Mus musculus
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ABSTRACT:
PROVIDER: PRJNA311084 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:4C-Seq has proven to be a powerful technique to identify genome-wide interactions with a single locus of interest (or "bait") that can be important for gene regulation. However, analysis of 4C-Seq data is complicated by the many biases inherent to the technique. An important consideration when dealing with 4C-Seq data is the differences in resolution of signal across the genome that result from differences in 3D distance separation from the bait. This leads to the highest signal in the region immediately surrounding the bait and increasingly lower signals in far-cis and trans. Another important aspect of 4C-Seq experiments is the resolution, which is greatly influenced by the choice of restriction enzyme and the frequency at which it can cut the genome. Thus, it is important that a 4C-Seq analysis method is flexible enough to analyze data generated using different enzymes and to identify interactions across the entire genome. Current methods for 4C-Seq analysis only identify interactions in regions near the bait or in regions located in far-cis and trans, but no method comprehensively analyzes 4C signals of different length scales. In addition, some methods also fail in experiments where chromatin fragments are generated using frequent cutter restriction enzymes. Here, we describe 4C-ker, a Hidden-Markov Model based pipeline that identifies regions throughout the genome that interact with the 4C bait locus. In addition, we incorporate methods for the identification of differential interactions in multiple 4C-seq datasets collected from different genotypes or experimental conditions. Adaptive window sizes are used to correct for differences in signal coverage in near-bait regions, far-cis and trans chromosomes. Using several datasets, we demonstrate that 4C-ker outperforms all existing 4C-Seq pipelines in its ability to reproducibly identify interaction domains at all genomic ranges with different resolution enzymes. 4C-Seq experiments from Igh and Cd83 bait in activated B cells and Tcrb (Eb) bait in double negative T cells and immature B cells. RNA-Seq and ATAC-Seq experiments in DN and Immature B cells.
2016-02-27 | E-GEOD-77645 | biostudies-arrayexpress
Project description:4C-Seq has proven to be a powerful technique to identify genome-wide interactions with a single locus of interest (or "bait") that can be important for gene regulation. However, analysis of 4C-Seq data is complicated by the many biases inherent to the technique. An important consideration when dealing with 4C-Seq data is the differences in resolution of signal across the genome that result from differences in 3D distance separation from the bait. This leads to the highest signal in the region immediately surrounding the bait and increasingly lower signals in far-cis and trans. Another important aspect of 4C-Seq experiments is the resolution, which is greatly influenced by the choice of restriction enzyme and the frequency at which it can cut the genome. Thus, it is important that a 4C-Seq analysis method is flexible enough to analyze data generated using different enzymes and to identify interactions across the entire genome. Current methods for 4C-Seq analysis only identify interactions in regions near the bait or in regions located in far-cis and trans, but no method comprehensively analyzes 4C signals of different length scales. In addition, some methods also fail in experiments where chromatin fragments are generated using frequent cutter restriction enzymes. Here, we describe 4C-ker, a Hidden-Markov Model based pipeline that identifies regions throughout the genome that interact with the 4C bait locus. In addition, we incorporate methods for the identification of differential interactions in multiple 4C-seq datasets collected from different genotypes or experimental conditions. Adaptive window sizes are used to correct for differences in signal coverage in near-bait regions, far-cis and trans chromosomes. Using several datasets, we demonstrate that 4C-ker outperforms all existing 4C-Seq pipelines in its ability to reproducibly identify interaction domains at all genomic ranges with different resolution enzymes.
2016-02-27 | GSE77645 | GEO
Project description:The ability to correlate chromosome conformation and gene expression gives a great deal of information regarding the strategies used by a cell to properly regulate gene activity. 4C-seq is a relatively new and increasingly popular technology where the set of genomic interactions generated by a single point in the genome can be determined. 4C-seq experiments generate large, complicated datasets and it is imperative that signal is properly distinguished from noise. Currently there are a limited number of methods for analyzing 4C-seq data. Here, we present a new method, fourSig, which, in addition to being simple to use and as precise as current methods, also includes a new feature to prioritize significantly enriched interactions and predict their reproducibility among experimental replicates. Here, we demonstrate the efficacy of fourSig with previously published and novel 4C-seq datasets and show that our significance prioritization correlates with the ability to reproducibly detect interactions amongst replicates. The datasets provided include those generated from allele-specific 4C-Seq with a viewpoint of the TSS for the gene Ibtk on mouse Chromosome 9. FASTQ files, text files containing genomic coordiantes and read counts, and bedGraph formats for UCSC Genome Browser tracks are provided. All sequences were mapped relative to mouse genome build mm9.
2014-02-22 | GSE50907 | GEO
Project description:The ability to correlate chromosome conformation and gene expression gives a great deal of information regarding the strategies used by a cell to properly regulate gene activity. 4C-seq is a relatively new and increasingly popular technology where the set of genomic interactions generated by a single point in the genome can be determined. 4C-seq experiments generate large, complicated datasets and it is imperative that signal is properly distinguished from noise. Currently there are a limited number of methods for analyzing 4C-seq data. Here, we present a new method, fourSig, which, in addition to being simple to use and as precise as current methods, also includes a new feature to prioritize significantly enriched interactions and predict their reproducibility among experimental replicates. Here, we demonstrate the efficacy of fourSig with previously published and novel 4C-seq datasets and show that our significance prioritization correlates with the ability to reproducibly detect interactions amongst replicates. The datasets provided include those generated from allele-specific 4C-Seq with a viewpoint of the TSS for the gene Ibtk on mouse Chromosome 9. FASTQ files, text files containing genomic coordiantes and read counts, and bedGraph formats for UCSC Genome Browser tracks are provided. All sequences were mapped relative to mouse genome build mm9. Sequencing of circular chromosome conformation capture (4C-Seq) was performed at the transcription start site (TSS) for the gene Ibtk for three replicates in F1 hybrid mouse trophoblast stem (TS) cells. Experiment was designed to detect allele specific patterns using SNP differences between the inbred lines mated to produce the TS cells (C57Bl/6 and CAST/EiJ)
2014-02-22 | E-GEOD-50907 | biostudies-arrayexpress
Project description:fourSig: A Method for Determining Chromosomal Interactions in 4C-Seq Data
| PRJNA219332 | ENA
Project description:There is evidence for the on-going recurrent transfer of mitochondrial DNA (mtDNA) into the nucleus in both germ line and somatic cells. However, the outcomes associated with the transfer of mtDNA into somatic cell nuclei are poorly understood. High-resolution Chromosome Conformation Capture (HiC) techniques, which are used to identify global patterns of chromatin interactions, regularly capture physical interactions between mitochondrial and nuclear DNA (i.e. mito-nDNA interactions) in mammalian cells. These mito-nDNA interactions are routinely considered a consequence of nonspecific ligation events during chromatin library preparation. Here, we have evaluated mito-nDNA interactions captured by HiC in six human cell lines, and by Circular Chromosome Conformation Capture (4C) in mouse cortical astrocytes. We show that mito-nDNA interactions are statistically significant and shared between biological and technical replicates in the HiC and 4C experiments. The most frequent interactions between mtDNA and nuclear loci in the HiC and 4C data occur with repetitive DNA sequences including the centromeric regions in the six human cell lines and 18S rDNA in mouse cortical astrocytes. Such findings confirm previous observations of mtDNA forming interactions with rDNA genes in budding yeast and centomeres in rat bone marrow cells. Finally the mitochondrial D-loop tends to be enriched in the captured mito-nDNA interactions. Collectively our results imply a degree of selective regulation in the identity of the interacting mitochondrial partners confirming that mito-nDNA interactions in mammalian cells are not random.
2017-11-21 | GSE77610 | GEO
Project description:ChEC-Seq: a robust method to identify protein-DNA interactions genome-wide
| PRJNA552083 | ENA
Project description:Here, we present Methyltransferase Targeting-based chromosome Architecture Capture (MTAC), a method that maps the contacts between a target site (viewpoint) and the rest of the genome with high resolution and sensitivity. By targeting M.CviPI DNA methyltransferase to the viewpoint and by detecting differentially methylated regions, MTAC detects hundreds of intra- and inter-chromosomal interactions in the budding yeast genome that cannot be captured by 4C, Hi-C, or Micro-C.
2023-09-05 | GSE242399 | GEO
Project description:Here, we present Methyltransferase Targeting-based chromosome Architecture Capture (MTAC), a method that maps the contacts between a target site (viewpoint) and the rest of the genome with high resolution and sensitivity. By targeting M.CviPI DNA methyltransferase to the viewpoint and by detecting differentially methylated regions, MTAC detects hundreds of intra- and inter-chromosomal interactions in the budding yeast genome that cannot be captured by 4C, Hi-C, or Micro-C.
2023-09-05 | GSE242395 | GEO
Project description:Here, we present Methyltransferase Targeting-based chromosome Architecture Capture (MTAC), a method that maps the contacts between a target site (viewpoint) and the rest of the genome with high resolution and sensitivity. By targeting M.CviPI DNA methyltransferase to the viewpoint and by detecting differentially methylated regions, MTAC detects hundreds of intra- and inter-chromosomal interactions in the budding yeast genome that cannot be captured by 4C, Hi-C, or Micro-C.
2023-09-05 | GSE242394 | GEO