OKseqHMM: a genome-wide replication fork directionality analysis toolkit
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ABSTRACT:
PROVIDER: PRJNA796577 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR17583175_1.fastq.gz | Fastqsanger.gz | |||
| SRR17583175_2.fastq.gz | Fastqsanger.gz | |||
| SRR17583176_1.fastq.gz | Fastqsanger.gz | |||
| SRR17583176_2.fastq.gz | Fastqsanger.gz |
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Project description:During each cell division, tens of thousands of DNA replication origins are coordinately activated to ensure the complete duplication of the entire human genome. However, the progression of replication forks can be challenged by numerous factors. One such factor is transcription-replication conflict (TRC), which can either be co-directional or head-on and the latter has been revealed as more dangerous for genome integrity. In order to study the direction of replication fork movement and TRC, we developed a bioinformatics tool, called OKseqHMM, to direct measure the replication fork directionality (RFD) as well as replication initiation and termination, along human genome obtained by sequencing of Okazaki fragments (OK-Seq) and related techniques. We have gathered and analyzed OK-seq data from a large number of organisms including yeast, mouse and human, to generate high-quality RFD profiles and determine initiation zones and termination zones by using Hidden Markov Model (HMM) algorithm (all tools and data are available at https://github.com/CL-CHEN-Lab/OK-Seq). In addition, we have extended our analysis to data obtained by related techniques, such as eSPAN and TrAEL-seq, which also contain RFD information. Our works, therefore, provide an important tool and resource for the community to further study TRC and genome instability, in a wide range of cell line models and growth conditions, which is of prime importance for human health.
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Project description:DNA molecular combing-based replication fork directionality profiling
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Project description:The replication strategy of metazoan genomes is still unclear, mainly because definitive maps of replication origins are missing. High-throughput methods are based on population average and thus may exclusively identify efficient initiation sites, whereas ineffective origins go undetected. Single-molecule analyses of specific loci can detect both common and rare initiation events along the targeted regions. However, these usually concentrate on positioning individual events, which only gives an overview of the replication dynamics. Here, we computed the replication fork directionality (RFD) profiles of two large genes in different transcriptional states in chicken DT40 cells, namely untranscribed and transcribed DMD and CCSER1 expressed at WT levels or overexpressed, by aggregating hundreds of oriented replication tracks detected on individual DNA fibres stretched by molecular combing. These profiles reconstituted RFD domains composed of zones of initiation flanking a zone of termination originally observed in mammalian genomes and were highly consistent with independent population-averaging profiles generated by Okazaki fragment sequencing (OK-seq). Importantly, we demonstrate that inefficient origins do not manifest as detectable RFD shifts, explaining why dispersed initiation has remained invisible to population-based assays. Our method can both generate quantitative profiles and identify discrete events, thereby constituting a comprehensive approach to study metazoan genome replication.
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Project description:During each cell division, tens of thousands of DNA replication origins are co-ordinately activated to ensure the complete duplication of the human genome. However, replication fork progression can be challenged by many factors, including co-directional and head-on transcription-replication conflicts (TRC). Head-on TRCs are more dangerous for genome integrity. To study the direction of replication fork movement and TRCs, we developed a bioinformatics toolkit called OKseqHMM (https://github.com/CL-CHEN-Lab/OK-Seq, https://doi.org/10.5281/zenodo.7428883). Then, we used OKseqHMM to analyse a large number of datasets obtained by Okazaki fragment sequencing to directly measure the genome-wide replication fork directionality (RFD) and to accurately predict replication initiation and termination at a fine resolution in organisms including yeast, mouse and human. We also successfully applied our analysis to other genome-wide sequencing techniques that also contain RFD information (e.g. eSPAN, TrAEL-seq). Our toolkit can be used to predict replication initiation and fork progression direction genome-wide in a wide range of cell models and growth conditions. Comparing the replication and transcription directions allows identifying loci at risk of TRCs, particularly head-on TRCs, and investigating their role in genome instability by checking DNA damage data, which is of prime importance for human health.
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Project description:Genome-wide replication fork directionality measured by strand-specific sequencing of Okazaki fragments in chicken lymphoma cells (DT40).
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Project description:DNA replication progression can be affected by the presence of physical barriers on the DNA, like RNA Polymerases, leading to replication stress and DNA damage. To characterize what happens at sites where DNA replication forks stall and pause, we establish a genome-wide approach to identify them. This approach uses multiple timepoints during S-phase, to identify replication fork/stalling hotspots throughout the genome. These sites are typically associated with increased DNA damage, overlap with fragile sites and with breakpoints of rearrangements identified in cancers, but do not overlap with replication origins. Overlaying these sites with a genome-wide analysis of RNA Polymerase II transcription, we found that replication fork stalling/pausing sites inside genes are directly related to transcription progression and activity. This would support data that indicate that transcription and replication can coexist over the same regions. We found instances where transcription activity by reducing histone density favors replication progression through genes, but also found that slowing down transcription elongation slows down directly replication progression through genes. Importantly, rearrangements found in cancers at transcription-replication collision sites can be detected in non-transformed cells and increased following treatment with ATM and ATR inhibitors. Altogether, our findings highlight how transcription and replication overlap during S-phase, with both positive and negative consequences for replication fork progression and genome stability.
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