Ontology highlight
ABSTRACT:
PROVIDER: PRJNA911442 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR22702300_subreads.fastq.gz | Fastqsanger.gz | |||
| SRR22702301_subreads.fastq.gz | Fastqsanger.gz | |||
| SRR22702302_subreads.fastq.gz | Fastqsanger.gz | |||
| SRR22702303_subreads.fastq.gz | Fastqsanger.gz | |||
| SRR22702304_subreads.fastq.gz | Fastqsanger.gz |
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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.
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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.
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Project description:CD34+ heamatopoietic stem cells were isolated from the bone marrow of two healthy donors undergoing total hip replacement. Promoter capture Hi-C (PCHi-C) was performed on these cells using the protocol according to Mifsud et al. 2015, with the exception that ligation was performed in situ, and a slightly modified bait capture set was used. Bait positions in hg19 are included as an additional file.
2021-10-15 | E-MTAB-10701 | biostudies-arrayexpress
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Project description:Aim: We aim to compare current (MeDIP-seq), new (Illumina Infinium 450K BeadChip) and future (PacBio) methods for whole genome DNA methylation analysis. As the interest in determination of disease methylation profiles increases, the scope, advantages and limitations of these methods requires assessment. There are key questions to answer and specific challenges to overcome. For example, how much detail/resolution is sufficient to identify regions of differential methylation and regions of biological/medical significance within a sample? How much coverage of the genome is required for accurate methylation analysis? Is it important to confirm which regions of the genome are unmethylated in addition to focusing on those that are methylated? Loss of methylation may be of equal importance within the cell since this may also contribute to disease pathogenesis. A multi-method (affinity enrichment/bisulphite-conversion based/direct sequencing of methyl-cytosine) and technology platform (Illumina HiSeq/PacBio/Illumina Infinium BeadChip) comparison will enable us to determine the strengths and weakness of each method. We propose to compare four methods using two DNA samples from the Coriell Institute for Cell Repository to assess both current and future capabilities for whole genome methylation analysis in parallel: A) MeDIP-seq using Illumina HiSeq B) Illumina Infinium HumanMethylation 450K BeadChip and C) whole genome methylation sequencing using PacBio. Existing single molecule deep bisulphite sequencing data generated previously from these same samples at the WTSI for targeted regions (30-40 genes) on the human X chromosome will be used to assess performance of each method. The methods selected for this study will generate data covering a range of resolutions from a whole genome scan to array (target defined) resolution and up to single base pair, single molecule resolution; the highest level of detail possible with methods currently available.Samples: DNA from sibling pair GM01240 (female) and GM01240 (male).Requirements: Both samples will be analysed using;A.MeDIP-seq using Illumina HiSeq (one HiSeq lane, 75bp paired end, per sample) B.Illumina Infinium HumanMethylation 450K BeadChipWe are expecting a potentially unnecessary high coverage using one HiSeq lane per sample. However, for the MeDIP procedure we do not have a multiplexing procedure in place. Our requirements for PacBio sequencing have been discussed with and will be supported by the Sequencing Technology Development group.
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Project description:The development of affinity purification technologies together with mass spectrometric analyses of the purified protein mixtures (AP-MS) has been used both to identify new protein-protein interactions and to define the subunit composition of protein complexes. Transcription factor protein interactions, however, have not been systematically analyzed using these approaches. Here, we have investigated whether ectopic expression of an affinity tagged transcription factor as bait in AP-MS experiments perturbs gene expression in cells resulting in false positive identification of bait associated proteins when typical experimental controls are used. Using quantitative proteomics and RNA-Seq, we determined that the increase in the abundance of a set of proteins caused by overexpression of the transcription factor RelA is not sufficient for these proteins to then copurify non-specifically and be misidentified as bait associated proteins. Therefore typical controls should be sufficient and a number of different baits can be compared with a common set of controls. This is of practical interest when identifying bait interactors from a large number of different baits. As expected, we found several known RelA interactors enriched in our RelA purifications (NFêB1, NFêB2, Rel, RelB, IêBá, IêBâ and IêBå). We also found several proteins not previously described in association with RelA, including the small mitochondrial chaperone Tim13. Using a variety of biochemical approaches, we further investigated the nature of the association between Tim13 and NFêB family transcription factors. The work here therefore provides a conceptual and experimental framework for analyzing transcription faction protein interactions.
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