand breaks using TrAEL-seq. PLoS Biol 2021;19:e3000886. https://doi.org/10.1371/journal.pbio.3000886.Search in Google Scholar PubMed PubMed Central

71. Matos-Rodrigues, G, van Wietmarschen, N, Wu, W, Tripathi, V, Koussa, NC, Pavani, R, et al.. S1-END-seq reveals DNA secondary structures in human cells. Mol Cell 2022;82:3538–52.e5. https://doi.org/10.1016/j.molcel.2022.08.007.Search in Google Scholar PubMed PubMed Central

72. Yu, C, Gan, H, Han, J, Zhou, ZX, Jia, S, Chabes, A, et al.. Strand-specific analysis shows protein binding at replication forks and PCNA unloading from lagging strands when forks stall. Mol Cell 2014;56:551–63. https://doi.org/10.1016/j.molcel.2014.11.007.Search in Google Scholar

73. Reverón-Gómez, N, González-Aguilera, C, Stewart-Morgan, KR, Petryk, N, Flury, V, Graziano, S, et al.. Accurate recycling of parental histones reproduces the histone modification landscape during DNA replication. Mol Cell 2018;72:239–49.e5. https://doi.org/10.1016/j.molcel.2018.08.010.Search in Google Scholar PubMed PubMed Central

74. Petryk, N, Reverón-Gómez, N, González-Aguilera, C, Dalby, M, Andersson, R, Groth, A. Genome-wide and sister chromatid-resolved profiling of protein occupancy in replicated chromatin with ChOR-seq and SCAR-seq. Nat Protoc 2021;16:4446–93. https://doi.org/10.1038/s41596-021-00585-3.Search in Google Scholar PubMed

75. Petryk, N, Dalby, M, Wenger, A, Stromme, CB, Strandsby, A, Andersson, R, et al.. MCM2 promotes symmetric inheritance of modified histones during DNA replication. Science 2018;361:1389–92. https://doi.org/10.1126/science.aau0294.Search in Google Scholar PubMed

76. Xu, C, Corces, VG. Nascent DNA methylome mapping reveals inheritance of hemimethylation at CTCF/cohesin sites. Science 2018;359:1166–70. https://doi.org/10.1126/science.aan5480.Search in Google Scholar PubMed PubMed Central

77. Xu, C, Corces, VG. Resolution of the DNA methylation state of single CpG dyads using in silico strand annealing and WGBS data. Nat Protoc 2019;14:202–16. https://doi.org/10.1038/s41596-018-0090-x.Search in Google Scholar PubMed PubMed Central

78. Ming, X, Zhang, Z, Zou, Z, Lv, C, Dong, Q, He, Q, et al.. Kinetics and mechanisms of mitotic inheritance of DNA methylation and their roles in aging-associated methylome deterioration. Cell Res 2020;30:980–96. https://doi.org/10.1038/s41422-020-0359-9.Search in Google Scholar PubMed PubMed Central

79. Ming, X, Zhu, B, Zhang, Z. Simultaneously measuring the methylation of parent and daughter strands of replicated DNA at the single-molecule level by Hammer-seq. Nat Protoc 2021;16:2131–57. https://doi.org/10.1038/s41596-020-00488-9.Search in Google Scholar PubMed

80. Wu, J, Liu, Y, Zhangding, Z, Liu, X, Ai, C, Gan, T, et al.. Cohesin maintains replication timing to suppress DNA damage on cancer genes. Nat Genet 2023;55:1347–58. https://doi.org/10.1038/s41588-023-01458-z.Search in Google Scholar PubMed

81. Emerson, DJ, Zhao, PA, Cook, AL, Barnett, RJ, Klein, KN, Saulebekova, D, et al.. Cohesin-mediated loop anchors confine the locations of human replication origins. Nature 2022;606:812–9. https://doi.org/10.1038/s41586-022-04803-0.Search in Google Scholar PubMed PubMed Central

82. Korsak, S, Banecki, KH, Buka, K, Agarwal, A, Borkowska, J, Chai, H, et al.. Chromatin as a coevolutionary graph: modeling the interplay of replication with chromatin dynamics. bioRxiv 2025. https://doi.org/2025.03.31.646315.10.1101/2025.03.31.646315Search in Google Scholar PubMed PubMed Central

83. Liu, Y, Zhangding, Z, Liu, X, Gan, T, Ai, C, Wu, J, et al.. Fork coupling directs DNA replication elongation and termination. Science 2024;383:1215–22. https://doi.org/10.1126/science.adj7606.Search in Google Scholar PubMed

84. Nurk, S, Koren, S, Rhie, A, Rautiainen, M, Bzikadze, AV, Mikheenko, A, et al.. The complete sequence of a human genome. Science 2022;376:44–53. https://doi.org/10.1126/science.abj6987.Search in Google Scholar PubMed PubMed Central

85. Massey, DJ, Koren, A. Telomere-to-telomere human DNA replication timing profiles. Sci Rep 2022;12:9560. https://doi.org/10.1038/s41598-022-13638-8.Search in Google Scholar PubMed PubMed Central

86. Rhie, A, Nurk, S, Cechova, M, Hoyt, SJ, Taylor, DJ, Altemose, N, et al.. The complete sequence of a human Y chromosome. Nature 2023;621:344–54. https://doi.org/10.1038/s41586-023-06457-y.Search in Google Scholar PubMed PubMed Central

87. Tubbs, A, Nussenzweig, A. Endogenous DNA damage as a source of genomic instability in cancer. Cell 2017;168:644–56. https://doi.org/10.1016/j.cell.2017.01.002.Search in Google Scholar PubMed PubMed Central

88. Rosendahl Huber, A, Van Hoeck, A, Van Boxtel, R. The mutagenic impact of environmental exposures in human cells and cancer: imprints through time. Front Genet 2021;12:760039. https://doi.org/10.3389/fgene.2021.760039.Search in Google Scholar PubMed PubMed Central

89. Zhang, Y, Zhang, X, Ba, Z, Liang, Z, Dring, EW, Hu, H, et al.. The fundamental role of chromatin loop extrusion in physiological V(D)J recombination. Nature 2019;573:600–4. https://doi.org/10.1038/s41586-019-1547-y.Search in Google Scholar PubMed PubMed Central

90. Zhang, X, Zhang, Y, Ba, Z, Kyritsis, N, Casellas, R, Alt, FW. Fundamental roles of chromatin loop extrusion in antibody class switching. Nature 2019;575:385–9. https://doi.org/10.1038/s41586-019-1723-0.Search in Google Scholar PubMed PubMed Central

91. Arter, M, Keeney, S. Divergence and conservation of the meiotic recombination machinery. Nat Rev Genet 2024;25:309–25. https://doi.org/10.1038/s41576-023-00669-8.Search in Google Scholar PubMed

92. Schwer, B, Wei, PC, Chang, AN, Kao, J, Du, Z, Meyers, RM, et al.. Transcription-associated processes cause DNA double-strand breaks and translocations in neural stem/progenitor cells. Proc Natl Acad Sci U S A 2016;113:2258–63. https://doi.org/10.1073/pnas.1525564113.Search in Google Scholar PubMed PubMed Central

93. Wang, G, Vasquez, KM. Dynamic alternative DNA structures in biology and disease. Nat Rev Genet 2023;24:211–34. https://doi.org/10.1038/s41576-022-00539-9.Search in Google Scholar PubMed PubMed Central

94. Scully, R, Panday, A, Elango, R, Willis, NA. DNA double-strand break repair-pathway choice in somatic mammalian cells. Nat Rev Mol Cell Biol 2019;20:698–714. https://doi.org/10.1038/s41580-019-0152-0.Search in Google Scholar PubMed PubMed Central

95. Iacovoni, JS, Caron, P, Lassadi, I, Nicolas, E, Massip, L, Trouche, D, et al.. High-resolution profiling of gammaH2AX around DNA double strand breaks in the mammalian genome. EMBO J 2010;29:1446–57. https://doi.org/10.1038/emboj.2010.38.Search in Google Scholar PubMed PubMed Central

96. Chiarle, R, Zhang, Y, Frock, RL, Lewis, SM, Molinie, B, Ho, YJ, et al.. Genome-wide translocation sequencing reveals mechanisms of chromosome breaks and rearrangements in B cells. Cell 2011;147:107–19. https://doi.org/10.1016/j.cell.2011.12.009.Search in Google Scholar

97. Pan, J, Sasaki, M, Kniewel, R, Murakami, H, Blitzblau, HG, Tischfield, SE, et al.. A hierarchical combination of factors shapes the genome-wide topography of yeast meiotic recombination initiation. Cell 2011;144:719–31. https://doi.org/10.1016/j.cell.2011.02.009.Search in Google Scholar PubMed PubMed Central

98. Crosetto, N, Mitra, A, Silva, MJ, Bienko, M, Dojer, N, Wang, Q, et al.. Nucleotide-resolution DNA double-strand break mapping by next-generation sequencing. Nat Methods 2013;10:361–5. https://doi.org/10.1038/nmeth.2408.Search in Google Scholar PubMed PubMed Central

99. Shastri, N, Tsai, YC, Hile, S, Jordan, D, Powell, B, Chen, J, et al.. Genome-wide identification of structure-forming repeats as principal sites of fork collapse upon ATR inhibition. Mol Cell 2018;72:222–38 e11. https://doi.org/10.1016/j.molcel.2018.08.047.Search in Google Scholar PubMed PubMed Central

100. Baranello, L, Kouzine, F, Wojtowicz, D, Cui, K, Przytycka, TM, Zhao, K, et al.. DNA break mapping reveals topoisomerase II activity genome-wide. Int J Mol Sci 2014;15:13111–22. https://doi.org/10.3390/ijms150713111.Search in Google Scholar PubMed PubMed Central

101. Gregoire, MC, Massonneau, J, Leduc, F, Arguin, M, Brazeau, MA, Boissonneault, G. Quantification and genome-wide mapping of DNA double-strand breaks. DNA Repair 2016;48:63–8. https://doi.org/10.1016/j.dnarep.2016.10.005.Search in Google Scholar PubMed

102. Xu, W, Liu, C, Zhang, Z, Sun, C, Li, Q, Li, K, et al.. DEtail-seq is an ultra-efficient and convenient method for meiotic DNA break profiling in multiple organisms. Sci China Life Sci 2023;66:1392–407. https://doi.org/10.1007/s11427-022-2277-y.Search in Google Scholar PubMed

103. Canela, A, Sridharan, S, Sciascia, N, Tubbs, A, Meltzer, P, Sleckman, BP, et al.. DNA breaks and end resection measured genome-wide by end sequencing. Mol Cell 2016;63:898–911. https://doi.org/10.1016/j.molcel.2016.06.034.Search in Google Scholar PubMed PubMed Central

104. Lensing, SV, Marsico, G, Hansel-Hertsch, R, Lam, EY, Tannahill, D, Balasubramanian, S. DSBCapture: in situ capture and sequencing of DNA breaks. Nat Methods 2016;13:855–7. https://doi.org/10.1038/nmeth.3960.Search in Google Scholar PubMed PubMed Central

105. Zhu, Y, Biernacka, A, Pardo, B, Dojer, N, Forey, R, Skrzypczak, M, et al.. qDSB-Seq is a general method for genome-wide quantification of DNA double-strand breaks using sequencing. Nat Commun 2019;10:2313. https://doi.org/10.1038/s41467-019-10332-8.Search in Google Scholar PubMed PubMed Central

106. Biernacka, A, Zhu, Y, Skrzypczak, M, Forey, R, Pardo, B, Grzelak, M, et al.. i-BLESS is an ultra-sensitive method for detection of DNA double-strand breaks. Commun Biol 2018;1:181. https://doi.org/10.1038/s42003-018-0165-9.Search in Google Scholar PubMed PubMed Central

107. Mimitou, EP, Keeney, S. S1-seq assay for mapping processed DNA ends. Methods Enzymol 2018;601:309–30. https://doi.org/10.1016/bs.mie.2017.11.031.Search in Google Scholar PubMed PubMed Central

108. Hoffman, EA, McCulley, A, Haarer, B, Arnak, R, Feng, W. Break-seq reveals hydroxyurea-induced chromosome fragility as a result of unscheduled conflict between DNA replication and transcription. Genome Res 2015;25:402–12. https://doi.org/10.1101/gr.180497.114.Search in Google Scholar PubMed PubMed Central

109. Yan, WX, Mirzazadeh, R, Garnerone, S, Scott, D, Schneider, MW, Kallas, T, et al.. BLISS is a versatile and quantitative method for genome-wide profiling of DNA double-strand breaks. Nat Commun 2017;8:15058. https://doi.org/10.1038/ncomms15058.Search in Google Scholar PubMed PubMed Central

110. Bouwman, BAM, Agostini, F, Garnerone, S, Petrosino, G, Gothe, HJ, Sayols, S, et al.. Genome-wide detection of DNA double-strand breaks by in-suspension BLISS. Nat Protoc 2020;15:3894–941. https://doi.org/10.1038/s41596-020-0397-2.Search in Google Scholar PubMed

111. Dobbs, FM, van Eijk, P, Fellows, MD, Loiacono, L, Nitsch, R, Reed, SH. Precision digital mapping of endogenous and induced genomic DNA breaks by INDUCE-seq. Nat Commun 2022;13:3989. https://doi.org/10.1038/s41467-022-31702-9.Search in Google Scholar PubMed PubMed Central

112. Cejka, P, Symington, LS. DNA end resection: mechanism and control. Annu Rev Genet 2021;55:285–307. https://doi.org/10.1146/annurev-genet-071719-020312.Search in Google Scholar PubMed

113. Baudat, F, Imai, Y, de Massy, B. Meiotic recombination in mammals: localization and regulation. Nat Rev Genet 2013;14:794–806. https://doi.org/10.1038/nrg3573.Search in Google Scholar PubMed

114. Khil, PP, Smagulova, F, Brick, KM, Camerini-Otero, RD, Petukhova, GV. Sensitive mapping of recombination hotspots using sequencing-based detection of ssDNA. Genome Res 2012;22:957–65. https://doi.org/10.1101/gr.130583.111.Search in Google Scholar PubMed PubMed Central

115. Wienert, B, Wyman, SK, Richardson, CD, Yeh, CD, Akcakaya, P, Porritt, MJ, et al.. Unbiased detection of CRISPR off-targets in vivo using DISCOVER-seq. Science 2019;364:286–9. https://doi.org/10.1126/science.aav9023.Search in Google Scholar PubMed PubMed Central

116. Zou, RS, Liu, Y, Gaido, OER, Konig, MF, Mog, BJ, Shen, LL, et al.. Improving the sensitivity of in vivo CRISPR off-target detection with DISCOVER-seq. Nat Methods 2023;20:706–13. https://doi.org/10.1038/s41592-023-01840-z.Search in Google Scholar PubMed PubMed Central

117. Claeys Bouuaert, C, Tischfield, SE, Pu, S, Mimitou, EP, Arias-Palomo, E, Berger, JM, et al.. Structural and functional characterization of the Spo11 core complex. Nat Struct Mol Biol 2021;28:92–102. https://doi.org/10.1038/s41594-020-00534-w.Search in Google Scholar PubMed PubMed Central

118. Lam, I, Keeney, S. Mechanism and regulation of meiotic recombination initiation. Cold Spring Harbor Perspect Biol 2014;7:a016634. https://doi.org/10.1101/cshperspect.a016634.Search in Google Scholar PubMed PubMed Central

119. Mu, X, Murakami, H, Mohibullah, N, Keeney, S. Chromosome-autonomous feedback down-regulates meiotic DNA break competence upon synaptonemal complex formation. Genes Dev 2020;34:1605–18. https://doi.org/10.1101/gad.342873.120.Search in Google Scholar PubMed PubMed Central

120. Murakami, H, Lam, I, Huang, PC, Song, J, van Overbeek, M, Keeney, S. Multilayered mechanisms ensure that short chromosomes recombine in meiosis. Nature 2020;582:124–8. https://doi.org/10.1038/s41586-020-2248-2.Search in Google Scholar PubMed PubMed Central

121. Mimitou, EP, Yamada, S, Keeney, S. A global view of meiotic double-strand break end resection. Science 2017;355:40–5. https://doi.org/10.1126/science.aak9704.Search in Google Scholar PubMed PubMed Central

122. Gittens, WH, Johnson, DJ, Allison, RM, Cooper, TJ, Thomas, H, Neale, MJ. A nucleotide resolution map of Top2-linked DNA breaks in the yeast and human genome. Nat Commun 2019;10:4846. https://doi.org/10.1038/s41467-019-12802-5.Search in Google Scholar PubMed PubMed Central

123. Brown, GGB, Gittens, WH, Allison, RM, Oliver, AW, Neale, MJ. CC-seq: nucleotide-resolution mapping of Spo11 DNA double-strand breaks in S. cerevisiae cells. Methods Mol Biol 2024;2818:3–22.10.1007/978-1-0716-3906-1_1Search in Google Scholar PubMed

124. Alt, FW, Zhang, Y, Meng, FL, Guo, C, Schwer, B. Mechanisms of programmed DNA lesions and genomic instability in the immune system. Cell 2013;152:417–29. https://doi.org/10.1016/j.cell.2013.01.007.Search in Google Scholar PubMed PubMed Central

125. Oliveira, TY, Resch, W, Jankovic, M, Casellas, R, Nussenzweig, MC, Klein, IA. Translocation capture sequencing: a method for high throughput mapping of chromosomal rearrangements. J Immunol Methods 2012;375:176–81. https://doi.org/10.1016/j.jim.2011.10.007.Search in Google Scholar PubMed PubMed Central

126. Delaney, JR, La Spada, AR. Protocol for mapping double-stranded DNA break sites across the genome with translocation capture sequencing. STAR Protoc 2023;4:102205. https://doi.org/10.1016/j.xpro.2023.102205.Search in Google Scholar PubMed PubMed Central

127. Gabriel, R, Lombardo, A, Arens, A, Miller, JC, Genovese, P, Kaeppel, C, et al.. An unbiased genome-wide analysis of zinc-finger nuclease specificity. Nat Biotechnol 2011;29:816–23. https://doi.org/10.1038/nbt.1948.Search in Google Scholar PubMed

128. Liu, Y, Yin, J, Gan, T, Liu, M, Xin, C, Zhang, W, et al.. PEM-seq comprehensively quantifies DNA repair outcomes during gene-editing and DSB repair. STAR Protoc 2022;3:101088. https://doi.org/10.1016/j.xpro.2021.101088.Search in Google Scholar PubMed PubMed Central

129. Yin, J, Liu, M, Liu, Y, Wu, J, Gan, T, Zhang, W, et al.. Optimizing genome editing strategy by primer-extension-mediated sequencing. Cell Discov 2019;5:18. https://doi.org/10.1038/s41421-019-0088-8.Search in Google Scholar PubMed PubMed Central

130. Tsai, SQ, Zheng, Z, Nguyen, NT, Liebers, M, Topkar, VV, Thapar, V, et al.. GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-cas nucleases. Nat Biotechnol 2015;33:187–97. https://doi.org/10.1038/nbt.3117.Search in Google Scholar PubMed PubMed Central

131. Wu, W, Kargbo-Hill, SE, Nathan, WJ, Paiano, J, Callen, E, Wang, D, et al.. Neuronal enhancers are hotspots for DNA single-strand break repair. Nature 2021;593:440–4. https://doi.org/10.1038/s41586-021-03468-5.Search in Google Scholar PubMed PubMed Central

132. Macheret, M, Bhowmick, R, Sobkowiak, K, Padayachy, L, Mailler, J, Hickson, ID, et al.. High-resolution mapping of mitotic DNA synthesis regions and common fragile sites in the human genome through direct sequencing. Cell Res 2020;30:997–1008. https://doi.org/10.1038/s41422-020-0358-x.Search in Google Scholar PubMed PubMed Central

133. Ji, F, Liao, H, Pan, S, Ouyang, L, Jia, F, Fu, Z, et al.. Genome-wide high-resolution mapping of mitotic DNA synthesis sites and common fragile sites by direct sequencing. Cell Res 2020;30:1009–23. https://doi.org/10.1038/s41422-020-0357-y.Search in Google Scholar PubMed PubMed Central

134. Zhu, Y, Tan, Y, Li, L, Xiang, Y, Huang, Y, Zhang, X, et al.. Genome-wide mapping of protein-DNA damage interaction by PADD-seq. Nucleic Acids Res 2023;51:e32. https://doi.org/10.1093/nar/gkad008.Search in Google Scholar PubMed PubMed Central

135. Reid, DA, Reed, PJ, Schlachetzki, JCM, Nitulescu, II, Chou, G, Tsui, EC, et al.. Incorporation of a nucleoside analog maps genome repair sites in postmitotic human neurons. Science 2021;372:91–4. https://doi.org/10.1126/science.abb9032.Search in Google Scholar PubMed PubMed Central

136. Zhai, J, Xiao, Z, Wang, Y, Wang, H. Human embryonic development: from peri-implantation to gastrulation. Trends Cell Biol 2022;32:18–29. https://doi.org/10.1016/j.tcb.2021.07.008.Search in Google Scholar PubMed

137. Taubenschmid-Stowers, J, Rostovskaya, M, Santos, F, Ljung, S, Argelaguet, R, Krueger, F, et al.. 8C-like cells capture the human zygotic genome activation program in vitro. Cell Stem Cell 2022;29:449–59 e6. https://doi.org/10.1016/j.stem.2022.01.014.Search in Google Scholar PubMed PubMed Central

138. Schultz, RM. Regulation of zygotic gene activation in the mouse. Bioessays 1993;15:531–8. https://doi.org/10.1002/bies.950150806.Search in Google Scholar PubMed

139. Wang, N, Xu, S, Egli, D. Replication stress in mammalian embryo development, differentiation, and reprogramming. Trends Cell Biol 2023;33:872–86. https://doi.org/10.1016/j.tcb.2023.03.015.Search in Google Scholar PubMed PubMed Central

140. Ahuja, AK, Jodkowska, K, Teloni, F, Bizard, AH, Zellweger, R, Herrador, R, et al.. A short G1 phase imposes constitutive replication stress and fork remodelling in mouse embryonic stem cells. Nat Commun 2016;7:10660. https://doi.org/10.1038/ncomms10660.Search in Google Scholar PubMed PubMed Central

141. Kermi, C, Aze, A, Maiorano, D. Preserving genome integrity during the early embryonic DNA replication cycles. Genes 2019;10:398. https://doi.org/10.3390/genes10050398.Search in Google Scholar PubMed PubMed Central

142. Palmerola, KL, Amrane, S, De Los Angeles, A, Xu, S, Wang, N, de Pinho, J, et al.. Replication stress impairs chromosome segregation and preimplantation development in human embryos. Cell 2022;185:2988–3007.e20. https://doi.org/10.1016/j.cell.2022.06.028.Search in Google Scholar PubMed

143. Xu, S, Wang, N, Zuccaro, MV, Gerhardt, J, Iyyappan, R, Scatolin, GN, et al.. DNA replication in early mammalian embryos is patterned, predisposing lamina-associated regions to fragility. Nat Commun 2024;15:5247. https://doi.org/10.1038/s41467-024-49565-7.Search in Google Scholar PubMed PubMed Central

144. Halliwell, JA, Martin-Gonzalez, J, Hashim, A, Dahl, JA, Hoffmann, ER, Lerdrup, M. Sex-specific DNA-replication in the early mammalian embryo. Nat Commun 2024;15:6323. https://doi.org/10.1038/s41467-024-50727-w.Search in Google Scholar PubMed PubMed Central

145. Miura, H, Takahashi, S, Poonperm, R, Tanigawa, A, Takebayashi, SI, Hiratani, I. Single-cell DNA replication profiling identifies spatiotemporal developmental dynamics of chromosome organization. Nat Genet 2019;51:1356–68. https://doi.org/10.1038/s41588-019-0474-z.Search in Google Scholar PubMed

146. Khokhlova, EV, Fesenko, ZS, Sopova, JV, Leonova, EI. Features of DNA repair in the early stages of Mammalian embryonic development. Genes 2020;11:1138. https://doi.org/10.3390/genes11101138.Search in Google Scholar PubMed PubMed Central

147. Ye, Y, Xie, W, Wang, X, Tan, S, Yang, L, Ma, Z, et al.. DNA-Damage orchestrates self-renewal and differentiation via reciprocal p53 family and Hippo/Wnt/TGF-β pathway activation in embryonic stem cells. Cell Mol Life Sci 2025;82:38. https://doi.org/10.1007/s00018-024-05561-0.Search in Google Scholar PubMed PubMed Central

148. McCoy, RC, Summers, MC, McCollin, A, Ottolini, CS, Ahuja, K, Handyside, AH. Meiotic and mitotic aneuploidies drive arrest of in vitro fertilized human preimplantation embryos. Genome Med 2023;15:77. https://doi.org/10.1186/s13073-023-01231-1.Search in Google Scholar PubMed PubMed Central

149. Sahoo, T, Dzidic, N, Strecker, MN, Commander, S, Travis, MK, Doherty, C, et al.. Comprehensive genetic analysis of pregnancy loss by chromosomal microarrays: outcomes, benefits, and challenges. Genet Med 2017;19:83–9. https://doi.org/10.1038/gim.2016.69.Search in Google Scholar PubMed

150. Arnadottir, GA, Jonsson, H, Hartwig, TS, Gruhn, JR, Møller, PL, Gylfason, A, et al.. Sequence diversity lost in early pregnancy. Nature 2025;642:672–81. https://doi.org/10.1038/s41586-025-09031-w.Search in Google Scholar PubMed PubMed Central

151. Bicknell, LS, Bongers, EM, Leitch, A, Brown, S, Schoots, J, Harley, ME, et al.. Mutations in the pre-replication complex cause Meier-Gorlin syndrome. Nat Genet 2011;43:356–9. https://doi.org/10.1038/ng.775.Search in Google Scholar PubMed PubMed Central

152. Garcia-Higuera, I, Taniguchi, T, Ganesan, S, Meyn, MS, Timmers, C, Hejna, J, et al.. Interaction of the Fanconi anemia proteins and BRCA1 in a common pathway. Mol Cell 2001;7:249–62. https://doi.org/10.1016/s1097-2765(01)00173-3.Search in Google Scholar PubMed

153. Nakatani, T, Schauer, T, Pal, M, Ettinger, A, Altamirano-Pacheco, L, Zorn, J, et al.. RIF1 controls replication timing in early mouse embryos independently of lamina-associated nuclear organization. Dev Cell 2025;60:2149–62.e7. https://doi.org/10.1016/j.devcel.2025.03.016.Search in Google Scholar PubMed PubMed Central

154. Yan, L, Guo, H, Hu, B, Li, R, Yong, J, Zhao, Y, et al.. Epigenomic landscape of human fetal brain, heart, and liver. J Biol Chem 2016;291:4386–98. https://doi.org/10.1074/jbc.m115.672931.Search in Google Scholar

155. Alberry, MS, Aziz, E, Ahmed, SR, Abdel-Fattah, S. Non invasive prenatal testing (NIPT) for common aneuploidies and beyond. Eur J Obstet Gynecol Reprod Biol 2021;258:424–9. https://doi.org/10.1016/j.ejogrb.2021.01.008.Search in Google Scholar PubMed

156. Findley, TO, Parchem, JG, Ramdaney, A, Morton, SU. Challenges in the clinical understanding of genetic testing in birth defects and pediatric diseases. Transl Pediatr 2023;12:1028–40. https://doi.org/10.21037/tp-23-54.Search in Google Scholar PubMed PubMed Central

157. Wang, Y, Li, Y, Zhu, X, Yang, M, Liu, Y, Wang, N, et al.. Concurrent preimplantation genetic testing and competence assessment of human embryos by transcriptome sequencing. Adv Sci (Weinh) 2024;11:e2309817. https://doi.org/10.1002/advs.202309817.Search in Google Scholar PubMed PubMed Central

158. Gleicher, N, Patrizio, P, Brivanlou, A. Preimplantation genetic testing for aneuploidy - a castle built on sand. Trends Mol Med 2021;27:731–42. https://doi.org/10.1016/j.molmed.2020.11.009.Search in Google Scholar PubMed

159. Lu, S, Zong, C, Fan, W, Yang, M, Li, J, Chapman, AR, et al.. Probing meiotic recombination and aneuploidy of single sperm cells by whole-genome sequencing. Science 2012;338:1627–30. https://doi.org/10.1126/science.1229112.Search in Google Scholar PubMed PubMed Central

160. Huang, J, Yan, L, Fan, W, Zhao, N, Zhang, Y, Tang, F, et al.. Validation of multiple annealing and looping-based amplification cycle sequencing for 24-chromosome aneuploidy screening of cleavage-stage embryos. Fertil Steril 2014;102:1685–91. https://doi.org/10.1016/j.fertnstert.2014.08.015.Search in Google Scholar PubMed

161. Yan, L, Huang, L, Xu, L, Huang, J, Ma, F, Zhu, X, et al.. Live births after simultaneous avoidance of monogenic diseases and chromosome abnormality by next-generation sequencing with linkage analyses. Proc Natl Acad Sci U S A 2015;112:15964–9. https://doi.org/10.1073/pnas.1523297113.Search in Google Scholar PubMed PubMed Central

162. Bhalla, S, Passarelli, R, Biswas, A, De, S, Ghodoussipour, S. Plasma-derived cell-free DNA as a biomarker for early detection, prognostication, and personalized treatment of urothelial carcinoma. J Clin Med 2024;13:2057. https://doi.org/10.3390/jcm13072057.Search in Google Scholar PubMed PubMed Central

163. Huang, L, Bogale, B, Tang, Y, Lu, S, Xie, XS, Racowsky, C. Noninvasive preimplantation genetic testing for aneuploidy in spent medium may be more reliable than trophectoderm biopsy. Proc Natl Acad Sci U S A 2019;116:14105–12. https://doi.org/10.1073/pnas.1907472116.Search in Google Scholar PubMed PubMed Central

164. Chiu, RW, Akolekar, R, Zheng, YW, Leung, TY, Sun, H, Chan, KC, et al.. Non-invasive prenatal assessment of trisomy 21 by multiplexed maternal plasma DNA sequencing: large scale validity study. Bmj 2011;342:c7401. https://doi.org/10.1136/bmj.c7401.Search in Google Scholar PubMed PubMed Central

165. Hanahan, D. Hallmarks of cancer: new dimensions. Cancer Discov 2022;12:31–46. https://doi.org/10.1158/2159-8290.cd-21-1059.Search in Google Scholar

166. Negrini, S, Gorgoulis, VG, Halazonetis, TD. Genomic instability-an evolving hallmark of cancer. Nat Rev Mol Cell Biol 2010;11:220–8. https://doi.org/10.1038/nrm2858.Search in Google Scholar PubMed

167. Dietzen, M, Zhai, H, Lucas, O, Pich, O, Barrington, C, Lu, WT, et al.. Replication timing alterations are associated with mutation acquisition during breast and lung cancer evolution. Nat Commun 2024;15:6039. https://doi.org/10.1038/s41467-024-50107-4.Search in Google Scholar PubMed PubMed Central

168. Tomasetti, C, Li, L, Vogelstein, B. Stem cell divisions, somatic mutations, cancer etiology, and cancer prevention. Science 2017;355:1330–4. https://doi.org/10.1126/science.aaf9011.Search in Google Scholar PubMed PubMed Central

169. Kotsantis, P, Petermann, E, Boulton, SJ. Mechanisms of oncogene-induced replication stress: jigsaw falling into place. Cancer Discov 2018;8:537–55. https://doi.org/10.1158/2159-8290.cd-17-1461.Search in Google Scholar

170. Dellino, GI, Cittaro, D, Piccioni, R, Luzi, L, Banfi, S, Segalla, S, et al.. Genome-wide mapping of human DNA-replication origins: levels of transcription at ORC1 sites regulate origin selection and replication timing. Genome Res 2013;23:1–11. https://doi.org/10.1101/gr.142331.112.Search in Google Scholar PubMed PubMed Central

171. Wu, X, Kabalane, H, Kahli, M, Petryk, N, Laperrousaz, B, Jaszczyszyn, Y, et al.. Developmental and cancer-associated plasticity of DNA replication preferentially targets GC-poor, lowly expressed and late-replicating regions. Nucleic Acids Res 2018;46:10157–72. https://doi.org/10.1093/nar/gky797.Search in Google Scholar PubMed PubMed Central

172. Saxena, S, Zou, L. Hallmarks of DNA replication stress. Mol Cell 2022;82:2298–314. https://doi.org/10.1016/j.molcel.2022.05.004.Search in Google Scholar PubMed PubMed Central

173. Li, Y, Zhang, Y, Shah, SB, Chang, CY, Wang, H, Wu, X. MutSbeta protects common fragile sites by facilitating homology-directed repair at DNA double-strand breaks with secondary structures. Nucleic Acids Res 2024;52:1120–35. https://doi.org/10.1093/nar/gkad1112.Search in Google Scholar PubMed PubMed Central

174. Setton, J, Hadi, K, Choo, ZN, Kuchin, KS, Tian, H, Da Cruz Paula, A, et al.. Long-molecule scars of backup DNA repair in BRCA1- and BRCA2-deficient cancers. Nature 2023;621:129–37. https://doi.org/10.1038/s41586-023-06461-2.Search in Google Scholar PubMed PubMed Central

175. Rehman, SK, Haynes, J, Collignon, E, Brown, KR, Wang, Y, Nixon, AML, et al.. Colorectal cancer cells enter a diapause-like DTP state to survive chemotherapy. Cell 2021;184:226–42 e21. https://doi.org/10.1016/j.cell.2020.11.018.Search in Google Scholar PubMed PubMed Central

176. Liu, Y, Wu, X, d’Aubenton-Carafa, Y, Thermes, C, Chen, CL. OKseqHMM: a genome-wide replication fork directionality analysis toolkit. Nucleic Acids Res 2023;51:e22. https://doi.org/10.1093/nar/gkac1239.Search in Google Scholar PubMed PubMed Central

177. Guo, Y, Wang, J, Benedict, B, Yang, C, van Gemert, F, Ma, X, et al.. Targeting CDC7 potentiates ATR-CHK1 signaling inhibition through induction of DNA replication stress in liver cancer. Genome Med 2021;13:166. https://doi.org/10.1186/s13073-021-00981-0.Search in Google Scholar PubMed PubMed Central

178. Yano, K, Kato, M, Endo, S, Igarashi, T, Wada, R, Kohno, T, et al.. PARP inhibition-associated heterochromatin confers increased DNA replication stress and vulnerability to ATR inhibition in SMARCA4-deficient cells. Cell Death Discov 2025;11:31. https://doi.org/10.1038/s41420-025-02306-1.Search in Google Scholar PubMed PubMed Central

179. Skaletsky, H, Kuroda-Kawaguchi, T, Minx, PJ, Cordum, HS, Hillier, L, Brown, LG, et al.. The male-specific region of the human Y chromosome is a mosaic of discrete sequence classes. Nature 2003;423:825–37. https://doi.org/10.1038/nature01722.Search in Google Scholar PubMed

180. Colaco, S, Modi, D. Genetics of the human Y chromosome and its association with male infertility. Reprod Biol Endocrinol 2018;16:14. https://doi.org/10.1186/s12958-018-0330-5.Search in Google Scholar PubMed PubMed Central

181. Black, EM, Giunta, S. Repetitive fragile sites: centromere satellite DNA as a source of genome instability in human diseases. Genes 2018;9:615. https://doi.org/10.3390/genes9120615.Search in Google Scholar PubMed PubMed Central

182. Ly, P, Teitz, LS, Kim, DH, Shoshani, O, Skaletsky, H, Fachinetti, D, et al.. Selective Y centromere inactivation triggers chromosome shattering in micronuclei and repair by non-homologous end joining. Nat Cell Biol 2017;19:68–75. https://doi.org/10.1038/ncb3450.Search in Google Scholar PubMed PubMed Central

183. Hu, Q, Espejo Valle-Inclán, J, Dahiya, R, Guyer, A, Mazzagatti, A, Maurais, EG, et al.. Non-homologous end joining shapes the genomic rearrangement landscape of chromothripsis from mitotic errors. Nat Commun 2024;15:5611. https://doi.org/10.1038/s41467-024-49985-5.Search in Google Scholar PubMed PubMed Central

184. Dobrovolna, M, Bowater, RP, Pecinka, P, Brazda, V, Bartas, M. High-resolution assembly of the human Y chromosome identifies a vast landscape of inverted repeats associated with structural and functional genomic features. Int J Mol Sci 2025;26:10180. https://doi.org/10.3390/ijms262010180.Search in Google Scholar PubMed PubMed Central

185. Vijayakumar, S, Hall, DC, Reveles, XT, Troyer, DA, Thompson, IM, Garcia, D, et al.. Detection of recurrent copy number loss at Yp11.2 involving TSPY gene cluster in prostate cancer using array-based comparative genomic hybridization. Cancer Res 2006;66:4055–64. https://doi.org/10.1158/0008-5472.can-05-3822.Search in Google Scholar PubMed

186. Dasari, VK, Deng, D, Perinchery, G, Yeh, CC, Dahiya, R. DNA methylation regulates the expression of Y chromosome specific genes in prostate cancer. J Urol 2002;167:335–8. https://doi.org/10.1016/s0022-5347(05)65464-x.Search in Google Scholar

187. Casas-Delucchi, CS, Daza-Martin, M, Williams, SL, Coster, G. The mechanism of replication stalling and recovery within repetitive DNA. Nat Commun 2022;13:3953. https://doi.org/10.1038/s41467-022-31657-x.Search in Google Scholar PubMed PubMed Central

188. Gadgil, R, Barthelemy, J, Lewis, T, Leffak, M. Replication stalling and DNA microsatellite instability. Biophys Chem 2017;225:38–48. https://doi.org/10.1016/j.bpc.2016.11.007.Search in Google Scholar PubMed PubMed Central

189. Ait, SA, Costa, AB, Sheng, Z, Guo, W, Haber, JE, Lobachev, KS. Structural parameters of palindromic repeats determine the specificity of nuclease attack of secondary structures. Nucleic Acids Res 2021;49:3932–47. https://doi.org/10.1093/nar/gkab168.Search in Google Scholar PubMed PubMed Central

190. Rogers, MJ. Y chromosome copy number variation and its effects on fertility and other health factors: a review. Transl Androl Urol 2021;10:1373–82. https://doi.org/10.21037/tau.2020.04.06.Search in Google Scholar PubMed PubMed Central

191. Forsberg, LA. Loss of chromosome Y (LOY) in blood cells is associated with increased risk for disease and mortality in aging men. Hum Genet 2017;136:657–63. https://doi.org/10.1007/s00439-017-1799-2.Search in Google Scholar PubMed PubMed Central

192. Qi, M, Pang, J, Mitsiades, I, Lane, AA, Rheinbay, E. Loss of chromosome Y in primary tumors. Cell 2023;186:3125–36.e11. https://doi.org/10.1016/j.cell.2023.06.006.Search in Google Scholar PubMed

193. Chen, X, Shen, Y, Choi, S, Abdel-Hafiz, HA, Basu, M, Hoelzen, L, et al.. Concurrent loss of the Y chromosome in cancer and T cells impacts outcome. Nature 2025;642:1041–50. https://doi.org/10.1038/s41586-025-09071-2.Search in Google Scholar PubMed PubMed Central

194. Wang, R, Yang, Y, Lu, T, Cui, Y, Li, B, Liu, X. Circulating cell-free DNA-Based methylation pattern in plasma for early diagnosis of esophagus cancer. PeerJ 2024;12:e16802. https://doi.org/10.7717/peerj.16802.Search in Google Scholar PubMed PubMed Central

195. Danovi, S. Y chromosome loss in cancer. Nat Genet 2025;57:1565. https://doi.org/10.1038/s41588-025-02276-1.Search in Google Scholar PubMed

196. Sabouny, R, Shutt, TE. The role of mitochondrial dynamics in mtDNA maintenance. J Cell Sci 2021;134. https://doi.org/10.1242/jcs.258944.Search in Google Scholar PubMed

197. Magnusson, J, Orth, M, Lestienne, P, Taanman, JW. Replication of mitochondrial DNA occurs throughout the mitochondria of cultured human cells. Exp Cell Res 2003;289:133–42. https://doi.org/10.1016/s0014-4827(03)00249-0.Search in Google Scholar PubMed

198. Korhonen, JA, Pham, XH, Pellegrini, M, Falkenberg, M. Reconstitution of a minimal mtDNA replisome in vitro. EMBO J 2004;23:2423–9. https://doi.org/10.1038/sj.emboj.7600257.Search in Google Scholar PubMed PubMed Central

199. Peter, B, Falkenberg, M. TWINKLE and other human mitochondrial DNA helicases: structure, function and disease. Genes 2020;11:408. https://doi.org/10.3390/genes11040408.Search in Google Scholar PubMed PubMed Central

200. Gammage, PA, Frezza, C. Mitochondrial DNA: the overlooked oncogenome? BMC Biol 2019;17:53. https://doi.org/10.1186/s12915-019-0668-y.Search in Google Scholar PubMed PubMed Central

201. Gustafson, MA, Sullivan, ED, Copeland, WC. Consequences of compromised mitochondrial genome integrity. DNA Repair 2020;93:102916. https://doi.org/10.1016/j.dnarep.2020.102916.Search in Google Scholar PubMed PubMed Central

202. Li, M, Schonberg, A, Schaefer, M, Schroeder, R, Nasidze, I, Stoneking, M. Detecting heteroplasmy from high-throughput sequencing of complete human mitochondrial DNA genomes. Am J Hum Genet 2010;87:237–49. https://doi.org/10.1016/j.ajhg.2010.07.014.Search in Google Scholar PubMed PubMed Central

203. Gupta, R, Kanai, M, Durham, TJ, Tsuo, K, McCoy, JG, Kotrys, AV, et al.. Nuclear genetic control of mtDNA copy number and heteroplasmy in humans. Nature 2023;620:839–48. https://doi.org/10.1038/s41586-023-06426-5.Search in Google Scholar PubMed PubMed Central

204. Lentz, SI, Edwards, JL, Backus, C, McLean, LL, Haines, KM, Feldman, EL. Mitochondrial DNA (mtDNA) biogenesis: visualization and duel incorporation of BrdU and EdU into newly synthesized mtDNA in vitro. J Histochem Cytochem 2010;58:207–18. https://doi.org/10.1369/jhc.2009.954701.Search in Google Scholar PubMed PubMed Central

205. Lozen, M, Chen, Y, Boisvert, RA, Schlacher, K. Mitochondrial replication assay (MIRA) for efficient in situ quantification of nascent mtDNA and protein interactions with nascent mtDNA (mitoSIRF). Bio Protoc 2023;13:e4680. https://doi.org/10.21769/bioprotoc.4680.Search in Google Scholar PubMed PubMed Central

206. Shokolenko, I, Alexeyev, M. Mitochondrial DNA: consensuses and controversies. DNA (Basel) 2022;2:131–48. https://doi.org/10.3390/dna2020010.Search in Google Scholar PubMed PubMed Central

207. Marom, S, Blumberg, A, Kundaje, A, Mishmar, D. mtDNA chromatin-like organization is gradually established during mammalian embryogenesis. iScience 2019;12:141–51. https://doi.org/10.1016/j.isci.2018.12.032.Search in Google Scholar PubMed PubMed Central

208. Matsuda, S, Nakayama, M, Do, Y, Ishiuchi, T, Yagi, M, Wanrooij, S, et al.. TEFM facilitates transition from RNA synthesis to DNA synthesis at H-strand replication origin of mtDNA. Commun Biol 2025;8:202. https://doi.org/10.1038/s42003-025-07645-4.Search in Google Scholar PubMed PubMed Central

209. Agaronyan, K, Morozov, YI, Anikin, M, Temiakov, D. Mitochondrial biology. Replication-transcription switch in human mitochondria. Science 2015;347:548–51. https://doi.org/10.1126/science.aaa0986.Search in Google Scholar PubMed PubMed Central

210. Vodicka, P, Vodenkova, S, Danesova, N, Vodickova, L, Zobalova, R, Tomasova, K, et al.. Mitochondrial DNA damage, repair, and replacement in cancer. Trends Cancer 2025;11:62–73. https://doi.org/10.1016/j.trecan.2024.09.010.Search in Google Scholar PubMed

211. Yin, PH, Lee, HC, Chau, GY, Wu, YT, Li, SH, Lui, WY, et al.. Alteration of the copy number and deletion of mitochondrial DNA in human hepatocellular carcinoma. Br J Cancer 2004;90:2390–6. https://doi.org/10.1038/sj.bjc.6601838.Search in Google Scholar PubMed PubMed Central

212. Reznik, E, Miller, ML, Senbabaoglu, Y, Riaz, N, Sarungbam, J, Tickoo, SK, et al.. Mitochondrial DNA copy number variation across human cancers. eLife 2016;5. https://doi.org/10.7554/elife.10769.Search in Google Scholar PubMed PubMed Central

213. Ricchetti, M. Replication stress in mitochondria. Mutat Res 2018;808:93–102. https://doi.org/10.1016/j.mrfmmm.2018.01.005.Search in Google Scholar PubMed

214. Singh, KK, Ayyasamy, V, Owens, KM, Koul, MS, Vujcic, M. Mutations in mitochondrial DNA polymerase-gamma promote breast tumorigenesis. J Hum Genet 2009;54:516–24. https://doi.org/10.1038/jhg.2009.71.Search in Google Scholar PubMed PubMed Central

215. Inigo, JR, Chandra, D. The mitochondrial unfolded protein response (UPR(mt)): shielding against toxicity to mitochondria in cancer. J Hematol Oncol 2022;15:98. https://doi.org/10.1186/s13045-022-01317-0.Search in Google Scholar PubMed PubMed Central

216. Kuo, CL, Ponneri Babuharisankar, A, Lin, YC, Lien, HW, Lo, YK, Chou, HY, et al.. Mitochondrial oxidative stress in the tumor microenvironment and cancer immunoescape: foe or friend? J Biomed Sci 2022;29:74. https://doi.org/10.1186/s12929-022-00859-2.Search in Google Scholar PubMed PubMed Central

217. Frascarelli, C, Zanetti, N, Nasca, A, Izzo, R, Lamperti, C, Lamantea, E, et al.. Nanopore long-read next-generation sequencing for detection of mitochondrial DNA large-scale deletions. Front Genet 2023;14:1089956. https://doi.org/10.3389/fgene.2023.1089956.Search in Google Scholar PubMed PubMed Central

218. Peng, F, Wang, S, Feng, Z, Zhou, K, Zhang, H, Guo, X, et al.. Circulating cell-free mtDNA as a new biomarker for cancer detection and management. Cancer Biol Med 2023;21:105–10. https://doi.org/10.20892/j.issn.2095-3941.2023.0280.Search in Google Scholar PubMed PubMed Central

219. van der Pol, Y, Moldovan, N, Ramaker, J, Bootsma, S, Lenos, KJ, Vermeulen, L, et al.. The landscape of cell-free mitochondrial DNA in liquid biopsy for cancer detection. Genome Biol 2023;24:229. https://doi.org/10.1186/s13059-023-03074-w.Search in Google Scholar PubMed PubMed Central

220. Arbini, AA, Guerra, F, Greco, M, Marra, E, Gandee, L, Xiao, G, et al.. Mitochondrial DNA depletion sensitizes cancer cells to PARP inhibitors by translational and post-translational repression of BRCA2. Oncogenesis 2013;2:e82. https://doi.org/10.1038/oncsis.2013.45.Search in Google Scholar PubMed PubMed Central

221. Dombi, E, Marinaki, T, Spingardi, P, Millar, V, Hadjichristou, N, Carver, J, et al.. Nucleoside supplements as treatments for mitochondrial DNA depletion syndrome. Front Cell Dev Biol 2024;12:1260496. https://doi.org/10.3389/fcell.2024.1260496.Search in Google Scholar PubMed PubMed Central

222. Tanaka, K, Masuike, Y, Kubo, Y, Harino, T, Kurokawa, Y, Eguchi, H, et al.. The role of mitochondrial DNA in modulating chemoresistance in esophageal cancer: mechanistic insights and therapeutic potential. Biomolecules 2025;15:1128. https://doi.org/10.3390/biom15081128.Search in Google Scholar PubMed PubMed Central

223. Kim, H, Nguyen, NP, Turner, K, Wu, S, Gujar, AD, Luebeck, J, et al.. Extrachromosomal DNA is associated with oncogene amplification and poor outcome across multiple cancers. Nat Genet 2020;52:891–7. https://doi.org/10.1038/s41588-020-0678-2.Search in Google Scholar PubMed PubMed Central

224. Turner, KM, Deshpande, V, Beyter, D, Koga, T, Rusert, J, Lee, C, et al.. Extrachromosomal oncogene amplification drives tumour evolution and genetic heterogeneity. Nature 2017;543:122–5. https://doi.org/10.1038/nature21356.Search in Google Scholar PubMed PubMed Central

225. Pradella, D, Zhang, M, Gao, R, Yao, MA, Gluchowska, KM, Cendon-Florez, Y, et al.. Engineered extrachromosomal oncogene amplifications promote tumorigenesis. Nature 2025;637:955–64. https://doi.org/10.1038/s41586-024-08318-8.Search in Google Scholar PubMed PubMed Central

226. Wu, S, Turner, KM, Nguyen, N, Raviram, R, Erb, M, Santini, J, et al.. Circular ecDNA promotes accessible chromatin and high oncogene expression. Nature 2019;575:699–703. https://doi.org/10.1038/s41586-019-1763-5.Search in Google Scholar PubMed PubMed Central

227. Yan, X, Mischel, P, Chang, H. Extrachromosomal DNA in cancer. Nat Rev Cancer 2024;24:261–73. https://doi.org/10.1038/s41568-024-00669-8.Search in Google Scholar PubMed

228. Gao, X, Liu, K, Luo, S, Tang, M, Liu, N, Jiang, C, et al.. Comparative analysis of methodologies for detecting extrachromosomal circular DNA. Nat Commun 2024;15:9208. https://doi.org/10.1038/s41467-024-53496-8.Search in Google Scholar PubMed PubMed Central

229. Huang, Q, Zhang, S, Wang, G, Han, J. Insight on ecDNA-mediated tumorigenesis and drug resistance. Heliyon 2024;10:e27733. https://doi.org/10.1016/j.heliyon.2024.e27733.Search in Google Scholar PubMed PubMed Central

230. Chen, JP, Diekmann, C, Wu, H, Chen, C, Della Chiara, G, Berrino, E, et al.. scCircle-seq unveils the diversity and complexity of extrachromosomal circular DNAs in single cells. Nat Commun 2024;15:1768. https://doi.org/10.1038/s41467-024-45972-y.Search in Google Scholar PubMed PubMed Central

231. Tang, J, Weiser, NE, Wang, G, Chowdhry, S, Curtis, EJ, Zhao, Y, et al.. Enhancing transcription-replication conflict targets ecDNA-positive cancers. Nature 2024;635:210–8. https://doi.org/10.1038/s41586-024-07802-5.Search in Google Scholar PubMed PubMed Central

232. Jaworski, JJ, Pfuderer, PL, Czyz, P, Petris, G, Boemo, MA, Sale, JE. ecDNA replication is disorganized and vulnerable to replication stress. Nucleic Acids Res 2025;53:gkaf711. https://doi.org/10.1093/nar/gkaf711.Search in Google Scholar PubMed PubMed Central

233. Karami, FM, Karimfar, N, Fazlollahpour Naghibi, A, Shafa, S, Ghasemi Shiran, M, Ataei, M, et al.. Revisiting characteristics of oncogenic extrachromosomal DNA as mobile enhancers on neuroblastoma and glioma cancers. Cancer Cell Int 2022;22:200. https://doi.org/10.1186/s12935-022-02617-8.Search in Google Scholar PubMed PubMed Central

234. Kang, X, Li, X, Zhou, J, Zhang, Y, Qiu, L, Tian, C, et al.. Extrachromosomal DNA replication and maintenance couple with DNA damage pathway in tumors. Cell 2025;188:3405–21 e27. https://doi.org/10.1016/j.cell.2025.04.012.Search in Google Scholar PubMed

235. Lange, JT, Rose, JC, Chen, CY, Pichugin, Y, Xie, L, Tang, J, et al.. The evolutionary dynamics of extrachromosomal DNA in human cancers. Nat Genet 2022;54:1527–33. https://doi.org/10.1038/s41588-022-01177-x.Search in Google Scholar PubMed PubMed Central

236. Yang, QL, Xie, Y, Qiao, K, Lim, JYS, Wu, S. Modern biology of extrachromosomal DNA: a decade-long voyage of discovery. Cell Res 2025;35:11–22. https://doi.org/10.1038/s41422-024-01054-8.Search in Google Scholar PubMed PubMed Central

237. Snyder, MW, Kircher, M, Hill, AJ, Daza, RM, Shendure, J. Cell-free DNA comprises an in vivo nucleosome footprint that informs its tissues-of-origin. Cell 2016;164:57–68. https://doi.org/10.1016/j.cell.2015.11.050.Search in Google Scholar PubMed PubMed Central

238. Ulz, P, Thallinger, GG, Auer, M, Graf, R, Kashofer, K, Jahn, SW, et al.. Inferring expressed genes by whole-genome sequencing of plasma DNA. Nat Genet 2016;48:1273–8. https://doi.org/10.1038/ng.3648.Search in Google Scholar PubMed

239. Cristiano, S, Leal, A, Phallen, J, Fiksel, J, Adleff, V, Bruhm, DC, et al.. Genome-wide cell-free DNA fragmentation in patients with cancer. Nature 2019;570:385–9. https://doi.org/10.1038/s41586-019-1272-6.Search in Google Scholar PubMed PubMed Central

240. Zhang, Z, Zhang, W. Fragmentation patterns of circulating cell-free DNA demonstrate biomarker potential for human cancers. Biota 2019;3:16. https://doi.org/10.21037/biotarget.2019.08.02.Search in Google Scholar PubMed PubMed Central

241. De Sarkar, N, Patton, RD, Doebley, AL, Hanratty, B, Adil, M, Kreitzman, AJ, et al.. Nucleosome patterns in circulating tumor DNA reveal transcriptional regulation of advanced prostate cancer phenotypes. Cancer Discov 2023;13:632–53. https://doi.org/10.1158/2159-8290.cd-22-0692.Search in Google Scholar PubMed PubMed Central

242. Helzer, KT, Sharifi, MN, Sperger, JM, Chrostek, MR, Bootsma, ML, Reese, SR, et al.. Analysis of cfDNA fragmentomics metrics and commercial targeted sequencing panels. Nat Commun 2025;16:9122. https://doi.org/10.1038/s41467-025-64153-z.Search in Google Scholar PubMed PubMed Central

243. Berkemeier, F, Cook, PR, Boemo, MA. DNA replication timing reveals genome-wide features of transcription and fragility. Nat Commun 2025;16:4658. https://doi.org/10.1038/s41467-025-59991-w.Search in Google Scholar PubMed PubMed Central

244. An, Y, Zhao, X, Zhang, Z, Xia, Z, Yang, M, Ma, L, et al.. DNA methylation analysis explores the molecular basis of plasma cell-free DNA fragmentation. Nat Commun 2023;14:287. https://doi.org/10.1038/s41467-023-35959-6.Search in Google Scholar PubMed PubMed Central

245. Liu, Y. At the dawn: cell-free DNA fragmentomics and gene regulation. Br J Cancer 2022;126:379–90. https://doi.org/10.1038/s41416-021-01635-z.Search in Google Scholar PubMed PubMed Central

246. Abbosh, C, Birkbak, NJ, Swanton, C. Early stage NSCLC - challenges to implementing ctDNA-based screening and MRD detection. Nat Rev Clin Oncol 2018;15:577–86. https://doi.org/10.1038/s41571-018-0058-3.Search in Google Scholar PubMed

247. Oxnard, GR, Thress, KS, Alden, RS, Lawrance, R, Paweletz, CP, Cantarini, M, et al.. Association between Plasma genotyping and outcomes of treatment with osimertinib (AZD9291) in advanced non-small-cell lung cancer. J Clin Oncol 2016;34:3375–82. https://doi.org/10.1200/jco.2016.66.7162.Search in Google Scholar

248. Noe, M, Mathios, D, Annapragada, AV, Koul, S, Foda, ZH, Medina, JE, et al.. DNA methylation and gene expression as determinants of genome-wide cell-free DNA fragmentation. Nat Commun 2024;15:6690. https://doi.org/10.1038/s41467-024-50850-8.Search in Google Scholar PubMed PubMed Central

249. Swarup, N, Leung, HY, Choi, I, Aziz, MA, Cheng, JC, Wong, DTW. Cell-free DNA: features and attributes shaping the next frontier in liquid biopsy. Mol Diagn Ther 2025;29:277–90. https://doi.org/10.1007/s40291-025-00773-x.Search in Google Scholar PubMed PubMed Central

250. Williams, MJ, Vazquez-Garcia, I, Tam, G, Wu, M, Varice, N, Havasov, E, et al.. Tracking clonal evolution during treatment in ovarian cancer using cell-free DNA. Nature 2025;647:757–65. https://doi.org/10.1038/s41586-025-09580-0.Search in Google Scholar PubMed PubMed Central

251. Silverman, IM, Schonhoft, JD, Herzberg, B, Yablonovitch, A, Lagow, E, Fiaux, PC, et al.. Genomic and epigenomic ctDNA profiling in liquid biopsies from heavily pretreated patients with DNA damage response-deficient tumors. Clin Cancer Res 2025;31:4136–49. https://doi.org/10.1158/1078-0432.ccr-25-1248.Search in Google Scholar PubMed PubMed Central

252. van ’t Erve, I, Alipanahi, B, Lumbard, K, Skidmore, ZL, Rinaldi, L, Millberg, LK, et al.. Cancer treatment monitoring using cell-free DNA fragmentomes. Nat Commun 2024;15:8801. https://doi.org/10.1038/s41467-024-53017-7.Search in Google Scholar PubMed PubMed Central

253. Doudna, JA. The promise and challenge of therapeutic genome editing. Nature 2020;578:229–36. https://doi.org/10.1038/s41586-020-1978-5.Search in Google Scholar PubMed PubMed Central

254. Hong, M, Clubb, JD, Chen, YY. Engineering CAR-T cells for next-generation cancer therapy. Cancer Cell 2020;38:473–88. https://doi.org/10.1016/j.ccell.2020.07.005.Search in Google Scholar PubMed

255. Baker, DJ, June, CH. CAR T therapy extends its reach to autoimmune diseases. Cell 2022;185:4471–3. https://doi.org/10.1016/j.cell.2022.10.026.Search in Google Scholar PubMed PubMed Central

256. Wang, X, Zhang, Y, Wang, H, Wu, X, He, C, Lin, S, et al.. Allogeneic CD19-targeting T cells for treatment-refractory systemic lupus erythematosus: a phase 1 trial. Nat Med 2025;31:3713–24. https://doi.org/10.1038/s41591-025-03899-x.Search in Google Scholar PubMed

257. Kumaresan, PR, Manuri, PR, Albert, ND, Maiti, S, Singh, H, Mi, T, et al.. Bioengineering T cells to target carbohydrate to treat opportunistic fungal infection. Proc Natl Acad Sci U S A 2014;111:10660–5. https://doi.org/10.1073/pnas.1312789111.Search in Google Scholar PubMed PubMed Central

258. Amor, C, Feucht, J, Leibold, J, Ho, YJ, Zhu, C, Alonso-Curbelo, D, et al.. Senolytic CAR T cells reverse senescence-associated pathologies. Nature 2020;583:127–32. https://doi.org/10.1038/s41586-020-2403-9.Search in Google Scholar PubMed PubMed Central

259. Cong, L, Ran, FA, Cox, D, Lin, S, Barretto, R, Habib, N, et al.. Multiplex genome engineering using CRISPR/cas systems. Science 2013;339:819–23. https://doi.org/10.1126/science.1231143.Search in Google Scholar PubMed PubMed Central

260. Mali, P, Yang, L, Esvelt, KM, Aach, J, Guell, M, DiCarlo, JE, et al.. RNA-Guided human genome engineering via Cas9. Science 2013;339:823–6. https://doi.org/10.1126/science.1232033.Search in Google Scholar PubMed PubMed Central

261. Aussel, C, Cathomen, T, Fuster-García, C. The hidden risks of CRISPR/Cas: structural variations and genome integrity. Nat Commun 2025;16:7208. https://doi.org/10.1038/s41467-025-62606-z.Search in Google Scholar PubMed PubMed Central

262. Baker, DJ, Levine, BL, June, CH. Assessing the oncogenic risk: the long-term safety of autologous chimeric antigen receptor T cells. Lancet 2025;405:751–4. https://doi.org/10.1016/s0140-6736(25)00039-x.Search in Google Scholar PubMed

263. Duncan, CN, Bledsoe, JR, Grzywacz, B, Beckman, A, Bonner, M, Eichler, FS, et al.. Hematologic cancer after gene therapy for cerebral adrenoleukodystrophy. N Engl J Med 2024;391:1287–301. https://doi.org/10.1056/nejmoa2405541.Search in Google Scholar

264. Ozdemirli, M, Loughney, TM, Deniz, E, Chahine, JJ, Albitar, M, Pittaluga, S, et al.. Indolent CD4+ CAR T-Cell lymphoma after cilta-cel CAR T-Cell therapy. N Engl J Med 2024;390:2074–82. https://doi.org/10.1056/nejmoa2401530.Search in Google Scholar

265. Yin, J, Lu, R, Xin, C, Wang, Y, Ling, X, Li, D, et al.. Cas9 exo-endonuclease eliminates chromosomal translocations during genome editing. Nat Commun 2022;13:1204. https://doi.org/10.1038/s41467-022-28900-w.Search in Google Scholar PubMed PubMed Central

266. Liu, M, Zhang, W, Xin, C, Yin, J, Shang, Y, Ai, C, et al.. Global detection of DNA repair outcomes induced by CRISPR–Cas9. Nucleic Acids Res 2021;49:8732–42. https://doi.org/10.1093/nar/gkab686.Search in Google Scholar PubMed PubMed Central

267. Yin, J, Hu, J. The origin of unwanted editing byproducts in gene editing. Acta Biochim Biophys Sin 2022;54:767–81. https://doi.org/10.3724/abbs.2022056.Search in Google Scholar PubMed PubMed Central

268. Chauhan, VP, Sharp, PA, Langer, R. Altered DNA repair pathway engagement by engineered CRISPR-Cas9 nucleases. Proc Natl Acad Sci U S A 2023;120:e2300605120. https://doi.org/10.1073/pnas.2300605120.Search in Google Scholar PubMed PubMed Central

269. Porteus, MH. A new class of medicines through DNA editing. N Engl J Med 2019;380:947–59. https://doi.org/10.1056/nejmra1800729.Search in Google Scholar

270. Wang, X, Wu, X, Tan, B, Zhu, L, Zhang, Y, Lin, L, et al.. Allogeneic CD19-targeted CAR-T therapy in patients with severe myositis and systemic sclerosis. Cell 2024;187:4890–904.e9. https://doi.org/10.1016/j.cell.2024.06.027.Search in Google Scholar PubMed

271. Manghwar, H, Li, B, Ding, X, Hussain, A, Lindsey, K, Zhang, X, et al.. CRISPR/cas systems in genome editing: methodologies and tools for sgRNA design, off-target evaluation, and strategies to mitigate off-target effects. Adv Sci (Weinh) 2020;7:1902312. https://doi.org/10.1002/advs.201902312.Search in Google Scholar PubMed PubMed Central

272. Taheri-Ghahfarokhi, A, Taylor, BJM, Nitsch, R, Lundin, A, Cavallo, AL, Madeyski-Bengtson, K, et al.. Decoding non-random mutational signatures at Cas9 targeted sites. Nucleic Acids Res 2018;46:8417–34. https://doi.org/10.1093/nar/gky653.Search in Google Scholar PubMed PubMed Central

273. Bell, CC, Magor, GW, Gillinder, KR, Perkins, AC. A high-throughput screening strategy for detecting CRISPR-Cas9 induced mutations using next-generation sequencing. BMC Genom 2014;15:1002. https://doi.org/10.1186/1471-2164-15-1002.Search in Google Scholar PubMed PubMed Central

274. Tsai, HH, Kao, HJ, Kuo, MW, Lin, CH, Chang, CM, Chen, YY, et al.. Whole genomic analysis reveals atypical non-homologous off-target large structural variants induced by CRISPR-Cas9-mediated genome editing. Nat Commun 2023;14:5183. https://doi.org/10.1038/s41467-023-40901-x.Search in Google Scholar PubMed PubMed Central

275. Warburton, PE, Sebra, RP. Long-read DNA sequencing: recent advances and remaining challenges. Annu Rev Genom Hum Genet 2023;24:109–32. https://doi.org/10.1146/annurev-genom-101722-103045.Search in Google Scholar PubMed

276. Hsu, PD, Scott, DA, Weinstein, JA, Ran, FA, Konermann, S, Agarwala, V, et al.. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat Biotechnol 2013;31:827–32. https://doi.org/10.1038/nbt.2647.Search in Google Scholar PubMed PubMed Central

277. Tsai, SQ, Nguyen, NT, Malagon-Lopez, J, Topkar, VV, Aryee, MJ, Joung, JK. CIRCLE-seq: a highly sensitive in vitro screen for genome-wide CRISPR–Cas9 nuclease off-targets. Nat Methods 2017;14:607–14. https://doi.org/10.1038/nmeth.4278.Search in Google Scholar PubMed PubMed Central

278. Cameron, P, Fuller, CK, Donohoue, PD, Jones, BN, Thompson, MS, Carter, MM, et al.. Mapping the genomic landscape of CRISPR–Cas9 cleavage. Nat Methods 2017;14:600–6. https://doi.org/10.1038/nmeth.4284.Search in Google Scholar PubMed

279. Hu, J, Meyers, RM, Dong, J, Panchakshari, RA, Alt, FW, Frock, RL. Detecting DNA double-stranded breaks in mammalian genomes by linear amplification–mediated high-throughput genome-wide translocation sequencing. Nat Protoc 2016;11:853–71. https://doi.org/10.1038/nprot.2016.043.Search in Google Scholar PubMed PubMed Central

280. Zuo, E, Sun, Y, Wei, W, Yuan, T, Ying, W, Sun, H, et al.. Cytosine base editor generates substantial off-target single-nucleotide variants in mouse embryos. Science 2019;364:289–92. https://doi.org/10.1126/science.aav9973.Search in Google Scholar PubMed PubMed Central

281. Ferrari, S, Beretta, S, Jacob, A, Cittaro, D, Albano, L, Merelli, I, et al.. BAR-seq clonal tracking of gene-edited cells. Nat Protoc 2021;16:2991–3025. https://doi.org/10.1038/s41596-021-00529-x.Search in Google Scholar PubMed

282. Wu, J, Zou, Z, Liu, Y, Liu, X, Zhangding, Z, Xu, M, et al.. CRISPR/Cas9-induced structural variations expand in T lymphocytes in vivo. Nucleic Acids Res 2022;50:11128–37. https://doi.org/10.1093/nar/gkac887.Search in Google Scholar PubMed PubMed Central

283. Stadtmauer, EA, Fraietta, JA, Davis, MM, Cohen, AD, Weber, KL, Lancaster, E, et al.. CRISPR-engineered T cells in patients with refractory cancer. Science 2020;367. https://doi.org/10.1126/science.aba7365.Search in Google Scholar PubMed PubMed Central

284. Wang, J-H, Gessler, DJ, Zhan, W, Gallagher, TL, Gao, G. Adeno-associated virus as a delivery vector for gene therapy of human diseases. Signal Transduct Targeted Ther 2024;9:78. https://doi.org/10.1038/s41392-024-01780-w.Search in Google Scholar PubMed PubMed Central

285. Geng, G, Xu, Y, Hu, Z, Wang, H, Chen, X, Yuan, W, et al.. Viral and non-viral vectors in gene therapy: current state and clinical perspectives. EBioMedicine 2025;118:105834. https://doi.org/10.1016/j.ebiom.2025.105834.Search in Google Scholar PubMed PubMed Central

286. Hanlon, KS, Kleinstiver, BP, Garcia, SP, Zaborowski, MP, Volak, A, Spirig, SE, et al.. High levels of AAV vector integration into CRISPR-induced DNA breaks. Nat Commun 2019;10:4439. https://doi.org/10.1038/s41467-019-12449-2.Search in Google Scholar PubMed PubMed Central

287. Nelson, CE, Wu, Y, Gemberling, MP, Oliver, ML, Waller, MA, Bohning, JD, et al.. Long-term evaluation of AAV-CRISPR genome editing for Duchenne muscular dystrophy. Nat Med 2019;25:427–32. https://doi.org/10.1038/s41591-019-0344-3.Search in Google Scholar PubMed PubMed Central

288. Kosugi, S, Terao, C. Comparative evaluation of SNVs, indels, and structural variations detected with short- and long-read sequencing data. Hum Genome Var 2024;11:18. https://doi.org/10.1038/s41439-024-00276-x.Search in Google Scholar PubMed PubMed Central

289. Jia, MS, Padmaswari, MH, Burcham, LA, Agrawal, S, Bulliard, GN, Stokes, AL, et al.. Beyond the cut: long-read sequencing reveals complex genomic and transcriptomic changes in AAV-CRISPR therapy for Duchenne muscular dystrophy. bioRxiv 2025. https://doi.org/10.1101/2025.08.01.668007.Search in Google Scholar PubMed PubMed Central

Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/mr-2025-0082).

Received: 2025-12-14
Accepted: 2026-03-11
Published Online: 2026-04-03

© 2026 the author(s), published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

https://doi.org/10.1515/mr-2025-0082
Keywords for this article
DNA replication; DNA damage repair; high-throughput sequencing; early embryonic development; cancer; genome editing
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