FORK-seq: replication landscape of the Saccharomyces cerevisiae genome by nanopore sequencing

Abstract

Genome replication mapping methods profile cell populations, masking cell-to-cell heterogeneity. Here, we describe FORK-seq, a nanopore sequencing method to map replication of single DNA molecules at 200-nucleotide resolution. By quantifying BrdU incorporation along pulse-chased replication intermediates from Saccharomyces cerevisiae, we orient 58,651 replication tracks reproducing population-based replication directionality profiles and map 4964 and 4485 individual initiation and termination events, respectively. Although most events cluster at known origins and fork merging zones, 9% and 18% of initiation and termination events, respectively, occur at many locations previously missed. Thus, FORK-seq reveals the full extent of cell-to-cell heterogeneity in DNA replication.

Human ORC/MCM density is low in active genes and correlates with replication time but does not solely define replication initiation zones

“…This and replication kinetic considerations led us to postulate that following efficient initiation at “master initiation zones” (Ma-IZs) identified by OK-seq, replication proceeds by cascade activation of secondary zones, which are too dispersed and inefficient to leave an imprint on population-averaged profiles (Petryk et al, 2016). Consistently, single-molecule studies of yeast genome replication by nanopore-sequencing revealed that 10-20% of initiation events occur dispersedly and away from known, efficient origins, in a manner undetectable by cell population methods (Hennion et al, 2020; Muller et al, 2019).…”

“…OK-seq data from cycling mouse B cells were downloaded from Tubbs et al (Tubbs et al, 2018) (GSE116319). The RFD profile was computed as in Hennion et al, 2020 with 10kb binning steps. Predicted ERCE shuffling was performed using a homemade function keeping the number of ERCE constant for each chromosome and avoiding unmapped genome sequences (genome regions with more than 20 consecutives N).…”

DNAscent v2: Detecting Replication Forks in Nanopore Sequencing Data with Deep Learning

“…Sequencing with ONT and detecting the position of these bases reveals a footprint of replication fork movement on each sequenced molecule, allowing this method to answer questions that would have been traditionally addressed with DNA fibre analysis but with higher-throughput and the ability to map each sequenced read to the genome. DNAscent (v1 and earlier) uses a hidden Markov model to assign a likelihood of BrdU to each thymidine [7], RepNano uses a convolutional neural network to estimate the fraction of thymidines substituted for BrdU in rolling 96-bp windows [8], and NanoMod compares modified and unmodified DNA to detect base analogues [5, 6]. As this method matures, it is critical that software is able to detect base analogues with high accuracy and throughput across different experimental protocols in a way that is easy to use.…”

“…(b) ROC curves showing the ratio of positive BrdU calls to false positive BrdU calls for four different experiments with different BrdU-for-thymidine substitution rates. The 26% and 49% BrdU samples are from [7] while the 38% and 69% samples are from [8]. The BrdU-for-thymidine substitution rate as measured by mass spectrometry is indicated by the dashed red line.…”

“…A shortcoming of earlier DNAscent versions was that origin calling was designed to work in synchronised early S-phase cells, but works in synchronous and asynchronous cells at any point in S-phase. DNAscent v2 was tested on two different BrdU-pulse experimental protocols: S. cerevisiae cells that were synchronised in G1 and released into S-phase in the presence of BrdU with no thymidine chase [7] and asynchronous thymidine-auxotrophic S. cerevisiae cells where BrdU was pulsed for 4 minutes followed by a thymidine chase [8]. Example single molecules mapping to a region that includes several efficient origins on S. cerevisiae chromosome I are shown for both experiments (Figure 2a-b).…”