Automated mapping of DNA replication fork progression in human cells with ForkML

Domain: chemistry
Published: 2026-01-23
Editorial outcome: major_revision

Abstract
Current approaches to mapping fork progression in the human genome suffer from drastically low throughput. Here, we introduce ForkML, a nanopore sequencing-based method automatically positioning thousands of individual fork velocities by tracking BrdU incorporation into replicating DNA after double pulse-labelling of asynchronous cells. ForkML recovers known human fork speed, accurately detects replication stress, and, crucially, connects replication dynamics to genomic and chromatin contexts, exposing fork slowdown in early-replicating transcribed regions.

Introduction
The regulation of replication fork progression is critical to ensure complete genome duplication, with replication stress impairing fork movement associated with cancer1 and defective embryonic development2. Fork velocity and replication stress have also emerged as factors governing cell fate3–6. For nearly 60 years, DNA fibre assays have been the preferred method to estimate fork rate and detect fork slowdown7. However, although performing well for bulk analysis, they are virtually incapable of locating replication signals, preventing the study of fork progression in its genomic and epigenomic contexts.

Recently, we have elaborated in budding yeast S. cerevisiae high-throughput, near-nucleotide resolution, single-molecule-based replication mapping techniques relying on Oxford Nanopore Technologies (ONT) sequencing to detect the incorporation of the thymidine analogue bromodeoxyuridine (BrdU) in replicating DNA8,9. Notably, we have developed the NanoForkSpeed (NFS) pipeline to extract the velocity of individual forks revealed as BrdU tracks synthesised during a brief pulse-labelling of asynchronously growing cells, followed by a chase with thymidine9. These display a distinct shape made of an abrupt ascending slope starting from a segment with background BrdU level, succeeded by a shallower descending slope, reflecting BrdU incorporation during the pulse and the chase, respectively. NFS captures the position of the fork at the start of the BrdU pulse and at the start of the thymidine chase, then divides the length of the corresponding interval by the duration of the pulse to determine fork speed.

Importantly, NFS exploits the fact that BrdU incorporation is null before the pulse and low but above background level at the end of the thymidine chase to identify and orient each BrdU signal. Unfortunately, for similar experiments performed in human cells, we observed that the level of residual BrdU incorporation after the chase was also virtually null, precluding the use of NFS to detect forks. Since the human eye can differentiate the ascending and descending parts of BrdU signals and infer fork starting position and orientation, we developed an alternative fork-detection strategy based on supervised machine learning trained with manual annotations to capture this information automatically and at scale. We have named this approach ForkML, which readily maps and measures thousands of individual fork speeds in a single ONT PromethION sequencing run of BrdU-labelled human DNA. ForkML retrieves previous estimates of fork velocity in human cells, accurately quantifies replication stress-induced fork slowdown, and relates fork progression to genome organisation and epigenetic regulation.

Results
ForkML labelling strategy consists of two short, consecutive BrdU pulses separated by cell incubation in BrdU-free medium in order to label an ongoing replication fork at two time points, thereby enabling fork speed to be computed as the distance travelled by the fork between the start of the two pulses divided by the time interval between them (Fig. 1a). BrdU labelling concentration and pulse duration were set to 10 µM and 4 min, respectively, to achieve both a high BrdU incorporation and a rapid return to background BrdU level (Supplementary Fig. 1). An interval of 30 min between the two pulse starts was chosen as it allowed the acquisition of close yet non-overlapping and easily recognisable consecutive BrdU signals (Supplementary Fig. 2). BrdU detection was performed thanks to an updated version of our machine learning-based BrdU basecalling model9 optimised for human DNA. More specifically, the neural network was trained using nanopore reads of yeast and human genomic DNA with varying BrdU substitution rates determined by mass spectrometry; the training process also included a dedicated procedure to limit artefactual signals (see Methods). The basecaller estimates the probability of having a BrdU at each thymidine site, with the BrdU content of a given window reflecting the proportion of BrdU-substituted thymidines. Replication tracks from doubly pulse-labelled, asynchronously growing HCT116 epithelial cells typically appeared as pairs of similar, juxtaposed BrdU signals (Fig. 1b, reads #1, #2). As previously observed in yeast8,9, each signal displayed an asymmetrical shape composed of a steep upward slope starting from null BrdU content, followed by a gentle downward slope converging towards zero BrdU, paralleling a rapid BrdU incorporation during the pulse, then a progressive thymidine reincorporation after BrdU removal from the culture medium. ‘Twin’ BrdU signals, therefore, represented an elongating fork captured 30 min apart, with signal asymmetry unveiling fork direction and fork speed corresponding to the ratio between the distance separating the starting positions of the consecutive steep slopes, marking the beginning of each BrdU pulse, and the time interval between pulse starts (i.e. 30 min). Replication initiation and termination events (Fig. 1b, reads #3, #4) as well as replicon clusters (Fig. 1b, read #5) were also frequently spotted. We subsequently implemented a machine learning-based fork detection and orientation pipeline using a neural network trained with manual annotations (Fig. 1c, Supplementary Fig. 3). ForkML’s pipeline was validated by (i) a very low false positive ratio (0.003; Supplementary Data 1), (ii) the consistency between its automatic annotations of BrdU tracks and our own visual interpretations, as shown in Fig. 1b and Supplementary Data 2, and (iii) an excellent correspondence between the local proportion of rightward and leftward forks computed by ForkML and GLOE-seq10 (Supplementary Fig. 4). Remarkably, ForkML’s quantitative monitoring of BrdU content enables accurate tracking of DNA replication until BrdU returns to background levels. For instance, ForkML correctly identified the firing of an origin next to an elongating fork (Fig. 1b, read #6), which would be merged into the same replication track in traditional DNA fibre assays owing to their binary output of thymidine analogue incorporation (Supplementary Fig. 5), resulting in both fork speed overestimation and missed initiation and termination events.

Four independent experiments each provided >2000 fork speed measurements exhibiting a similar, relatively broad dispersion and a consistent median value of 1.2 kb/min (Fig. 1d), in agreement both with the heterogeneous individual fork rates documented in human cells7 and with previous 1.0–1.9 kb/min average fork speed estimates in the HCT116 cell line11–13. Once again, manual and automatic fork speed measurements closely matched (Supplementary Fig. 6). Of note, since DNA fibre length can influence fork speed measurement7 (i.e. the shorter the DNA molecules, the less likely it is to capture fast forks), only nanopore reads that were sufficiently long (>90 kb) to provide an unbiased sampling of fork velocity were selected (Supplementary Fig. 7). ForkML detected fork slowing induced by hydroxyurea (HU) and aphidicolin (APH), two common DNA replication inhibitors (Fig. 1d, Supplementary Fig. 7), demonstrating that it can determine fork speed both under physiological and stressed conditions. As expected, a great concordance between ForkML and manual annotations was also observed in these conditions (Supplementary Fig. 6). Examples of BrdU content profiles in reads from doubly BrdU pulse-labelled HCT116 cells grown in the presence of HU or APH are shown in Supplementary Fig. 8.

Thanks to ForkML’s fork positioning capacity, we could interrogate fork velocity in relationship with the replication timing programme, chromatin environment, and transcriptional activity, finding that fork speeds appear to be lower in early-replicating, actively transcribed regions, in line with recent results14, as well as in constitutive heterochromatin (Fig. 2a–c). Assigning oriented forks to Watson or Crick strands allowed the discrimination of in vivo rates of leading- and lagging-strand synthesis, which are inaccessible to DNA fibre studies (Supplementary Fig. 9). Finally, the mapping of >20,000 individual initiation events confirmed that human genome replication combines initiation in delineated zones and dispersed initiation, as lately reported15,16, with the latter accounting for the overwhelming majority (>85%) of initiation events, consistent with a recent study17 (Fig. 2d).

Importantly, ForkML is compatible with ONT’s latest R10 chemistry (Supplementary Fig. 10), which guarantees its long-term use. To demonstrate that it is applicable to different cell types, we doubly pulse-labelled asynchronous HeLa cells under the same conditions as those optimised for the HCT116 cell line. HeLa BrdU signals resembled HCT116’s (Supplementary Fig. 11) and appeared to be just as well detected based on the comparison with manual annotations (Supplementary Fig. 11 and Supplementary Data 2). Two biological replicates each yielded >2000 fork velocities and a reproducible median fork speed of ~1.5 kb/min (Supplementary Fig. 11). Lastly, to assess the versatility of our BrdU labelling protocol, we applied it to a panel of human normal and cancer cells, namely BJ-hTERT skin fibroblasts, A549 adenocarcinomic alveolar basal epithelial cells, choriocarcinoma-derived JEG-3 cells and LoVo colorectal carcinoma cells. We found that every cell line produced BrdU signals similar in both amplitude and shape to those obtained in HCT116 and HeLa cells, allowing accurate fork speed measurement, as confirmed by manual annotations (Supplementary Fig. 12). Altogether, these results strongly suggest that our DNA replication labelling conditions of two 4-min pulses with 10 µM BrdU and a 30-min interval between pulse starts can be applied as is to estimate fork velocity in most cultured cells. Fork speeds measured by ForkML in the six cell lines examined in this study are recapitulated in Fig. 3.

Unlike a competing approach13, ForkML does not employ lentiviral transduction of the PIP-FUCCI construct followed by cell sorting to enrich for S-phase cells, which simplifies experiments and makes it suitable for routine use. Moreover, whereas both methods yield a similar number of fork speed measurements per sequencing run, ForkML retrieves over five times more initiation and termination events (Supplementary Data 3), an asset for analysing the human replication programme. ForkML is conceptually simpler than the scEdU-seq technique of van den Berg and colleagues14, relying on intricate modelling to calculate fork velocity at the single-cell level. In contrast to the aforementioned methods, replication signal interpretation by ForkML uses machine learning, an approach that often lacks explainability. In this regard, we would like to emphasise that ForkML does not depend on complex features extracted from BrdU tracks to compute fork speed, but only on pulse start positions. Also, should BrdUTP and/or dTTP concentrations not be homogeneous throughout the nucleus, this may locally affect the overall shape and amplitude of BrdU signals but not the location of their starting points, and therefore the estimation of fork speed.

In conclusion, ForkML is a modern, digital version of DNA fibre assays, with a comparable labelling protocol but direct fork mapping, automated data analysis, higher throughput and improved fork tracking. ForkML relies on fast and simple sample preparation, with the BrdU labelling conditions defined here likely being directly applicable to most cultured cells. For cell lines that incorporate BrdU differently, we recommend investigators to use the same modus operandi that we initially followed for HCT116 cells, that is, (i) select a BrdU concentration that allows for sufficiently high incorporation to produce visible tracks while being low enough to avoid reaching a saturation plateau, and (ii) choose a time interval between pulse starts recovering well-separated yet close BrdU signals to facilitate fork detection while maximising the probability of having consecutive signals on the same read and limiting the extent of BrdU-free segments between them where potential replication events are masked. Should a different experimental design be chosen or atypical signals be exposed, it is worth noting that ForkML is a tunable system providing all necessary tools for users to implement a detection model specifically trained under their own conditions. ForkML accompanying web application, which offers BrdU track visualisation and manual annotation, additionally provides a straightforward way to validate detected signals. Finally, since reliable BrdU basecalling following nanopore sequencing has been reported in organisms as diverse as S. cerevisiae8,9,18–21, fission yeast22,23, Plasmodium falciparum24,25, Plasmodium knowlesi26, Trypanosoma cruzi27, Leishmania major28, Drosophila29 and human cells13,17,30, estimation of fork velocity by ForkML is certainly applicable to BrdU pulse-labelled DNA from a wide variety of experimental systems, especially mammalian models such as mouse or rat. ForkML application to organoids or tumoroids is conceivable, although BrdU diffusion into the deeper layers of these three-dimensional structures may be slower than in cultured cells, which could require further optimisation of labelling conditions. ForkML software is available on GitHub (https://github.com/touala/ForkML).

Methods
Cell lines and cell culture
HCT116 human colon carcinoma cells, purchased from the American Type Culture Collection (ATCC; #CCL-247), were grown in McCoy’s 5A medium (Gibco #16600082 and #26600023) with 10% foetal bovine serum (FBS; Dominique Dutscher #500105M1M, batch S00G0); HeLa (MRL2 strain, a gift from O. Bensaude, IBENS) and A549 cells (a gift from A. Lebreton, IBENS) were grown in DMEM + GlutaMAX (4.5 g/L D-Glucose) medium (Gibco #31966021) with 10% FBS; LoVo cells (a gift from A. Lebreton) were grown in DMEM + GlutaMAX (1 g/L D-Glucose) medium (Gibco #21885025) with 10% FBS; JEG-3 cells (a gift from A. Lebreton) were grown in MEM (Gibco #31095029) with 10% FBS; BJ-hTERT cells (a gift from CL. Chen, Institut Curie) were grown in MEM Alpha (Gibco #22561021) with 10% FBS. All media were supplemented with 100 U ml−1 penicillin and 100 µg ml−1 streptomycin (Sigma-Aldrich #P4333). Cells were grown at 37°C, 20% O2, 5% CO2, and routinely confirmed to be negative for mycoplasma contamination.

Double BrdU pulse-labelling of neo-synthesised DNA
Exponentially growing cells were pulse-labelled for 4 min with 10 µM BrdU (Sigma-Aldrich #B5002), washed twice with prewarmed PBS, incubated in BrdU-free conditioned medium then pulse-labelled a second time for 4 min with 10 µM BrdU 30 min after the start of the first BrdU pulse, washed twice with prewarmed PBS and finally incubated again in BrdU-free conditioned medium until 30 min after the start of the second BrdU pulse. Cells were then washed with PBS, harvested by trypsinisation and counted before genomic DNA was extracted using Monarch HMW DNA Extraction Kit for Cells & Blood (New England Biolabs #T3050) according to the manufacturer’s instructions for ultra-long DNA sequencing applications. Conditioned medium came from a ‘sister’ culture dish containing an equal number of cells as the labelled one; it was used to ensure that cell medium composition was strictly the same in the course of the experiment. For BJ-hTERT, two doubly BrdU pulse-labelled samples prepared in parallel were combined prior to genomic DNA extraction to obtain sufficient DNA for library preparation. For the experiment carried out with different BrdU concentrations (Supplementary Fig. 1), cells were pulsed only once with either 0.1, 1, 10 or 100 µM BrdU, and collected 2 h after PBS washes. For the experiment performed with different time intervals between pulse starts (Supplementary Fig. 2), cells were pulsed twice with 10 µM BrdU in the same conditions as above except that the second BrdU pulse was performed 10, 20, 30 or 45 min after the start of the first one and that cell were collected 10, 20, 30 or 45 min after the start of the second BrdU pulse. For experiments in the presence of HU (Sigma-Aldrich #H8627) or APH (Sigma-Aldrich #A4487), cells were treated with either drug 2 h prior to the first BrdU pulse, with PBS and conditioned medium both supplemented with either drug to guarantee treatment continuity.

Library preparation and data acquisition
Samples were sequenced using either R9.4.1 or R10.4.1 flow cells from ONT. MinION and PromethION sequencing libraries were prepared with ONT ligation sequencing kits SQK-ULK001 and SQK-ULK114 (for R9.4.1 and R10.4.1 flow cells, respectively) or ONT ligation sequencing kit SQK-LSK109 in conjunction with ONT EXP-NBD104 Native Barcoding Expansion 1–12 in case of multiplexing according to ONT protocols. During sequencing, flow cells were washed with the ONT EXP-WSH004 kit, and libraries were reloaded up to two times. Data were acquired using MinKNOW (ONT, MinKNOW versions 22.12.5, 23.07.12, 23.11.7, 24.11.10, and 25.03.9) with default parameters. Demultiplexing of barcoded nanopore reads was performed during live basecalling with ONT’s fast model. Detailed information for every sample sequenced in this study is presented in Supplementary Data 1.

BrdU basecalling model update (R9.4.1 datasets)
Training samples. As in ref. 9, we used nanopore reads of genomic DNA samples displaying various BrdU substitution rates. More specifically, S. cerevisiae’s DNA with BrdU contents of 0, 9.4, 16.6, 27.9, 35.1 and 46.1% (from ref. 9) and human DNA from HeLa cells with BrdU contents measured by mass spectrometry of 0, 12.1, 17.4, 28.4, 33.5 and 37.8% (see below) were used. For each sample, composed of a mix of substituted and unsubstituted reads (corresponding to parental DNA), 400 reads were used for training. Substituted reads were separated from unsubstituted ones using our previously trained model9. Considering that the BrdU substitution rate for each pentamer with a central thymidine should correspond to the one extrapolated (i.e. taking into account the fraction of unsubstituted parental DNA) from mass spectrometry data on the cognate DNA sample, we adjusted each pentamer BrdU detection threshold to correct the initial training set. To limit false positives, we selected (i) 100 reads of unlabelled yeast DNA where artefactual replication signals had been detected (these essentially mapped to the ribosomal DNA (rDNA) and to the positions of Ty elements) and 100 reads of genomic DNA from the thymidine-auxotroph MCM869 strain grown for 24 h with BrdU9 mapping to the same regions to introduce the smallest possible bias in the neural network at these locations; (ii) 100 reads (10% of which being from the rDNA) of unlabelled human DNA with artefactual replication signals and 100 reads (15% of which being from the rDNA) of BrdU-labelled human DNA with genuine replication signals overlapping those regions with artefacts, once again not to bias the neural network at such locations; (iii) reads with artefactual signals detected in unlabelled human DNA with intermediate BrdU basecalling models.

Architecture and training. Model training was performed with ONT’s Taiyaki (v5.1.0; https://github.com/nanoporetech/taiyaki) from the Megalodon programme (v2.2.9; https://nanoporetech.github.io/megalodon/) using the long short-term memory (LSTM) architecture referred to as mLstm_cat_mod_flipflop with default parameters. The Taiyaki model was then converted into a Guppy (v4.4.1) model.

BrdU basecalling and read mapping
BrdU basecalling and read mapping were performed as in ref. 9. (Megalodon v2.2.9, Guppy v4.4.1 and minimap2 v2.26) with our updated BrdU model adapted to human DNA (see above) for R9.4.1 datasets and with ONT’s Dorado (v0.7.3, with model fast 5.0.0) followed by DNAscent v4.0.3 (https://github.com/MBoemo/DNAscent) for R10.4.1 datasets. T2T-CHM1331 (v2.0) was used as a reference genome for both types of data.

Identification of genomic regions with problematic read depth
Mapped read depth was computed at each genomic position using samtools depth (v1.16.1; -a -r <chr_name>) to process one chromosome at a time, and further summed up by 30 kbp genomic bins. For each cell type, total bin depths were computed across all datasets (n=9 for HCT116, n=2 for HeLa, and n=1 for A549 and LoVo). Next, we defined ‘problematic’ bins based on two criteria: (i) a depth above or below thresholds set relative to the median genomic depth (min_max_depth), and (ii) large inter-bin depth differences (above th_diff). Problematic bins laying within min_bin_distance of each other were merged to form larger contiguous regions. For HCT116 (HeLa/A549/LoVo) cells, min_max_depth of [0.2, 2] ([0.2, 4]), th_diff of 0.4 (1.0), and min_bin_distance of 600 kbp (same) were used. Our approach allowed the identification of both sharp local anomalies and broader problematic regions in read depth profiles, corresponding to 10.8, 9.0, 10.1, and 8.0% of the T2T-CHM13 genome for HCT116, HeLa, A549, and LoVo sequences, respectively. These regions were excluded from subsequent analyses. BJ-hTERT and JEG-3 datasets were not filtered according to this procedure due to an insufficient genomic coverage.

Training of the BrdU signal segmentation model for ForkML
We have developed ForkML to train and apply BrdU double-pulse detection models. The software is organised into one Nextflow (v22.10.7; https://www.nextflow.io/) pipeline (‘Detection’ pipeline) for using the pre-trained R9 or R10 BrdU signal segmentation models provided here, compatible with nanopore reads from R9.4.1 and R10.4.1 flow cells, respectively, plus two Nextflow pipelines (‘Annotating’ and ‘Training’ pipelines) and a local browser-based Shiny (v1.6.0; https://shiny.posit.co/) application for ForkML users wishing to have a segmentation model trained on signals generated under their own experimental conditions (optional). All pipelines are available on GitHub (https://github.com/touala/ForkML, v0.2.0) with dedicated conda (https://anaconda.org/anaconda/conda) environments to facilitate dependencies installation (including R32 v3.6.3 and v4.4.1, and Python v3.10.0 and v3.10.8). To generate training data for machine learning-based segmentation of BrdU double-pulse signals, the ‘Annotation’ pipeline selects, processes, and organises candidate reads for manual annotation (https://github.com/touala/ForkML/tree/main/annotating). Once BrdU signals have been annotated through the supplied Shiny interface, the ‘Training’ pipeline (https://github.com/touala/ForkML/tree/main/training) is provided to (i) aggregate signals and detailed annotations, (ii) create appropriate training, validation, and testing datasets, and (iii) train and evaluate a machine learning model for signal segmentation.

Data preprocessing and annotation procedure. First, a subset of reads ≥10 kb representative of the biological data is assembled by including both reads with and without BrdU signals. Reads containing putative BrdU incorporation are identified in .bam files (from Megalodon or DNAscent for R9.4.1 and R10.4.1 data, respectively) using basic criteria on base modification probability profiles (MM/ML tags in .bam files). By default, the raw signal is averaged in 100 bp non-overlapping bins, and reads with at least 10 consecutive bins with smoothed signal above 0.1 are considered as containing BrdU for training. In combination with the BrdU signal-based filtering, random reads are also selected. Next, raw modification probability signals are re-extracted and further processed to ease visual interpretation, i.e. values are averaged in 100 bp sliding windows, then smoothed with a centred 2500 bp Gaussian kernel (standard deviation=300). The resulting data are split into small batches of 50 reads to facilitate partial and/or distributed annotation. The pipeline outputs a directory structure compatible with our custom Shiny-based annotating interface, containing all the information required for efficient manual annotation of the various BrdU patterns. The annotation process is performed from a web browser by clicking on positions that delimit and orient a BrdU signal, corresponding to a sharp increase followed by a slow decrease in BrdU level, which defines the transient position of a replication fork. These positions, referred to as x0, x1 and x2, represent the onset of BrdU incorporation, the signal peak, and the return to baseline, respectively (see Supplementary Fig. 3; please note that x0 and x1 are confounded in the case of an initiation event occurring after the start of the BrdU pulse and that x1 and x2 are confounded when two converging forks merge during the BrdU pulse).

Training procedure. The ‘Training’ pipeline is used to train a machine learning model with any number of generated annotations. It supports optimisable configuration of U-Net model architectures33 (e.g. layers, filters, and kernel sizes). First, the annotated .bam files and matched detailed fork annotations (i.e. x0, and x2) are divided into small batches to facilitate distributed computing. BrdU modification probability profiles are subsequently extracted and averaged in 100 bp non-overlapping bins. Each bin is then labelled according to manual annotations to reflect whether it corresponds to background BrdU signal (1), BrdU signal of a rightward replication fork (2), or BrdU signal of a leftward replication fork (3). Processed signals and cognate labels are organised into training, validation, and test datasets in Python’s NumPy array format (by default with a 60, 20, and 20% split, respectively). Finally, signal diversity within sets is doubled by mirroring the BrdU signal and the corresponding annotation. Model training is performed using TensorFlow/Keras (v2.11.0) with GPU or CPU execution with either hyperparameter (HP) tuning or the pre-tuned HPs optimised in this study. Training is carried out with Adam as optimiser (tf.keras.optimizers.Adam), tf.keras.losses.categorical_crossentropy as the loss function, and mean intersection-over-union (IOU) as the main metric (Supplementary Data 2). HP tuning is implemented using Bayesian Optimisation and Hyperband (BOHB) from HpBandSter (v0.7.4; https://github.com/automl/HpBandSter) (Supplementary Data 4). Most steps are designed to run in parallel for scalability, and intermediate and final outputs are structured for reproducibility and easy downstream evaluation with Nextflow automated tracking.

Pre-trained R9 or R10 BrdU signal segmentation models. To train ForkML’s R9 model, we used a random selection of 5000 reads longer than 10 kb and 1000 reads with BrdU signals from three untreated HCT116 replicates doubly pulse-labelled with BrdU (HCT116_UT_R9_rep1, 2, and 3) that had been manually annotated by four researchers (V.R., D.C., A.T., and B.L.T.), with an overlap between annotated read sets to ensure homogenous annotation; differences were resolved with joint review of problematic reads. We also included (i) 5000 random reads longer than 10 kb and 1000 reads with BrdU signals from the four HU- or APH-treated HCT116 samples (HCT116_[HU/APH]_R9_rep1 and 2) manually annotated by A.T. and (ii) artefactual BrdU signals (i.e. noise from imperfect signal acquisition or BrdU basecalling) misclassified as genuine replication forks in an unlabelled sample (HCT116_noBrdU_R9) by a preliminary model in order to limit false positives.

ForkML’s R10 model was trained using a similar approach with reads from doubly BrdU pulse-labelled, untreated HCT116 and HeLa cells (HCT116_UT_R10_rep4 and HeLa_TR_R10; the latter sample was used exclusively for ForkML’s training), manually annotated by two researchers (A.T. and B.L.T.) plus artefactual BrdU signals from an unlabelled sample (HCT116_noBrdU_R10). HPs from the R9 model were reused.

All R9 and R10 training datasets can be found as NumPy arrays in Zenodo (https://doi.org/10.5281/zenodo.15706384).

Processing of raw signal segmentation with ForkML
ForkML ‘Detection’ pipeline processes doubly BrdU pulse-labelled DNA and quantifies fork speed in nanopore sequencing reads from either ONT’s R9.4.1 or R10.4.1 flow cells. The pipeline includes raw BrdU signal preprocessing and segmentation, fork detection and refinement, fork speed calculation, as well as initiation and termination events localisation. It also generates summary statistics, including the number of detected forks, fork speed distribution, and initiation and termination event counts. Fork speeds are further summarised by read length bins (default 40 kb bin size), and visualisations are produced as boxplots as well as a typical subset of individual read profiles with annotated forks, speeds, and events. The complete Nextflow pipeline runs via command-line with configurable parameters, including time interval between pulse starts, bin size, and plotting options.

BrdU detection was performed as described in the ‘BrdU basecalling and read mapping’ section, and the resulting alignment file was used as input for ForkML. The input file was first divided into smaller batches to facilitate parallel processing and leverage cluster infrastructure. BrdU signal was then extracted and smoothed in 100 bp non-overlapping windows as for the training dataset. The appropriate ForkML segmentation model (R9/R10) was loaded and the BrdU signal was segmented into one of the three labels (background or rightward fork or leftward fork) described above. Smoothed signal and prediction were aggregated for potential review of individual reads. For R9, an additional filtering of raw fork predictions was applied to discard suspicious reads based on individual read level signal score computed from mean(abs(p(BrdU)–0.5)), which reflects the uncertainty of BrdU detection from nanopore sequencing (ranging from 0 to 0.5). Only reads with high-confidence signals (signal score ≥ 0.4) were retained. For R10.4.1 datasets, DNAscent does not output single-nucleotide BrdU probabilities but an estimate of local BrdU content, making this metric irrelevant. Next, fork predictions were refined using a two-step procedure to merge closely spaced, co-directional tracks (originating from signal over-segmentation) and remove short, isolated predictions unlikely to represent true replication forks. Merging was performed using distance and size thresholds (e.g. tracks within 1500 bp of each other and smaller than 5000 bp were candidates for merging). Fork predictions at read borders were further processed to account for incomplete signals. Refinement steps, including direction adjustment for ambiguous or discardable signals, were additionally performed. Please note that all of the above is done by default by ForkML. For this study, reads with more than two successive detections in the same orientation, incompatible with our experimental design, as well as reads with detected forks mapping to genomic regions with problematic read depth (see ‘Identification of genomic regions with problematic read depth’ section) were discarded.

Replication fork speeds were computed by pairing consecutive co-directional BrdU signals on the same read and dividing the genomic distance between positions of their respective x0 by the time interval between pulse starts (i.e. 30 min). BrdU signals near read borders, with possibly missing x0 preventing fork speed computation, and closely spaced, diverging BrdU signals corresponding to initiation events arising after the start of the BrdU pulse were excluded from the speed measurement procedure. Both genome-referential and read-referential coordinates were processed for completeness in the case of R10 signals (genomic coordinates were used in this study), which was not relevant for R9 signals, for which BrdU detection assumes perfect read mapping. Replication initiation and termination events were identified along each read as transitions in fork direction (diverging or converging forks, respectively), and positioned as the midpoints between cognate forks’ x0, assuming similar speed for sister forks emanating from one origin and for forks coming from neighbouring origins. Fork type (leading- or lagging-strand synthesis) was assigned based on the mapping strand and directionality (see below).

GLOE-seq data analysis
HCT116 GLOE-seq datasets10 were retrieved from SRA using prefetch-orig followed by fasterq-dump-orig (v2.11.2; –split-files), and trimmed using trim_galore (v0.6.7; -j 8 --length 11 --retain_unpaired -r1 15 -r2 15 --paired). Read mapping was performed with bwa (v0.7.17-r1188) on the T2T-CHM13 reference genome using the samse command. Resulting mappings from replicates were then merged and filtered using samtools (v1.16.1; -q 30). Replication fork directionality (RFD) was then computed from counts of forward and reverse strand read mapping as follows: RFD = (reverse − forward) / (reverse + forward) for all non-overlapping 1 or 10 kbp genomic bins, depending on the analysis.

Validation of fork orientation by ForkML
Local RFD determined by ForkML in HCT116 cells was compared with the HCT116 RFD profile computed from GLOE-seq data in 10 kbp bins to evaluate the accuracy of ForkML in orienting forks. For each fork predicted by ForkML, we listed any overlap with GLOE-seq RFD bin(s) and computed the corresponding RFD weighted average. Only RFD bins with relative read coverage between 20 and 200% of the mean coverage were retained to exclude poorly mapped or complex ploidy regions prone to artefacts with GLOE-seq short reads data. Next, ForkML predicted forks were grouped into 20 equally-sized RFD weighted average intervals ranging from −1 (100% leftward forks) to +1 (100% rightward forks). To highlight variability due to limited sampling, forks within each bin were randomly assigned to five subgroups. Then, for each of those combinations of GLOE-seq RFD bin and subgroup, we computed a pseudo-RFD based on the average ForkML directionality prediction. The resulting data, presented in Supplementary Fig. 4, were visualised as boxplots with overlaid jitter points corresponding to the mean RFD for each of the randomly assigned subgroups, stratified by RFD bins and experimental replicates, to illustrate the correspondence between ForkML predicted fork directionality and gold-standard RFD measurements.

Fork speed versus replication timing and chromatin subcompartments
Genomic positions of high-resolution Repli-seq replication timing (RT) data and chromatin subcompartments annotation in HCT116 cells from ref. 34, 35 were converted to T2T-CHM13 coordinates using the LiftOver tool36 (https://genome.ucsc.edu/cgi-bin/hgLiftOver). For the ‘RT only’ analysis (Fig. 2a), we first computed the weighted average RT across the sixteen Repli-seq fractions using the original 50 kbp bin size. Then, each ForkML fork speed was assigned to the midpoint of the segment delimited by the x0 positions of the two consecutive BrdU signals, which was subsequently associated with the corresponding RT when available. For the ‘chromatin subcompartments’ analysis (Fig. 2b, c), we first defined a genomic range based on both x0 positions used to compute fork speed, then associated the corresponding chromatin subcompartment. Any fork speed overlapping none or more than one annotated subcompartment was discarded. The weighted average RT values computed in 100 kbp bins were then associated with the corresponding fork speeds. Smoothed signals in Fig. 2a, c were computed using a generalised additive model on the 1–99 quantiles of RT values.

Computation of the proportion of individual initiation events inside and outside initiation zones (IZ)
IZ detection was performed as in ref. 37 using HCT116 GLOE-seq-based RFD (see above) computed on 1 kbp genomic bins. Initiation events were detected by ForkML as the midpoints between diverging forks. Each initiation event was then annotated whether falling or not within an IZ using GRanges from the GenomicRanges R package (v1.51.4).

In vivo rates of leading- and lagging-strand synthesis
ForkML enables annotation of replication fork velocities as ‘leading’ or ‘lagging’ by combining fork direction with mapping strand, leveraging the fact that nanopore sequencing reads DNA from 5′ to 3′. Specifically, forward strand-mapping rightward forks and reverse strand-mapping leftward forks correspond to leading-strand synthesis; conversely, forward strand-mapping leftward forks and reverse strand-mapping rightward forks indicate lagging-strand synthesis. Fork velocities from untreated, HU- and APH-treated HCT116 cells were grouped accordingly in Supplementary Fig. 9. Of note, an imbalance in the number of fork speeds between leading and lagging strands was observed in certain samples, as in ref. 13. This is likely attributable to variations in the timing of cell collection and DNA extraction after the final incubation in BrdU-free medium, which may result in incomplete replication tracks. In such cases, since BrdU signals from lagging strands are located closer to the 5′ end of sequencing reads (i.e. the start of sequencing reads), this makes it less likely to detect two consecutive BrdU signals for computing fork speeds.

Statistical analyses
Pearson’s Chi-squared test with Yates’ continuity correction between observed proportions of initiation events inside and outside initiation zones in HCT116 cells and proportions in a randomised control (Fig. 2d) was computed using R (v4.3.0) chisq.test function. Randomised initiation events were simulated by estimating per-chromosome observed initiation density and generating a total of 50,000 random genomic positions, which were filtered to remove positions laying within problematic read depth regions defined above. Finally, the randomised dataset was down-sampled to match the per-chromosome initiation event number and density from the observed dataset.

For leading/lagging strand comparison (Supplementary Fig. 9), the R environment v4.4.2 was used. Prior to analysis, fork speed values were averaged for every sample according to their leading/lagging strand status, in order to (1) decrease the sensitivity of the tests, which otherwise would detect fork speed differences of no practical significance when extremely large number of values are compared and (2) use as unique source of variation the inter-experiment variations, which correspond to error variations, while fork speed variations have a biological origin. Data were fitted to a linear model that included fork speed as the response variable, the leading/lagging strand as a predictive variable, and the biological replicate as a blocking factor. Two by two effect comparisons (two-sided contrast comparisons) were performed with the emmeans() function of the emmeans R package (v1.11.1). Statistical significance was set to p ≤ 0.05. In each case, type I error was controlled by correcting the p values according to the Benjamini and Hochberg method (‘BH’ option in the p.adjust() function of R). Statistical analysis results are detailed in Supplementary Data 5.

Quantification of BrdU by LC-MS/MS
The genomic DNA of HeLa cells grown for 24 h with 20, 30, 50, 75 or 100 µM BrdU or cultured in the absence of BrdU (negative control) was phenol-chloroform-extracted, then either subjected to nanopore sequencing or digested into nucleosides using the Nucleoside Digestion Mix (New England Biolabs #M0649S). Each of the six conditions was analysed once, with a single LC-MS injection per sample. Quantification of BrdU was conducted using a Q Exactive Mass Spectrometer (Thermo Fisher Scientific) equipped with an electrospray ionisation source (H-ESI II Probe) coupled to an Ultimate 3000 RS HPLC (Thermo Fisher Scientific) with a UV detector set at 270 nm for monitoring. A Thermo Fisher Hypersil GOLD aQ chromatography column (100 × 2.1 mm, 1.9 µm particle size) heated to 30°C was used for the injection of digested DNA. The flow rate was maintained at 0.3 ml/min, and the column was operated for 10 min with an isocratic eluent comprising 1% acetonitrile in water containing 0.1% formic acid. Parent ions were fragmented in positive ion mode using parallel reaction monitoring (PRM) at 10% normalised collision energy. MS2 resolution was set at 17,500, the AGC target at 2e5, maximum injection time at 50 ms, and the isolation window at 1.0 m/z. The inclusion list contained the following masses: 243.1 (thymidine, T) and 309.0 (BrdU). Extracted ion chromatograms (±5 ppm) of base fragments used for detection and quantification were 127.0701 and 194.9225 for T and BrdU, respectively. Calibration curves were previously generated using synthetic standards (Biosynth), showing linear responses with R2=1 (Supplementary Data 6). Quantification was performed by integrating the area under the curve (AUC) of the extracted ion chromatograms (Supplementary Data 6). BrdU incorporation was calculated as BrdU / (BrdU + T) and expressed as a percentage of total T (Supplementary Data 6). Data were collected using Xcalibur software (v4.2, Thermo Scientific) and analysed using Freestyle software (v1.5, Thermo Scientific). No statistical tests were applied. UV-LC-MS profiles are shown in Supplementary Data 7.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, the Authors describe ForkML, a method that leverages Oxford Nanopore sequencing technology for mapping DNA replication fork progression in human cells. In ForkML, an asynchronous cell culture is first subjected to a double pulse of BrdU incorporation, followed by genomic DNA extraction and Nanopore library preparation and sequencing. A machine learning algorithm optimized for human DNA is used to call incorporated BrdU bases and, finally, the speed and direction of replication forks is computed based on the profile of BrdU density along the sequenced reads.\nForkML largely builds on two methods previously developed by the same group and specifically tailored for S. cerevisiae cells (FORK-seq – Hennion et al, Genome Biol 2020; and NanoForkSpeed – Theulot et al, Nat Commun 2022), essentially extending their applicability to human cells. In this respect, the novelty of ForkML is limited, although its applicability to human cells is clearly a major step forward.\nThe Authors technically validated their new method using the HCT116 human colorectal cancer cell line, obtaining replication fork speed estimates in line with previous measurements based on GLOE-seq. The Authors show several examples of BrdU density profiles, providing clear guidance on their biological interpretation. They also show that fork speed progression, as assessed by ForkML, varies between different chromatin subcompartments and slows down when cells are pre-treated with replication stress-inducing agents.\nOverall, the manuscript is well written and convincingly shows that replication fork dynamics in human cells can be assessed using Oxford Nanopore technology. Where the current manuscript falls short is in demonstrating a broader application of ForkML to a larger panel of cell lines and more biologically relevant models, such as organoids, hindering the potential for new biological discoveries.\nMAJOR REMARKS\n1) The Authors convincingly show that ForkML is able to detect replication fork progression and can be used to estimate fork speed in human cells. However, a major limitation of the current manuscript is that ForkML was applied to only two cell lines (HCT116 and HeLa), questioning its generalizability and preventing the Authors from potentially making interesting biological discoveries by applying ForkML to more informative model systems, such as organoids. Would the BrdU labeling protocol optimized on HCT116 cells work for other cell lines, or would this require extensive optimization? If this were the case, this would be an obvious limitation for future widespread adoption of ForkML by the DNA damage/repair community.\nThe Authors should demonstrate their method on a panel of commonly used normal and cancer cell lines, providing guidance on how to adjust the timing/concentration of BrdU pulses for different lines. Furthermore, the Authors are encouraged to test their method on organoids or tumoroids (e.g., from colorectal cancer), exploring how replication stressors (aphidicolin or HU) act on those cells.\n2) In Figure 1 and in the Supplementary Figures, the Authors show several examples of BrdU profiles; however, summarizing statistics are missing: how many “twin” vs. single peaks were identified along each chromosome? How many initiation/termination sites? How many instances of firing near an elongating fork, like the one shown in Fig. 1b, read #6?\n3) Related to the previous point, no information about cell-to-cell variability in fork progression is provided. Given that the assay uses asynchronous cell populations, the same genomic region should, in principle, be covered by multiple reads displaying different BrdU profiles: can the Authors show different examples of BrdU profiles for the same region? For a given region, how much does fork speed vary between cells and what is the effect of replication stress on cell-to-cell variability? The Authors should also provide guidance on which genome coverage is needed to make robust quantification of fork progression variability.\n4) It is not clear how the Authors identified and annotated BrdU peaks as exemplified in Fig. 1b: was this done entirely manually or automatically? If the latter, the Authors should briefly introduce the approach used in the main text.\n5) On line 53, the Authors state that they used a BrdU base calling model optimized for human DNA. Would the model also work reliably for other mammalian species, e.g., mouse? The Authors should try to develop a more generalizable model applicable to mammalian cells and not limited to human. This would allow easier and faster adoption of the method by researchers working on non-human mammalian models, such as mouse or rat.\nADDITIONAL REMARKS\nManuscript\n1) Lines 68–73: this part is unclear. Please provide more examples and statistics: how many instances of the pattern shown in read #6 did the Authors find? Why would this “appear as a single replication track in traditional one-dimensional fiber assays”? Please clarify.\n2) Lines 74–76: the explanation of how fork speed was computed should be moved in the previous paragraph after the explanation of the experimental workflow. Please also add an explanation on how BrdU density profiles should be interpreted before describing the results.\n3) In line with the previous comment, a short explanation of how the BrdU base calling model was trained should be provided in the main text, and an example of raw data used for the training should be included in Fig. 1 after the scheme of ForkML workflow, before the current Fig. 1b.\n4) Lines 81–82: “Of note, only adequately long nanopore reads were selected”: what is an adequate length? Please provide a recommended length range. The Authors should also comment on the minimum genome coverage needed (see also major remark #3 above). Did they find the same genomic region and replication pattern in multiple reads?\nFigures\n5) Fig. 1a: please add the duration of BrdU pulses and wash-out intervals.\n6) Fig. 1b: “twin” BrdU peaks as well as other features of the BrdU profiles described in the main text should be marked in this figure to facilitate understanding of the terminology used. Also, different colors could be used to distinguish the first and second pulse. How was the boundary between the first and second pulse identified? This should be included in the explanation of how fork speed was calculated (see Additional remark #2 above)\n7) Also in Fig. 1b: ‘BrdU probability’: there is no explanation in the main text on how this probability is calculated. Please explain.", "reviewer_2": "This manuscript by Rojat et al describes the development of ForkML which is a Nanopore based sequencing method to call the positions of individual replication forks in mammalian genomes.\nThe dynamics of DNA replication in large eukaryotic genomes is incompletely understood and is the subject of much debate. Replication origin use can vary significantly between cells in the same population and so population-based approaches are not well suited to mapping the true complexity of replication patterns in cells. Because of this, single molecule approaches are being developed to map replication of individual DNA molecules. ForkML, utilizes long-read Oxford Nanopore data to map BrdU incorporation during S-phase. The authors use two consecutive pulses of BrdU which allow them to unambiguously define replication fork direction; the decay in BrdU incorporation across genomic distance allows the authors to estimate replication fork rate. Using this data, the authors show that leading and lagging strands have similar rate, and that fork rate changes depending on the chromatin environment.\nOverall, this is concise well-presented study that will be of interest to researchers studying DNA replication dynamics. The method appears to be robust and reproducible. I have only one concern with this manuscript:\n1. The authors should acknowledge that the method essentially measures local concentration of BrdUTP vs dTTP at replication forks, thus apparent differences in fork rate in different chromatin regions may be caused by local variations in dTTP production/availability by RNR etc.., which impact BrdU incorporation rather than actual differences in fork speed.", "reviewer_3": "In ‘Automated mapping of DNA replication fork progression in human cells with ForkML’ by Rojat et al. the author describes a new BrdU pulse labeling scheme in combination with machine learning tools to identify progressing replisomes using Oxford Nanopore sequencing. At face value, the manuscript looks nice and is fairly well-written with appealing figures. However, the manuscript requires very major revisions including an extensive re-writing, comparisons of computational methods and additional experiments to highlight the need for forkML\nMajor comments\nFirst, the authors have already described a method in yeast and this according to the author does virtually the same, so why is there a necessity to employ a machine learning approach for human systems. I assume this is because the origins of replication are not at pre-determined location and they need to call replication direction. This will be completely missed by the broader readership of Nature Communications and also to me results in a complete knowledge gap and\nThe results section is described in a very limited fashion describing two (EDF9-10) to even three (EDF5-7) multipanel extended data figures in a single sentence. This makes it near impossible for the reviewers to properly review this manuscript. The authors need to expand the manuscript to properly explain their results.\nI strongly feel that this method is not delivering anything new over the previously published paper by Jones et al.12 in Nature Communications. The manuscript would need to compare both methods as now it is impossible for the reviewer, or future readers, to assess whether forkML outprforms the previously published analysis framework. I strongly suggest the authors to explore quality metrics and compare these methods.\nIn addition, all the biological insights retrieved from this paper has been previously published by Jones et al. 12 and van den Berg et al. 13. For this manuscript to be a proper fit at Nature Communications, the author needs to show which additional insights can be retrieved. I strongly suggest performing a suite of additional experiments to highlight their machine learning framework\nFinally, the authors state that the Jones et al., uses advanced mathematical modeling, but their entire analyses framework is based on machine learning. These approaches are more often than not even more complicated “under the hood” and hidden by a machine learning framework, which makes it easier to implement but lack explainability. The authors need to address this at the very least in the discussion section, which seems to be completely lacking."}, "author_response": "Response to referees\n• Reviewer #1\nIn this manuscript, the Authors describe ForkML, a method that leverages Oxford Nanopore\nsequencing technology for mapping DNA replication fork progression in human cells. In\nForkML, an asynchronous cell culture is first subjected to a double pulse of BrdU incorporation,\nfollowed by genomic DNA extraction and Nanopore library preparation and sequencing. A\nmachine learning algorithm optimized for human DNA is used to call incorporated BrdU bases\nand, finally, the speed and direction of replication forks is computed based on the profile of\nBrdU density along the sequenced reads.\nForkML largely builds on two methods previously developed by the same group and\nspecifically tailored for S. cerevisiae cells (FORK-seq – Hennion et al, Genome Biol 2020; and\nNanoForkSpeed – Theulot et al, Nat Commun 2022), essentially extending their applicability\nto human cells. In this respect, the novelty of ForkML is limited, although its applicability to\nhuman cells is clearly a major step forward.\nThe Authors technically validated their new method using the HCT116 human colorectal cancer\ncell line, obtaining replication fork speed estimates in line with previous measurements based\non GLOE-seq. The Authors show several examples of BrdU density profiles, providing clear\nguidance on their biological interpretation. They also show that fork speed progression, as\nassessed by ForkML, varies between different chromatin subcompartments and slows down\nwhen cells are pre-treated with replication stress-inducing agents.\nOverall, the manuscript is well written and convincingly shows that replication fork dynamics\nin human cells can be assessed using Oxford Nanopore technology.\nWe thank Reviewer #1 for their comments.\nWhere the current manuscript falls short is in demonstrating a broader application of ForkML\nto a larger panel of cell lines and more biologically relevant models, such as organoids,\nhindering the potential for new biological discoveries.\nPlease see our answer to major remark #1 below.\nMAJOR REMARKS\n1) The Authors convincingly show that ForkML is able to detect replication fork progression\nand can be used to estimate fork speed in human cells. However, a major limitation of the\ncurrent manuscript is that ForkML was applied to only two cell lines (HCT116 and HeLa),\nquestioning its generalizability and preventing the Authors from potentially making interesting\nbiological discoveries by applying ForkML to more informative model systems, such as\norganoids. Would the BrdU labeling protocol optimized on HCT116 cells work for other cell\nlines, or would this require extensive optimization? If this were the case, this would be an\nobvious limitation for future widespread adoption of ForkML by the DNA damage/repair\ncommunity. The Authors should demonstrate their method on a panel of commonly used normal\nand cancer cell lines, providing guidance on how to adjust the timing/concentration of BrdU\npulses for different lines. Furthermore, the Authors are encouraged to test their method on\norganoids or tumoroids (e.g., from colorectal cancer), exploring how replication stressors\n(aphidicolin or HU) act on those cells.\ncells without any additional optimisation. To further assess its versatility, we have applied it to\na panel of commonly used human normal and cancer cells, namely BJ-hTERT skin fibroblasts,\nA549 adenocarcinomic alveolar basal epithelial cells, choriocarcinoma-derived JEG-3 cells and\nLoVo colorectal carcinoma cells, as suggested. We found that all four cell lines produced BrdU\nsignals similar to those obtained in HCT116 and HeLa cells, allowing accurate fork speed\nmeasurement. Altogether, our results strongly suggest that our DNA replication labelling\nconditions of two 4-minute pulses with 10 µM BrdU and a 30-minute interval between pulse\nstarts can be directly applied to estimate fork velocity in most cell lines. These results have been\nincorporated into the manuscript (lines 138-147, new Fig. 3 and new Supplementary Fig. 12,\nsee below). In addition, we provide guidance on how to adjust the timing/concentration of BrdU\npulses for the rare cell lines that may incorporate BrdU differently, emphasising that, should\ndifferent labelling protocols be used or atypical replication signals be exposed, ForkML is a\ntunable system allowing users to implement and validate a BrdU signal segmentation model\nspecifically trained under their own experimental conditions (lines 166-178).\nReviewer #1 also suggested to test organoids or tumoroids. Indeed, to the best of our\nknowledge, no other replication mapping method has explored these systems. While there is no\ndoubt that such studies would be very interesting, it is possible that BrdU diffusion into the\ndeeper layers of tumoroids or organoids would be slower than in cell culture, requiring further\noptimisation of ForkML. Given that we are not expert in organoids nor tumoroids, carrying out\nexperiments in these systems would require collaborative work with a competent group and a\nsubstantial effort that would be better suited for a follow-up study. We have explained in the\nDiscussion that ForkML application to organoids or tumoroids may require further optimisation\n(lines 183-186).\nFigure 3. Replication fork speed estimated by ForkML in a panel of human normal and cancer cell lines.\nViolin plots of individual fork velocities detected by ForkML in doubly BrdU pulse-labelled HCT116, HeLa, BJ-\nhTERT, A549, JEG-3, and LoVo cells. Box, inter-quartile range (IQR); thick line, median value (med.); whiskers,\n1.5x IQR; red dot, mean value; n, number of fork speeds. Only reads exceeding the length threshold determined\nfor each cell line as in Supplementary Fig. 7 were considered. Sequencing was performed using R10.4.1 flow cells.\nUntreated replicates #4 and #1 are shown for HCT116 and HeLa cells, respectively.\nand LoVo cells. a, Examples of BrdU content profiles of R10.4.1 nanopore reads of genomic DNA from doubly\nBrdU pulse-labelled BJ-hTERT, A549, JEG-3 and LoVo cells. b, Violin plots of individual fork velocities detected\nby ForkML on R10.4.1 nanopore reads of genomic DNA from doubly BrdU pulse-labelled cultures of the indicated\ncell line. Box, inter-quartile range (IQR); thick line, median value (med.); whiskers, 1.5x IQR; red dot, mean value;\nn, number of fork speeds. A subset of nanopore reads from the full dataset were subjected to both ForkML detection\nand manual annotation for comparison. Only reads longer than 100, 70, 80 and 90 kb were considered for BJ-\nhTERT, A549, JEG-3 and LoVo cells, respectively (this threshold was determined for each cell line as in\nSupplementary Fig. 7).\n2) In Figure 1 and in the Supplementary Figures, the Authors show several examples of BrdU\nprofiles; however, summarizing statistics are missing: how many “twin” vs. single peaks were\nidentified along each chromosome? How many initiation/termination sites? How many\ninstances of firing near an elongating fork, like the one shown in Fig. 1b, read #6?\nPart of this information was already detailed for the entire genome in Supplementary Table 1\n(now Supplementary Data 1). Columns N and O report the number of forks (or “single peaks”,\n3\nas Reviewer #1 calls them) and the number of fork speeds (“twin peaks”), respectively; the\nnumber of initiation and termination events are reported in columns U and V, respectively. We\nhave added new columns (W-Y) to report the number of origins firing near elongating forks, as\nrequested. We opted for genomic values for clarity and because per chromosome values were\nrelatively low and not necessarily meaningful due to the complex ploidy of most of the cell\nlines studied.\n3)Related to the previous point, no information about cell-to-cell variability in fork progression\nis provided. Given that the assay uses asynchronous cell populations, the same genomic region\nshould, in principle, be covered by multiple reads displaying different BrdU profiles: can the\nAuthors show different examples of BrdU profiles for the same region? For a given region, how\nmuch does fork speed vary between cells and what is the effect of replication stress on cell-to-\ncell variability? The Authors should also provide guidance on which genome coverage is\nneeded to make robust quantification of fork progression variability.\nThe total number of fork speeds in untreated, HU- and APH-treated HCT116 cells is 13227,\n10707 and 12009, respectively, or approximately four fork velocities per megabase of the\nhuman genome for each condition. Therefore, only a very limited number of loci, if any, are\nexpected to be covered by multiple fork speeds. Providing a robust quantification of cell-to-cell\nvariability in replication fork progression is interesting but would require a drastic increase in\nsequencing to achieve sufficient resolution.\n4) It is not clear how the Authors identified and annotated BrdU peaks as exemplified in Fig.\n1b: was this done entirely manually or automatically? If the latter, the Authors should briefly\nintroduce the approach used in the main text.\nBrdU peak annotation in reads presented in Fig. 1b, as well as in Extended Data Figs. 7, 9, 10\n(and now in Supplementary Figs. 5, 8, 10-12), was performed entirely automatically thanks to\nForkML’s machine learning-based fork detection and orientation pipeline employing a neural\nnetwork trained with manual annotations. To clarify this point, we have added the sentence\n“Forks, initiation and termination events and fork speeds automatically detected by ForkML\nare indicated above BrdU signals” in Fig. 1b caption, and the sentence “the consistency between\nits (ForkML) automatic annotations of BrdU tracks and our own visual interpretations, as shown\nin Fig. 1b and Supplementary Data 2” in the main text (lines 96-98).\n5)On line 53, the Authors state that they used a BrdU base calling model optimized for human\nDNA. Would the model also work reliably for other mammalian species, e.g., mouse? The\nAuthors should try to develop a more generalizable model applicable to mammalian cells and\nnot limited to human. This would allow easier and faster adoption of the method by researchers\nworking on non-human mammalian models, such as mouse or rat.\nThe main reason why our BrdU basecalling model, initially trained with budding yeast genomic\nDNA, was further optimised for human DNA was to limit false positives in the ribosomal DNA,\nthat we have studied extensively as part of another project (in preparation). In fact, the exact\nsame model as the one developed in S. cerevisiae (Theulot et al., Nat. Commun., 2022) was\nused in the fission yeast Schizosaccharomyces pombe (Cheng et al., Mol. Cell, 2025) as well as\nin Plasmodium falciparum malaria parasite (Castellano et al., Nucleic Acids Res., 2024), and\ngave good results when applied as is to human DNA. Accordingly, the same DNAscent model\nfor BrdU and EdU detection with ONT’s R9 chemistry was applied for P. falciparum (Totañes\net al., Nucleic Acids Res., 2023) and human cells (Jones et al., Nat. Commun., 2025). Given the\nmuch stronger proximity between human and mouse DNA than between human and yeast or\nPlasmodium falciparum DNA, our BrdU basecalling model – or that of DNAscent – will\ncertainly work satisfactorily with DNA from mouse or from any mammalian species.\norganisms as diverse as Drosophila (Han et al., bioRxiv, 2025), Plasmodium knowlesi (Totañes\net al., Nucleic Acids Res., 2025), Trypanosoma cruzi (de Oliveira Vitarelli et al., mBio, 2024)\nand Leishmania major (Damasceno et al., Cell Rep., 2025), in addition to S. cerevisiae (Muller\net al., Nat. Methods, 2019; Hennion et al., Genome Biol., 2020; Claussin et al., Mol. Cell, 2022;\nTheulot et al., Nat. Commun., 2022; Theulot et al., Nat. Commun., 2025; Thiyagarajan et al.,\nbioRxiv, 2025), human cells (Carrington et al., Genome Biol., 2025; Jaworski et al., Nucleic\nAcids Res., 2025; Jones et al., Nat. Commun., 2025; this study), P. falciparum (Totañes et al.,\nNucleic Acids Res., 2023; Castellano et al., Nucleic Acids Res., 2024), and S. pombe (Cheng et\nal., Mol. Cell, 2025; Díez-Santos et al., bioRxiv, 2025). Results presented at a recent DNA\nreplication meeting additionally reported extension of BrdU detection by nanopore sequencing\nto Arabidopsis thaliana cells. There is therefore already ample evidence that detection of BrdU\nincorporation by nanopore sequencing works in a wide variety of experimental systems, which\nshould facilitate a general adoption of ForkML. This is now indicated in the main text (lines\n178-183).\nADDITIONAL REMARKS\nManuscript\n1) Lines 68–73: this part is unclear. Please provide more examples and statistics: how many\ninstances of the pattern shown in read #6 did the Authors find? Why would this “appear as a\nsingle replication track in traditional one-dimensional fiber assays”? Please clarify.\nAs stated above, we have added new columns (W-Y) in Supplementary Table 1 (now\nSupplementary Data 1) to report the number of origins firing near elongating forks, which\naccount for 5-10% of all initiation events; other examples of this pattern are presented in reads\n#6 of Extended Data Figs. 7, 9, 10 (now Supplementary Figs. 8, 10, 11). We have also modified\nthe related text for clarity (lines 100-104) and added a new figure (Supplementary Fig. 5, see\nbelow) illustrating how this pattern would appear in traditional DNA fibre assays. As a\nreminder, these rely on the sequential incorporation of two thymidine analogues to identify and\norient replication signals, which appear after immunodetection as coloured lines, or “tracks”,\non stretched DNA fibres. In the case of an origin firing next to an elongating fork during the\nlabelling period with one analogue, both replication events will be merged into the same-\ncoloured track, resulting in (i) fork speed overestimation and (ii) missed initiation and\ntermination events.\nSupplementary Figure 5. Comparison between replication signals obtained with ForkML and DNA fibre\nassays. Top, read #6 from HCT116 Untreated replicate #1 after ForkML; bottom, corresponding theoretical signal\n5\nin DNA fibre assays after consecutive pulse-labelling of asynchronous cells with thymidine analogues\niododeoxyuridine (IdU) then chlorodeoxyuridine (CldU) for 30 minutes. The elongating fork and the origin firing\nin its vicinity during the 30-minute IdU labelling period will be part of the same green track, causing the green-to-\nred signal to be interpreted as a leftward fork instead of the combination of an elongating fork, an initiation event\nand a termination event, as revealed by ForkML. Misdetection issues may be mitigated by shortening the labelling\ntimes of 20 to 30 minutes typically used for each pulse in DNA fibre assays.\n2) Lines 74–76: the explanation of how fork speed was computed should be moved in the\nprevious paragraph after the explanation of the experimental workflow. Please also add an\nexplanation on how BrdU density profiles should be interpreted before describing the results.\nAs requested, the explanation of how fork speed was computed has been moved after the\npresentation of the experimental workflow (lines 89-91), and an explanation on how BrdU\ndensity profiles should be interpreted has been added before describing the results (“The\nbasecaller estimates the probability of having a BrdU at each thymidine site, with the BrdU\ncontent of a given window reflecting the proportion of BrdU-substituted thymidines”, lines 79-\n81).\n3) In line with the previous comment, a short explanation of how the BrdU base calling model\nwas trained should be provided in the main text, and an example of raw data used for the training\nshould be included in Fig. 1 after the scheme of ForkML workflow, before the current Fig. 1b.\nA brief description of the training of our BrdU basecalling model is now provided in the main\ntext (“More specifically, the neural network was trained using nanopore reads of yeast and\nhuman genomic DNA with varying BrdU substitution rates determined by mass spectrometry;\nthe training process also included a dedicated procedure to limit artefactual signals”, lines 76-\n79), accompanied by a reference to the “Methods” section where it is further described. Since\nthis model is merely an update of the previous one detailed in Theulot et al., Nat. Commun.,\n2022, we believe it is preferable to simply refer to that article, as it is currently the case in the\ntext, to avoid further complicating Fig. 1.\n4) Lines 81–82: “Of note, only adequately long nanopore reads were selected”: what is an\nadequate length? Please provide a recommended length range.\nAs demonstrated by Techer and colleagues (Techer et al., J. Mol. Biol., 2013), DNA fibre length\ncan influence fork speed estimation: the shorter the DNA molecules, the less likely it is that fast\nforks can be captured. Therefore, in the context of our study, the adequate length of nanopore\nreads corresponds to the minimal read length able to provide an unbiased sampling of fork\nvelocity (90 kb in the case of untreated HCT116 cells, as described in Extended Data Fig. 6,\nnow Supplementary Fig. 7). The relevant passage of the manuscript has been modified for\nclarity (“Of note, since DNA fibre length can influence fork speed measurement (Techer et al.,\nJ. Mol. Biol., 2013) (i.e., the shorter the DNA molecules, the less likely it is to capture fast\nforks), only nanopore reads that were sufficiently long (>90 kb) to provide an unbiased\nsampling of fork velocity were selected (Supplementary Fig. 7)”, lines 111-114). Since the\nadequate read length depends on the speed of the forks in the cell line under scrutiny, the\nthreshold was determined for each cell line using the modus operandi described in Extended\nData Fig. 6 (now Supplementary Fig. 7), as indicated in the caption of the cognate figures.\nThe Authors should also comment on the minimum genome coverage needed (see also major\nremark #3 above). Did they find the same genomic region and replication pattern in multiple\nreads?\nOne ForkML experiment run on a PromethION flow cell typically provides >2000 positioned\nindividual fork velocities, which greatly exceeds the usual throughput of a few hundred speeds\n6\nof unknown location of traditional DNA fibre assays. Nevertheless, this corresponds to a\ngenomic coverage of less than one fork velocity per megabase of the human genome, with\nhardly any locus covered by multiple fork speeds. The current coverage is therefore not\ncompatible with a convincing quantification of cell-to-cell and locus-to-locus fork progression\nvariability, but it is sufficient to identify fork speed differences according to replication timing\nand between chromatin compartments.\nFigures\n5)Fig. 1a: please add the duration of BrdU pulses and wash-out intervals.\nSince the duration and concentration of the BrdU pulse as well as the washing intervals can be\nadjusted at users’ will, we believe this general version of ForkML experimental workflow is\npreferable. The labelling conditions defined in our study and likely applicable to any cell line\nhave been added to Fig. 1a caption.\n6) Fig. 1b: “twin” BrdU peaks as well as other features of the BrdU profiles described in the\nmain text should be marked in this figure to facilitate understanding of the terminology used.\nAlso, different colors could be used to distinguish the first and second pulse.\nTo avoid overloading Fig. 1b, and because “Rightward fork”, “Leftward fork”, “Initiation\nevent” and “Termination event” are already defined below the reads, the requested indications\nhave been added to the caption (“Reads #1, #2: elongating forks (“twin” BrdU signals); read\n#3, initiation event (diverging forks); read #4, termination event (converging forks); read #5,\nreplicon cluster; read #6, origin firing next to an elongating fork”). We prefer to refrain from\nusing different colours to distinguish the first and second pulse for clarity.\nHow was the boundary between the first and second pulse identified? This should be included\nin the explanation of how fork speed was calculated (see Additional remark #2 above)\nThe boundary between the first and the second pulse was defined as the starting point of the\nsteep slope of the second BrdU signal, which was identified, like the starting point of the steep\nslope of the first BrdU signal, by ForkML segmentation model. This was mentioned in the\ndescription of how fork speed was calculated, with the corresponding sentence (“the ratio\nbetween the distance separating the starting positions of the consecutive steep slopes, marking\nthe beginning of each BrdU pulse, and the time interval between pulse starts (i.e., 30 minutes)”)\nnow located right after the explanation of the experimental workflow (lines 89-91), as requested\nby Reviewer #1.\n7) Also in Fig. 1b: ‘BrdU probability’: there is no explanation in the main text on how this\nprobability is calculated. Please explain.\n“BrdU probability” refers to the likelihood that a thymidine site in a given genomic window\nincorporated a BrdU, which corresponds in practice to the local fraction of BrdU-substituted\nthymidines. For clarity, the Y-axis legend of BrdU content profiles is now \"Predicted BrdU\ncontent\".\n7\n• Reviewer #2\nThis manuscript by Rojat et al describes the development of ForkML which is a Nanopore\nbased sequencing method to call the positions of individual replication forks in mammalian\ngenomes.\nThe dynamics of DNA replication in large eukaryotic genomes is incompletely understood and\nis the subject of much debate. Replication origin use can vary significantly between cells in the\nsame population and so population-based approaches are not well suited to mapping the true\ncomplexity of replication patterns in cells. Because of this, single molecule approaches are\nbeing developed to map replication of individual DNA molecules. ForkML, utilizes long-read\nOxford Nanopore data to map BrdU incorporation during S-phase. The authors use two\nconsecutive pulses of BrdU which allow them to unambiguously define replication fork\ndirection; the decay in BrdU incorporation across genomic distance allows the authors to\nestimate replication fork rate. Using this data, the authors show that leading and lagging strands\nhave similar rate, and that fork rate changes depending on the chromatin environment.\nOverall, this is concise well-presented study that will be of interest to researchers studying DNA\nreplication dynamics. The method appears to be robust and reproducible.\nWe thank Reviewer #2 for their comments.\nI have only one concern with this manuscript:\n1.The authors should acknowledge that the method essentially measures local concentration of\nBrdUTP vs dTTP at replication forks, thus apparent differences in fork rate in different\nchromatin regions may be caused by local variations in dTTP production/availability by RNR\netc.., which impact BrdU incorporation rather than actual differences in fork speed.\nOur method indeed computes the ratio of BrdUTP to BrdUTP+dTTP along positions of an\nelongating fork across a defined time interval. However, fork speed measurement only depends\non the position of the fork at the start of each BrdU pulse (x position).\nproduction/availability of dTTP in different chromatin regions may affect the overall shape and\namplitude of the BrdU signal according to the temporal evolution of the local BrdUTP and\ndTTP concentrations, but they will not affect x positions.\nDiscussion to explain that BrdUTP and dTTP concentrations may not be homogeneous\nthroughout the nucleus, which may locally affect the shape of BrdU signals without altering the\ndetection of their starting points, and therefore the estimation of fork speed (lines 158-161).\n8\n• Reviewer #3\nIn ‘Automated mapping of DNA replication fork progression in human cells with ForkML’ by\nRojat et al. the author describes a new BrdU pulse labeling scheme in combination with machine\nlearning tools to identify progressing replisomes using Oxford Nanopore sequencing. At face\nvalue, the manuscript looks nice and is fairly well-written with appealing figures.\nWe thank Reviewer #3 for their comments.\nHowever, the manuscript requires very major revisions including an extensive re-writing,\ncomparisons of computational methods and additional experiments to highlight the need for\nforkML\nMajor comments\nFirst, the authors have already described a method in yeast and this according to the author does\nvirtually the same, so why is there a necessity to employ a machine learning approach for human\nsystems. I assume this is because the origins of replication are not at pre-determined location\nand they need to call replication direction. This will be completely missed by the broader\nreadership of Nature Communications and also to me results in a complete knowledge gap and\nWe thank Reviewer #3 for pointing out that the broader readership may not understand why we\ndid not proceed in exactly the same way with human cells as with budding yeast.\nAs described in Theulot et al., Nat. Commun., 2022, to compute fork speed in S. cerevisiae,\nasynchronously growing cells are labelled with a 2-min BrdU pulse followed by a 20 min chase\nwith a ten-fold excess of thymidine. The resulting BrdU signals display a distinct, asymmetrical\nshape made of an abrupt ascending slope starting from a segment with background BrdU level\nsucceeded by a shallower descending slope, reflecting BrdU incorporation during the pulse and\nthe chase, respectively. The NanoForkSpeed (NFS) pipeline captures the position of the fork at\nthe start of the BrdU pulse and at the start of the thymidine chase, then divides the length of the\ncorresponding interval by the duration of the pulse to determine fork speed. Importantly, NFS\nexploits the fact that BrdU incorporation is null before the pulse and low but above background\nlevel at the end of the thymidine chase to identify and orient each BrdU signal. Unfortunately,\nfor similar experiments performed in human cells, we observed that the level of residual BrdU\nincorporation after the chase was also virtually null, precluding the use of NFS to detect forks.\nSince the human eye can differentiate the ascending and descending parts of BrdU signals and\ninfer fork starting position and orientation, we developed an alternative fork-detection strategy\nbased on supervised machine learning trained with manual annotations to capture this\ninformation automatically and at scale. This point has now been added in the Introduction (lines\n43-59).\nThe results section is described in a very limited fashion describing two (EDF9-10) to even\nthree (EDF5-7) multipanel extended data figures in a single sentence. This makes it near\nimpossible for the reviewers to properly review this manuscript. The authors need to expand\nthe manuscript to properly explain their results.\nThis manuscript was first submitted to another journal that required a very compact format for\nsubmission and was directly transferred to Nature Communications without reformatting. We\nhave now expanded the manuscript to better explain, detail and discuss our results (see\nsentences highlighted in yellow in the revised version of the manuscript).\nI strongly feel that this method is not delivering anything new over the previously published\npaper by Jones et al.12 in Nature Communications. The manuscript would need to compare\n9\nboth methods as now it is impossible for the reviewer, or future readers, to assess whether\nforkML outprforms the previously published analysis framework. I strongly suggest the authors\nto explore quality metrics and compare these methods.\nWe thank Reviewer #3 for raising this important point. We now include a dedicated analysis\n(new Supplementary Data 3, see below) comparing ForkML to the recent method by Jones and\ncolleagues (hereafter referred to as “DNAscent”). This analysis shows that both approaches\nyield a similar number of fork speed measurements per sequencing run. However, ForkML\noffers several practical and analytical advantages that we believe will appeal to users. First, in\ncontrast to DNAscent, ForkML does not use lentiviral transduction of the PIP-FUCCI construct\nfollowed by cell sorting to enrich for S-phase cells. Avoiding these steps substantially reduces\nexperimental complexity, time, and costs, and makes ForkML accessible to laboratories without\ncell-sorting capabilities. In addition to a simplified experimental workflow, ForkML displays a\ngreater sensitivity for the detection of initiation and termination events, identifying more than\nfive times as many events of this type as DNAscent in comparable datasets. This was\ninstrumental for the robust quantification of initiation inside and outside initiation zones (Fig.\n2d), and is valuable when analysing the human replication program. Importantly, this higher\nrate is not attributable to overdetection issues since Supplementary Table 2 (now\nSupplementary Data 2) demonstrates good concordance between manual annotations and\nForkML-based detection. Finally, although it is difficult to directly compare the accuracy of\nfork speed measurement by ForkML and DNAscent, visual inspection of the respective\nreplication signals suggests that it is easier to position the starting positions of two consecutive\nsteep slopes in the case of ForkML than to call the start and end of DNAscent’s replication\ntracks. Moreover, ForkML quantitatively monitors BrdU incorporation after the pulse, enabling\ndetection of subtle signal merging that can otherwise bias fork speed measurement. The\ncomparison between ForkML and DNAscent (and the scEdU-seq technique of van den Berg et\nal., see below) has been incorporated in the main text, lines 148-158.\nSupplementary Data 3. Comparative analysis between ForkML and the method developed by Jones and\ncolleagues13 for fork speed estimation and detection of initiation and termination events. Data for Jones et al.\n2025 are from Figure 5 and Table S1 in ref. 13. As Table S1 does not report the total sequencing output per sample,\na direct comparison between studies based on sequencing yields could not be performed. Moreover, any\ndifferences in output would be difficult to attribute unambiguously to varying sequencing efficiencies or to\nupstream differences in sample preparation inherent to the respective experimental workflows. For this reason,\ncomparative analysis was performed at the level of individual sequencing runs, assuming these to be representative\nof typical yields. To enable a technically meaningful comparison, analysis was further restricted to datasets\ngenerated with the same chemistry and sequencing platform, namely R10.4.1 PromethION flow cells. We therefore\nexclusively focused on the two R10 PromethION datasets generated by Jones et al. (RPE1-Untreated and RPE1-\nATRi, as listed in the ENA repository, accession number PRJEB80561) and the seven R10 PromethION runs from\nour study.\nby Jones et al. 12 and van den Berg et al. 13. For this manuscript to be a proper fit at Nature\nCommunications, the author needs to show which additional insights can be retrieved. I strongly\nsuggest performing a suite of additional experiments to highlight their machine learning\nframework\nWe respectfully note that the primary aim of our study is to introduce ForkML as a robust,\nscalable, and broadly accessible method for mapping replication fork progression in the human\ngenome, capable of replacing approaches that have been used for decades, while providing\nhigher throughput, higher resolution, and direct fork mapping. As with most methodological\nstudies, demonstration of performance necessarily includes assessing whether the technique can\nreproduce established biological findings, which we consider to be an essential validation rather\nthan a limitation. In this regard, ForkML successfully recapitulates key observations reported\nby Jones et al. and van den Berg et al., and does so without the substantial experimental\nrequirements of these earlier studies (i.e., lentiviral transduction and cell sorting for DNAscent;\nsingle-cell isolation for scEdU-seq). This simplicity makes ForkML suitable for routine\nimplementation in any molecular biology laboratory using nanopore sequencing. Beyond\nreplicating the results from Jones et al. and van den Berg et al., ForkML independently confirms\nthe recent observation by Carrington et al. that most initiation events occur dispersively in the\nhuman genome (Carrington et al., Genome Biol., 2025). The fact that a single methodological\nframework can recover the main insights from three different studies, each employing distinct\nexperimental modalities and analytical principles, underscores the generality, robustness, and\nversatility of our approach. In conclusion, by enabling high-throughput fork progression\nanalysis with minimal experimental burden, ForkML provides precisely the type of tool that\npromotes cross-validation between studies. Comprehensive discovery-driven analyses (e.g.,\ngenome-wide maps of fork progression across cell types and conditions) would require a strong\nincrease in sequencing depth. We consider such large-scale biological investigations more\nappropriate for a dedicated follow-up study, building upon the methodological foundations\nestablished here.\nFinally, the authors state that the Jones et al., uses advanced mathematical modeling, but their\nentire analyses framework is based on machine learning. These approaches are more often than\nnot even more complicated “under the hood” and hidden by a machine learning framework,\nwhich makes it easier to implement but lack explainability. The authors need to address this at\nthe very least in the discussion section, which seems to be completely lacking.\nThe original sentence aimed at meaning that ForkML does not require S-phase cell sorting,\nunlike Jones et al., nor advanced mathematical modelling, unlike van den Berg et al. However,\nwe understand that it could be interpreted in Reviewer#3’s way. We apologise for the\nmisunderstanding. The sentence has been deleted and replaced with a complete paragraph for\nclarity (lines 148-155). Regarding Reviewer #3’s comment that machine learning approaches\nare easier to implement but lack explainability, we would like to point out that ForkML does\nnot extract complex features from the BrdU signals to compute fork speed, only the positions\nmarking the beginning of BrdU pulses. This point is now discussed in the main text (lines\n155-158).\nLine 36: an additional reference (Wang et al., 2025, published while our manuscript was under\nreview) has been included.\nLines 75: “using” has been replaced with “thanks to”.\nLines 76: “BrdU basecalling model” has been replaced with “machine learning-based BrdU\nbasecalling model”.\nLines 83-84: the sentence has been slightly modified for clarity.\nLine 95: “Our” has been replaced with “ForkML’s”.\nLine 109: “1” has been replaced with “1.0”.\nLines 123-124: “forks are slower in early-replicating, actively transcribed regions (Fig. 2a-c),\nin line with recent results (van den Berg et al, 2024)” has been replaced with “fork speeds\nappear to be lower in early-replicating, actively transcribed regions, in line with recent results\n(van den Berg et al, 2024), as well as in constitutive heterochromatin (Fig. 2a-c)”.\nLine 213: “twin” has been replaced with “sister”.\nLines 314-315: “a Shiny web application” has been replaced with “a local browser-based Shiny\napplication”.\nLine 318: “v0.1.0” has been replaced with “v0.2.0”.\nLine 324: “web” has been removed to read “Shiny interface”.\nLine 419: “co-directional detections” has been replaced with “successive detections in the same\norientation”.\nLine 460: jitter points have been defined as “corresponding to the mean RFD for each of the\nrandomly assigned subgroups”.\nLine 469: “the” has been added to read “using the original”.\nLine 470: “discretised into the middle” has been replaced with “assigned to the midpoint”.\nLine 502: “two-sided” has been removed since it is not relevant for Pearson's Chi-squared test.\nMethods have been updated to take into account the new analyses performed in response to\nreviewers’ comments (lines 194-199, 215-217, 235, 277, 281, 284, 285, and 286-287).\n“Inter-pulse time interval” and “to randomly sample fork speed/velocity” have been replaced\nthroughout the manuscript with “time interval between pulse starts” and “to provide an unbiased\nsampling of fork velocity”, respectively, for clarity.\nFigure captions have been moved to the end of the manuscript.\nManuscript organisation, reference numbering and supplementary material name (i.e.,\n“Supplementary Fig.” instead of “Extended Data Fig.”, “Supplementary Data” instead of\n“Supplementary Table”) have been updated to comply with Nature Communications formatting\ninstructions.\nBecause of the addition of new Supplementary Fig. 5, former Extended Data Figs. 5-10 are now\nSupplementary Figs. 6-11.\nFormer Supplementary Table 3 is now Supplementary Data 5.\nIn Fig. 1 and Supplementary Figs. 6, 9-12 captions, “larger” has been replaced with “longer”.\n• Reviewer #1 (Remarks to the Author):\nThe Authors have addressed all of my comments and expanded the number of cell lines profiled\nby ForkML, as I had suggested. Overall, the Authors' revisions have substantially improved the\nclarity and quality of the manuscript and figures. I therefore commend the Authors for their\nwork.\n• Reviewer #2 (Remarks to the Author):\nThe reviewers have addressed my concerns.\n• Reviewer #3 (Remarks to the Author):\nThe authors have considerably increased the quality, readability and validation for forkML.\nForkML will be a great addition to the field of DNA replication. the authors have adressed all\nof my concerns, therefore, I recommend acceptence of this manuscript.\nReviewer #3 (Remarks on code availability):\nThe code is present on github and nicely annotated for use by other researchers.\nWe thank all three reviewers for their comments.\n1"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The Authors have addressed all of my comments and expanded the number of cell lines profiled by ForkML, as I had suggested. Overall, the Authors' revisions have substantially improved the clarity and quality of the manuscript and figures. I therefore commend the Authors for their work.", "reviewer_2": "The reviewers have addressed my concerns.", "reviewer_3": "The authors have considerably increased the quality, readability and validation for forkML. ForkML will be a great addition to the field of DNA replication. the authors have adressed all of my concerns, therefore, I recommend acceptence of this manuscript."}, "author_response": ""}]

Decision letter