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569 results for “DNA replication”
Clonal decomposition and DNA replication states defined by scaled single cell genome sequencing
<p><strong>OV2295 Tables</strong></p> <p>ov2295_breakpoint_counts.csv.gz: Table of breakpoint counts per cell</p> <ul> <li>prediction_id: identifier for the breakpoint</li> <li>cell_id: identifier for the cell</li> <li>read_count: number of reads</li> <li>library_id: identifier for the DNA library</li> <li>sample_id: identifier for the sequenced sample</li> <li>chromosome_1: chromosome of breakend 1</li> <li>strand_1: orientation of break end 1</li> <li>position_1: position of break end 1</li> <li>chromosome_2: chromosome of breakend 2</li> <li>strand_2: orientation of break end 2</li> <li>position_2: position of break end 2</li> </ul> <p>ov2295_cell_cn.csv.gz: Table of cell specific copy number</p> <ul> <li>cell_id: identifier for the cell</li> <li>sample_id: identifier for the sequenced sample</li> <li>library_id: identifier for the DNA library</li> <li>chr: chromosome of bin</li> <li>start: start of bin</li> <li>end: end of bin</li> <li>reads: number of reads</li> <li>copy: raw normalized copy number</li> <li>state: copy number state</li> <li>gc: percent gc of the bin</li> <li>map: average mappability of the bin</li> </ul> <p>ov2295_cell_metrics.csv.gz: Table of cell metrics</p> <ul> <li>cell_id: identifier of the cell</li> <li>unpaired_mapped_reads: number of unpaired mapped reads</li> <li>paired_mapped_reads: number of mapped reads that were properly paired</li> <li>unpaired_duplicate_reads: number of unpaired duplicated reads</li> <li>paired_duplicate_reads: number of paired reads that were also marked as duplicate</li> <li>unmapped_reads: number of unmapped reads</li> <li>percent_duplicate_reads: percentage of duplicate reads</li> <li>estimated_library_size: scaled total number of mapped reads</li> <li>total_reads: total number of reads, regardless of mapping status</li> <li>total_mapped_reads: total number of mapped reads</li> <li>total_duplicate_reads: number of duplicate reads</li> <li>total_properly_paired: number of properly paired reads</li> <li>coverage_breadth: percentage of genome covered by some read</li> <li>coverage_depth: average reads per nucleotide position in the genome</li> <li>median_insert_size: median insert size between paired reads</li> <li>mean_insert_size: mean insert size between paired reads</li> <li>standard_deviation_insert_size: standard deviation of the insert size between paired reads</li> <li>index_sequence: index sequence of the adaptor sequence</li> <li>column: column of the cell on the nanowell chip</li> <li>img_col: column of the cell from the perspective of the microscope</li> <li>index_i5: id of the i5 index adapter sequence</li> <li>sample_type: type of the sample</li> <li>primer_i7: id of the i5 index primer sequence</li> <li>experimental_condition: experimental treatment of the cell, includes controls</li> <li>index_i7: id of the i7 index adapter sequence</li> <li>cell_call: living/dead classification of the cell based on staining usually, C1 == living, C2 == dead</li> <li>sample_id: name of the sample</li> <li>primer_i5: id of the i5 index primer sequence</li> <li>row: row of the cell on the nanowell chip</li> <li>library_id: identifier for the DNA library</li> <li>index: ignored</li> <li>multiplier: during parameter searching, the set [1..6] that was chosen</li> <li>MSRSI_non_integerness: median of segment residuals from segment integer copy number states</li> <li>MBRSI_dispersion_non_integerness: median of bin residuals from segment integer copy number states</li> <li>MBRSM_dispersion: median of bin residuals from segment median copy number values</li> <li>autocorrelation_hmmcopy: hmmcopy copy autocorrelation</li> <li>cv_hmmcopy: ignored</li> <li>empty_bins_hmmcopy: number of empty bins in hmmcopy</li> <li>mad_hmmcopy: median absolute deviation of hmmcopy copy</li> <li>mean_hmmcopy_reads_per_bin: mean reads per hmmcopy bin</li> <li>median_hmmcopy_reads_per_bin: median reads per hmmcopy bin</li> <li>std_hmmcopy_reads_per_bin: standard deviation value of reads in hmmcopy bins</li> <li>total_halfiness: summed halfiness penality score of the cell</li> <li>total_mapped_reads_hmmcopy: total mapped reads in all hmmcopy bins</li> <li>scaled_halfiness: summed scaled halfiness penalty score of the cell</li> <li>mean_state_mads: mean value for all median absolute deviation scores for each state</li> <li>mean_state_vars: variance value for all median absolute deviation scores for each state</li> <li>mad_neutral_state: median absolute deviation score of the neutral 2 copy state</li> <li>breakpoints: number of breakpoints, as indicated by state changes not at the ends of chromosomes</li> <li>mean_copy: mean hmmcopy copy value</li> <li>state_mode: the most commonly occuring state</li> <li>log_likelihood: hmmcopy log likelihood for the cell</li> <li>true_multiplier: the exact decimal value used to scale the copy number for segmentation</li> <li>order: order of the cell in the hierarchical clustering tree</li> <li>quality: random forest classifier proability score that cell is good</li> </ul> <p>ov2295_clone_alleles.csv.gz: Table of clone specific allele data</p> <ul> <li>chr: chromosome of bin</li> <li>start: start of bin</li> <li>end: end of bin</li> <li>hap_label: haplotype block identifier</li> <li>clone_id: clone identifier</li> <li>allele_1_sum: number of reads for allele 1 of the haplotype block</li> <li>allele_2_sum: number of reads for allele 2 of the haplotype block</li> <li>total_counts_sum: total reads for the haplotype block</li> </ul> <p>ov2295_clone_breakpoints.csv.gz: Table of breakpoints per clone for OV2295 samples. Columns:</p> <ul> <li>prediction_id: identifier for the breakpoint</li> <li>chromosome_1: chromosome of breakend 1</li> <li>strand_1: orientation of break end 1</li> <li>position_1: position of break end 1</li> <li>chromosome_2: chromosome of breakend 2</li> <li>strand_2: orientation of break end 2</li> <li>position_2: position of break end 2</li> <li>clone_id: clone identifier</li> <li>read_count: number of reads</li> <li>is_present: presence=1, absent=0</li> </ul> <p>ov2295_clone_clusters.csv.gz: Table of cell clusters as putative clones</p> <ul> <li>cell_id: identifier for the cell</li> <li>clone_id: clone identifier</li> </ul> <p>ov2295_clone_cn.csv.gz: Table of allele specific copy number per clone for OV2295 samples. Columns:</p> <ul> <li>chr: chromosome of bin</li> <li>start: start of bin</li> <li>end: end of bin</li> <li>total_cn: HMMCopy predicted total copy number </li> <li>minor_cn: HMM predicted minor copy number </li> <li>major_cn: HMM predicted major copy number </li> <li>clone_id: clone identifier</li> </ul> <p>ov2295_clone_snvs.csv.gz: Table of SNVs per clone for OV2295 samples. Columns:</p> <ul> <li>chrom: chromosome</li> <li>coord: genome position</li> <li>ref: reference nucleotide</li> <li>alt: alternate nucleotide</li> <li>clone_id: clone identifier</li> <li>ref_counts: number of reads at this position matching the reference nucleotide</li> <li>alt_counts: number of reads at this position matching the alternate nucleotide</li> <li>total_counts: total number of reads at this position</li> <li>is_present: presence=0, absent=1</li> <li>is_het: is heterozygous</li> <li>is_hom: is homozygous for the alternate</li> </ul> <p>ov2295_nodes.csv.gz: Table of phylogenetic information for SNV evolution</p> <ul> <li>variant_id: identifier for the SNV as chrom:coord:ref:alt</li> <li>node: node in the phylogenetic tree</li> <li>loss: probability the SNV was lost at this node</li> <li>origin: probability the SNV originated at this node</li> <li>presence: probability the SNV is present at this node</li> <li>ml_origin: binary indicator the SNV originated at this node</li> <li>ml_presence: binary indicator the SNV is present at this node</li> <li>ml_loss: binary indicator the SNV was lost at this node</li> </ul> <p>ov2295_snv_counts.csv.gz: Table of SNV counts</p> <ul> <li>chrom: chromosome</li> <li>coord: genome position</li> <li>ref: reference nucleotide</li> <li>alt: alternate nucleotide</li> <li>ref_counts: number of reads at this position matching the reference nucleotide</li> <li>alt_counts: number of reads at this position matching the alternate nucleotide</li> <li>cell_id: identifier for the cell</li> <li>total_counts: total number of reads at this position</li> <li>sample_id: identifier for the sequenced sample</li> </ul> <p>ov2295_tree.pickle: Phylogenetic tree in python pickle format. Requires installation of the stochastic dollo code at: https://bitbucket.org/dranew/dollo, version 0.4.2.</p> <p>Note the following sample mapping: ‘SA922’: ‘OV2295(R2)’, ‘SA921’: ‘TOV2295(R)’, ‘SA1090’: ‘OV2295’,</p> <p><strong>Plots</strong></p> <p>ov_supp_clone_allele_cn.png: Clone allele ratios for each OV2295 sample.</p> <p>ov_supp_clone_total_cn.png: Clone copy number for each OV2295 sample.</p> <p>ov_supp_sample_total_cn.png: Bulk copy number for each OV2295 sample.</p> <p>ov_supp_sample_allele_cn.png: Bulk allele ratios for each OV2295 sample.</p>
PRIMPOL ensures robust handoff between on-the-fly and post-replicative DNA lesion bypass.
<p><strong>Supplementary Table 6. CRISPR screen raw sgRNA counts.</strong></p><p>Excel sheet 'Mellor et al Table S6_sgRNA_count.xlsx'.</p><p>Output of the MAGeCK count command aligning Illumina sequencing reads to the sgRNA sequences within the Human Improved Genome-wide Knockout CRISPR library, following CRISPR/Cas9 screens in WT and <i>primpol</i> TK6 cells.</p><p> </p><p><strong>Supplementary Table 7. CRISPR screen untreated summary.</strong></p><p>Excel sheet 'Mellor et al Table S7_untreated_summary.xlsx'.</p><p>Output of the MAGeCK test command comparing sgRNA abundance between the sequencing libraries produced following a CRISPR/Cas9 knockout screen in WT versus <i>primpol</i> TK6 cells in untreated conditions.</p><p> </p><p><strong>Supplementary Table 8. CRISPR screen cisplatin treated summary.</strong></p><p>Excel sheet 'Mellor et al Table S8_cddp_treated_summary.xlsx'.</p><p>Output of the MAGeCK test command comparing sgRNA abundance between the sequencing libraries produced following a CRISPR/Cas9 knockout screen in WT versus <i>primpol</i> TK6 cells challenged with 7 days continuous 0.25 μM cisplatin treatment.</p><p> </p><p><strong>readme.xlsx</strong></p><p>Tab 1: List of files</p><p>Tab 2: Summary of CRISPR screen samples & conditions</p><p> </p><p><strong>Raw sequencing data (zipped fastq files)</strong></p><p>WT_un_1.fastq.gz Wild type untreated replicate 1<br>WT_un_2.fastq.gz Wild type untreated replicate 2<br>WT_cis_1.fastq.gz Wild type cisplatin-treated replicate 1<br>WT_cis_2.fastq.gz Wild type cisplatin-treated replicate 2<br>Pp_un_1.fastq.gz <i>primpol </i>untreated replicate 1<br>Pp_un_2.fastq.gz <i>primpol </i>untreated replicate 2<br>Pp_cis_1.fastq.gz <i>primpol </i>cisplatin-treated replicate 1<br>Pp_cis_2.fastq.gz <i>primpol </i>cisplatin-treated replicate 2</p><p> </p>
Data from: Metabarcoding of soil environmental DNA replicates plant community variation but not specificity
<blockquote> <p>While metabarcoding of plant DNA from their environment is an exciting method that can supplement inventorying of live plant species, the accuracy and specificity has yet to be fully assessed over complex continuous landscapes. In this work, we evaluate plant community profiles produced via metabarcoding of soil by comparing them to a morphological survey. We assessed plant communities by metabarcoding of soil DNA in 130 sites along ecological gradients (nutrients, succession, moisture) in Denmark using chloroplast <i>trn</i>L region (10-143 bp) primer set and compared the resulting communities to communities produced with a longer nuclear ITS2 region (~216 bp) and a morphological survey. We found that the community variation observed within the morphological survey was well represented by molecular surveys, with significant correlation with both community composition and richness using both primer sets. While the majority of the ITS2 sequences could be assigned to species (over 80%), we had less success with the <i>trn</i>L sequences (70%), which was only possible after restricting the reference database to local species. We conclude that the community profiles produced by metabarcoding can be highly effective in performing large-scale macroecological studies. However, the discovery rates and taxonomic assignments produced via metabarcoding remained inferior to morphological surveys, but manual curation of databases improves the <i>specificity</i> of assignments made by the <i>trn</i>L primers, and improves the <i>accuracy</i> of the assignments made with the ITS2 primers. Finally, we suggest that a greater percentage of named diversity would be recovered by increasing soil sampling with the use of additional universal primer sets.</p> </blockquote>
Nanotiming: single-molecule based, telomere-to-telomere DNA replication timing profiling by nanopore sequencing
<p>Dataset for the manuscript "Nanotiming: telomere-to-telomere DNA replication timing profiling by nanopore sequencing" by Theulot et al ,2024 (<span>https://doi.org/10.1038/s41467-024-55520-3</span>) related to the github repository (https://github.com/LacroixLaurent/NanoTiming)</p> <ul> <li>WT_rep3.tar.gz contains fast5 file from an experiment where yeast BT1 strain was grown for one doubling time with 5µM BrdU then DNA was sequenced on R9.4.1 ONT flowcell</li> <li>mod_mapping.bam contains the bam file resulting from the BrdU base calling with megalodon (v2.2.9) using our BT1 reference genome and our BrdU aware model for base-calling</li> <li>WT_rep3_nanoT.bed.gz contains the reads coordinates from the mod_mappings file</li> <li>WT_rep3_nanoT_alldata.rds contains the BrdU profiles for each reads of the mod_mappings file, with the BrdU signal binned in 1kb non overlaping windows</li> <li>WT_rep3_nanoT.rds contains the genomic BrdU signal profiles by 1kb non overlaping windows</li> <li>TeloLengthDataNanoT.rds contains all the telomeric sequences extracted from the experiments reported in the Figure 4 and S19 to S23 of the manuscript with the associated filtering information and nanotiming signal.</li> </ul> <p> </p>
Processed Hi-C contact matrices for "Single-cell DNA replication profiling identifies spatiotemporal developmental dynamics of chromosome organization"
<p>Processed Hi-C interaction matrices (iterative correction) saved in .hic format (40kb bins).</p> <p>.hic files were generated by juicer pipeline using processed Hi-C interaction matrices.</p> <p>Only <em>cis </em>interactions were available.</p> <p>To extract the data, please see </p> <p>https://github.com/aidenlab/juicer/wiki/Data-Extraction</p>
Escherichia coli DNA replication study: processed alignment data
<p>Genomes are replicated by large protein complexes called replisomes. In bacterial DNA replication, two replisomes replicate the DNA starting from the same origin site and proceeding in opposite directions. Understanding their movement in vivo has been challenging. We used quantitative genome sequencing to characterize the dynamics of bacterial replisomes at 5 different temperatures (17, 22, 27, 32 and 37 °C) in exponential growth (3 replicates) or in stationary phase (one experiment at 17, 27 and 37 °C).</p> <p>The data deposited here give the coordinates of the sequence reads (deposited under the BioProject PRJNA772106) covering the Escherichia coli str. K-12 substr. MG1655 complete genome (accession number U00096.3).</p> <p>The file archive contains data files for each sample, at nucleotide resolution and binned in intervals of 10,000 base pairs. It also contains a C program to perform the binning and a README summarising how the alignment was done. <em>Please note that once uncompressed, the data will take 5 Gb of disks space in total.</em></p>
Data from: Metabarcoding of soil environmental DNA replicates plant community variation but not specificity
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Cell-cycle dependent DNA repair and replication unifies patterns of chromosome instability
<p>This repository contains the data used to generate the figures in paper: Cell-cycle dependent DNA repair and replication unifies patterns of chromosome instability.</p>
DNA replication dynamics during erythrocytic schizogony in the malaria parasites Plasmodium falciparum and Plasmodium knowlesi
<p>Malaria parasites are unusual, early-diverging protozoans with non-canonical cell cycles. They do not undergo binary fission, but divide primarily by schizogony. This is a mode of replication involving asynchronous production of multiple nuclei within the same cytoplasm, culminating in a single mass cytokinesis event. The rate and efficiency of parasite replication is fundamentally important to malarial disease, which tends to be severe in hosts with high parasite loads. Here, we have studied for the first time the dynamics of schizogony in two human malaria parasite species, <em>Plasmodium falciparum</em> and <em>Plasmodium knowlesi</em>. These differ in their cell-cycle length, the number of progeny produced and the genome composition, among other factors. Comparing them could therefore yield new information about the parameters and limitations of schizogony. We report that the dynamics of schizogony differ significantly between these two species, most strikingly in the gap phases between successive nuclear replications, which are longer in <em>P. falciparum</em> and shorter, but more heterogenous, in <em>P. knowlesi</em>. In both species, gaps become longer as schizogony progresses, whereas each period of active replication grows shorter. In both species there is also extreme variability between individual cells, with some schizonts producing many more nuclei than others, and some individual nuclei arresting their replication for many hours while adjacent nuclei continue to replicate. The efficiency of schizogony is probably influenced by a complex set of factors in both the parasite and its host cell.</p>
Microscopy data from: Identification of genetic interactions with priB links the PriA/PriB DNA replication restart pathway to double-strand DNA break repair in Escherichia coli
<p>Collisions between DNA replication complexes (replisomes) and impediments such as damaged DNA or proteins tightly bound to the chromosome lead to premature dissociation of replisomes at least once per cell cycle in <em>Escherichia coli</em>. Left unrepaired, these events produce incompletely replicated chromosomes that cannot be properly partitioned into daughter cells. DNA replication restart, the process that reloads replisomes at prematurely terminated sites, is therefore essential in <em>E. coli</em> and other bacteria. Three replication restart pathways have been identified in <em>E. coli</em>: PriA/PriB, PriA/PriC, and PriC/Rep. A limited number of genetic interactions between replication restart and other genome maintenance pathways have been defined, but a systematic study placing replication restart reactions in a broader cellular context has not been performed. We have utilized transposon insertion sequencing to identify new genetic interactions between DNA replication restart pathways and other cellular systems. Known genetic interactors with the <em>priB</em> replication restart gene (uniquely involved in the PriA/PriB pathway) were confirmed and several novel <em>priB </em>interactions were discovered. Far fewer connections were found with the PriA/PriC or PriC/Rep pathways, suggesting a primacy role for the PriA/PriB pathway in <em>E. coli</em>. Targeted genetic and imaging-based experiments with <em>priB</em> and its genetic partners revealed significant double-strand DNA break (DSB) accumulation in strains with mutations in <em>dam</em>, <em>rep</em>, <em>rdgC</em>, <em>lexA</em>, or <em>polA</em>. Modulating the activity of the RecA recombinase partially suppressed the detrimental effects of <em>rdgC</em> or <em>lexA</em> mutations in Δ<em>priB</em> cells. Taken together, our results highlight roles for several genes in DSB homeostasis and define a genetic network that facilitates DNA repair/processing upstream of PriA/PriB-mediated DNA replication restart in <em>E. coli</em>.</p>
Data set for the replication package of the paper "Simulations of DNA-origami self-assembly reveal design-dependent nucleation barriers"
<p>Data set for the replication package of the paper "Simulations of DNA-origami self-assembly reveal design-dependent nucleation barriers".</p>
Replication stalling activates SSB for recruitment of DNA damage tolerance factors
<p>This repository contains four zipped folders with data and code related to this manuscript in the form of MATLAB files. The folders contain:</p> <ol> <li>By figure: plotted data organized by figure in the manuscript (as MATLAB workspaces, .mat files).</li> <li>Variables: plotted data organized by strain number and description (as MATLAB workspaces, .mat files).</li> <li>Functions: MATLAB functions needed to 1) integrate MicrobeTracker(1) cell segmentation analysis with u-track point source detection(2) and tracking(3) analysis and 2) perform subsequent processing of u-track analysis results (as MATLAB scripts, .m files)</li> <li>Scripts: MATLAB scripts needed to perform different types of data analysis (as MATLAB scripts, .m files). These scripts are organized by integration time (short vs. long exposure) and by type of analysis (radial distribution functional analysis, diffusion coefficient analysis, etc)</li> </ol>
Mitigating transcription-replication conflicts: In early Drosophila embryos, rapid onset of transcription after mitosis depends on DNA replication
<p>This dataset includes the raw imaging data (exported from Volocity as TIF in Z-stacks or maximal projections) related to the publication: Mitigating transcription-replication conflicts: In early Drosophila embryos, rapid onset of transcription after mitosis depends on DNA replication, Cell Reports 2022.</p>
RepliChrom: Interpretable machine learning predicts cancer-associated enhancer-promoter interactions using DNA replication timing
<p>This dataset accompanies the study "RepliChrom: Interpretable machine learning predicts cancer-associated enhancer-promoter interactions using DNA replication timing". The study introduces RepliChrom, a computational framework designed to predict enhancer–promoter interactions (EPIs) by leveraging multi-scale replication timing (RT) signals. This approach addresses the fundamental challenge of distinguishing gene targets regulated by distal enhancers from those activated by proximal transcriptional activity-a key problem in understanding the causal basis of complex diseases.</p> <p>Despite recent advances in high-throughput technologies such as Hi-C, ChIA-PET, and Hi-TrAC that allow genome-wide reconstruction of 3D chromatin architecture, the role of DNA replication timing in mediating these spatial interactions remains underexplored. RepliChrom fills this gap by using cell-type-specific RT profiles as predictive features for chromatin interaction inference.</p> <p>To support model development, training, and evaluation, we provide a comprehensive multi-omics dataset covering six human cell lines (K562, GM12878, HeLaS3, IMR90, NHEK, and HUVEC ), encompassing:</p> <p>Hi-C datasets: Processed chromatin interaction loops used to define positive and negative enhancer–promoter interaction pairs.</p> <p>ChIA-PET datasets: Interaction data anchored around transcription factor binding, including POLR2A and CTCF, used for model validation across different interaction types.</p> <p>Hi-TrAC datasets: Targeted chromatin accessibility-derived interaction data, offering complementary validation of the model on alternate platforms.</p> <p>Replication Timing (RT) data: Processed RT signal profiles for each cell line, used to extract multi-scale temporal features as inputs for RepliChrom.</p> <p>These datasets enable reproducibility of the model training process and serve as benchmark resources for future research into DNA replication–mediated regulation of 3D genome architecture.</p> <p><strong>Included Files</strong></p> <p>Hi-C_datasets.zip (2.23 MB): Processed Hi-C interaction pairs for six cell types.</p> <p>ChIA-PET_datasets.zip (818.18 KB): CTCF and POLR2A ChIA-PET interactions across multiple lines.</p> <p>Hi-TrAC_datasets.zip (196 bytes): Hi-TrAC-based chromatin interaction training datasets across multiple lines.</p> <p>Cellline_RT_data.zip (86.17 MB): Replication timing signal data across multiple human cell types for multi-scale replication timing feature extraction.</p> <p><strong>Usage</strong><br>All datasets are intended for academic, non-commercial use. The provided files can be directly used to reproduce the training and evaluation of RepliChrom, and may also support broader applications in enhancer–promoter modeling, replication-timing analysis, and 3D genomics studies. For detailed usage instructions and code implementation, please refer to the GitHub repository: https://github.com/DaoFuying/RepliChrom</p>
Data from: Multispecies site occupancy modeling and study design for spatially replicated environmental DNA metabarcoding
<p>Although environmental DNA (eDNA) metabarcoding has become widely applied to gauge ecosystems in a noninvasive and cost-efficient manner, false negatives can occur due to various factors in its inherent multistage workflow. It is therefore essential to deal with this kind of species detection errors in eDNA metabarcoding to achieve accurate assessment of species distribution and diversity. To address this issue, we proposed a variant of the multispecies site occupancy model for eDNA metabarcoding studies and applied it to an eDNA metabarcoding dataset of freshwater fish communities collected in the Kasumigaura watershed in Japan.</p> <ul> </ul>
Data from: Multispecies site occupancy modeling and study design for spatially replicated environmental DNA metabarcoding
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Microscopy data from: Identification of genetic interactions with priB links the PriA/PriB DNA replication restart pathway to double-strand DNA break repair in Escherichia coli
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Data from: Extrusion-modulated DnaA activity oscillations coordinate DNA replication with biomass growth
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DNA replication dynamics during erythrocytic schizogony in the malaria parasites Plasmodium falciparum and Plasmodium knowlesi
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Data from: The function and evolution of motile DNA replication systems in ciliates
<p>DNA replication is a ubiquitous, complex, and conserved cellular process. However, regulation of DNA replication is only understood in a small fraction of organisms that poorly represent the diversity of genetic systems in nature. Here we used a combination of computational and experimental approaches to examine the function and evolution of one such system, the replication band (RB) in spirotrich ciliates, which is a localized, motile hub that traverses the macronucleus while replicating DNA. We show that the RB can take unique physical forms in different species, ranging from polar bands to a "replication envelope", where replication initiates at the nuclear periphery and advances centripetally inwards. Furthermore, we identified genes involved in cellular transport, including calcium transporters and cytoskeletal regulators, that are associated with the RB and may be involved in its function and translocation. These findings reveal the complex evolution and diversity of motile DNA replication systems and raise new possibilities regarding the regulation of nuclear organization and processes.</p>
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