Measuring DNA replication timing of maize root tips with Repli-seq method
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ABSTRACT:
PROVIDER: PRJNA1219493 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR32237161_1.fastq.gz | Fastqsanger.gz | |||
| SRR32237161_2.fastq.gz | Fastqsanger.gz | |||
| SRR32237162_1.fastq.gz | Fastqsanger.gz | |||
| SRR32237162_2.fastq.gz | Fastqsanger.gz | |||
| SRR32237163_1.fastq.gz | Fastqsanger.gz |
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Project description:DNA replication is a highly regulated cell cycle process that integrates the time that a genome segment replicates in S phase with its transcriptional activity, chromatin structure, and epigenetic state. The overall goal of this project is to define such linkages in maize by exploiting the genetic and genomic resources of this species. We used an updated Repli-seq protocol to profile replication timing in Zea mays B73 root tips and compare it to other methods for profiling replication timing.
2025-04-28 | GSE288800 | GEO
Project description:genome-wide analysis of replication timing by repli-chip and repli-seq
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Project description:During the cell cycle, DNA replication is a highly regulated process that integrates the time that a genome segment replicates in S phase with its transcriptional activity, chromatin structure, and epigenetic state. We are using fluorescence activated nuclei sorting in combination with high throughput sequencing to characterize the patterns of DNA replication across the maize B73 genome using a DNA copy number based procedure.
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Project description:Mouse ESC 46C replication timing has been assessed by repli-chip.
2017-07-28 | GSE95090 | GEO
Project description:This track is produced as part of the ENCODE Project. This track shows genome-wide assessment of DNA replication timing in cell lines using the sequencing-based "Repli-seq" methodology (see below). Replication timing is known to be an important feature for epigenetic control of gene expression that usually operates at a higher-order level than at the level of specific genes. For each experiment (cell line, replicate), replication timing was ascertained by the isolation and sequencing of newly replicated DNA from six cell cycle fractions: G1/G1b, S1, S2, S3, S4, G2 (six fraction profile). Replication patterns are visualized as a continuous function based on sequencing tag density (Percentage-normalized Signal) and as a wavelet-smoothed transform of the six fraction profile (Wavelet-smoothed Signal). Replication peaks corresponding to replication initiation zones (Peaks) and valleys corresponding to replication termination zones (Valleys) were determined from local maxima and minima, respectively, in the wavelet-smoothed signal data. A measure of relative copy number at each genomic location (Summed Densities) was determined by summing the normalized tag density values of each cell cycle fraction at that location (equals one replicated genome equivalent). For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Cells were grown according to the approved ENCODE cell culture protocols. Repli-seq was performed as described by Hansen et al. (2010). Briefly, newly replicated DNA was labeled in vivo with a pulse of 5-bromo-2-deoxyuridine (BrdU), cells were fractionated into six different parts of the cell cycle by flow cytometry according to DNA content, cell cycle fractionated DNA was sonicated and an anti-BrdU monoclonal antibody was used to isolate the newly replicating DNA. Fragment ends were sequenced using the Illumina Genome Analyzer II or HiSeq platforms (36 bp reads). Some experiments (BJ, K562, BG02ES, GM06990) were originally performed and mapped to an earlier version of the human reference genome NCBI36/hg18 (Hansen et al., 2010) and were remapped to the more recent reference genome GRCh37/hg19. Uniquely mapping high-quality reads were mapped to the genome minus the Y chromosome. Replication signals within each six cell cycle fraction were derived from the density of sequence tags mapping within a 50 kb sliding window (stepped 1 kb across the genome); these densities were normalized to 4 million tags per genome. To avoid variation due to copy number or sequence bias, cell cycle-specific replication signals at each location were determined as a percentage of the sum of the six normalized tag density signals (Percentage-normalized Signal). To transform the six fraction replication signals into one track (Wavelet-smoothed Signal), the percentage-normalized signals at each location were used to calculate a weighted average value based on the average DNA content of each fraction according to flow cytometry [higher values correspond to earlier replication; formula=(0.917*G1b)+(0.750*S1)+(0.583*S2)+(0.417*S3)+(0.250*S4)+(0*G2)]. These weighted average data were smoothed by wavelet transformation [J7 level, corresponding to a scale of 128 kb; see Thurman et al. (2007)]. Replication initiation zones were flagged by determining local maxima in the wavelet-smoothed data (Peaks) and, similarly, replication termination zones were flagged by local minima (Valleys). The sum of the 4 million normalized replication tag densities correspond to replication of one genome and can, therefore, be used as a measure of relative genomic copy number (Summed Densities). This is useful for evaluation of unusual replication patterns, such as "biphasic" ones where replication has both early and late components [as described by Hansen et al. (2010)].
2012-04-26 | E-GEOD-34399 | biostudies-arrayexpress
Project description:The role of DNA sequence in determining replication timing (RT) and chromatin higher order organization remains elusive. To address this question, we have developed an extra-chromosomal replication system consisting of ~200kb human bacteria artificial chromosomes (BACs) modified with Epstein-Barr virus (EBV) replication origin elements (E-BACs). E-BACs were stably maintained as autonomous mini-chromosomes in both HeLa and human induced pluripotent stem cells (hiPSCs) and established their RT de novo. We applied repli-seq to evaluate E-BACs' replication timing.
2018-01-29 | GSE102522 | GEO
Project description:DNA replication timing is known to facilitate the establishment of the epigenome, however, the intimate connection between replication timing and changes to the genome and epigenome in cancer remain largely uncharacterised. Here, we perform Repli-Seq and integrated epigenome analyses and demonstrate that genomic regions that undergo long-range epigenetic deregulation in prostate cancer also show concordant differences in replication timing. A subset of altered replication timing domains are conserved across cancers from different tissue origins. Notably, late-replicating regions in cancer cells display a loss of DNA methylation, and a switch in heterochromatin features from H3K9me3-marked constitutive to H3K27me3-marked facultative heterochromatin. Finally, analysis of 214 prostate and 35 breast cancer genomes reveal that late-replicating regions are prone to cis and early-replication to trans chromosomal rearrangements. Together, our data suggests that the nature of chromosomal rearrangement in cancer is related to the spatial and temporal positioning and altered epigenetic states of early-replicating compared to late-replicating loci.
2019-01-23 | GSE98730 | GEO
Project description:E/L Repli-seq is a powerful tool for detecting cell type-specific replication landscapes in mammalian cells, but its potential to monitor DNA replication under replication stress awaits better understanding. Here, we used E/L Repli-seq to examine the temporal order of DNA replication in human retinal pigment epithelium cells treated with the topoisomerase I inhibitor camptothecin. We found that the replication profiles by E/L Repli-seq exhibits characteristic patterns after replication-stress induction, including the loss of specific initiation zones within individual early replicating timing domains. We also observed global disappearance of the replication timing domain structures in the profiles, which can be explained by checkpoint-dependent suppression of replication initiation. Thus, our results demonstrate the effectiveness of E/L Repli-seq at identifying cells with replication-stress-induced altered DNA replication programs.
2021-09-30 | GSE167045 | GEO
Project description:LSD1 Facilitates Euchromatic Origin Firing and Regulates Replication Timing [Repli-seq]
| PRJNA661444 | ENA
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Richard Sandstrom mailto:sull@u.washington.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track is produced as part of the ENCODE Project. This track shows genome-wide assessment of DNA replication timing in cell lines using the sequencing-based "Repli-seq" methodology (see below). Replication timing is known to be an important feature for epigenetic control of gene expression that usually operates at a higher-order level than at the level of specific genes. For each experiment (cell line, replicate), replication timing was ascertained by the isolation and sequencing of newly replicated DNA from six cell cycle fractions: G1/G1b, S1, S2, S3, S4, G2 (six fraction profile). Replication patterns are visualized as a continuous function based on sequencing tag density (Percentage-normalized Signal) and as a wavelet-smoothed transform of the six fraction profile (Wavelet-smoothed Signal). Replication peaks corresponding to replication initiation zones (Peaks) and valleys corresponding to replication termination zones (Valleys) were determined from local maxima and minima, respectively, in the wavelet-smoothed signal data. A measure of relative copy number at each genomic location (Summed Densities) was determined by summing the normalized tag density values of each cell cycle fraction at that location (equals one replicated genome equivalent). For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf
2012-04-27 | GSE34399 | GEO