Robust 4C-seq data analysis to screen for regulatory DNA interactions
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE40420 | GEO | 2012/09/04
SECONDARY ACCESSION(S): PRJNA174285
REPOSITORIES: GEO
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Project description:Regulatory DNA elements can control expression of distant genes via physical interactions. Here, we present a cost-effective methodology and computational analysis pipeline for robust characterization of the physical organization around selected promoters and other functional elements using Chromosome Conformation Capture combined with high-throughput sequencing (4C-seq) data. Our approach can be multiplexed and routinely integrated with other functional genomics assays to facilitate physical characterization of gene regulation. A high resolution 4C-seq protocol involving two restriction digests and a revised analysis pipeline was applied to several viewpoints in four genomic loci (the well-characterized alpha-globin and beta-globin loci, and the novel Oct4 and Satb1 loci), allowing robust detection of physical interactions between regulatory DNA elements.
2012-09-04 | E-GEOD-40420 | biostudies-arrayexpress
Project description:To test if lncRNA AC092723.1 play a role in physical interaction between the IRF8 promoter and the rs2280381-containing region, we conducted circular chromatin conformation capture assay in the human primary monocyte. We knock down lncRNA AC092723.1 by electro-transfecting ASOs, the control group is transfected negtive ASOs with no impact on lncRNA AC092723.1.We used CSP6I as the first digested enzyme and NlaIII as the second enzyme. After constructing 4C library,we then utilize 4C-PCR primer to construct IRF8 point view 4C library.
2022-02-25 | E-MTAB-11175 | biostudies-arrayexpress
Project description:To test if there is a physical interaction between the IRF8 promoter and the rs2280381-containing region, we conducted circular chromatin conformation capture assay in the U937 cell. For the IRF8 viewpoint group, we used CSP6I as the first digested enzyme and NlaIII as the second enzyme. For the rs2280381-containing region, we used MboI as the first digested enzyme and NlaIII as the second enzyme. We use 4C-PCR primer to construct IRF8 point view and rs2280381-containing region 4C library.
2021-07-01 | E-MTAB-10120 | biostudies-arrayexpress
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Project description:Cancer cells frequently depend on chromatin regulatory activities to maintain a malignant phenotype. Here, we show that leukemia cells require the mammalian SWI/SNF chromatin remodeling complex for their survival and aberrant self-renewal potential. While Brg1, an ATPase subunit of SWI/SNF, is known to suppress tumor formation in several cancer types, we found that leukemia cells instead rely on Brg1 to support their oncogenic transcriptional program, which includes Myc as one of its key targets. To account for this context-specific function, we identify a cluster of lineage-specific enhancers located 1.7 megabases downstream of Myc that are occupied by SWI/SNF, as well as the BET protein Brd4. Brg1 is required at these distal elements to maintain transcription factor occupancy and for long-range chromatin looping interactions with the Myc promoter. Notably, these distal Myc enhancers coincide with a region that is focally amplified in 3% of acute myeloid leukemia. Together, these findings define a leukemia maintenance function for SWI/SNF that is linked to enhancer-mediated gene regulation, providing general insights into how cancer cells exploit transcriptional coactivators to maintain oncogenic gene expression programs Enhancer usually regulates its targets through physical contact/interaction. In order to study chromosome conformation of Myc locus and potential distal enhancer E1-E5 region in murine AML cells, we utilize the high resolution 4C-seq and analysis pipeline to search cis elements that physical interact with Myc and E1-E5 region through setting up two individual viewpoints in these two regions.
2013-12-02 | E-GEOD-52595 | 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.
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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:To characterize enhancer elements regulating the MEF2C gene during neurodevelopment, we performed among others Circularized Chromosome Conformation Capture (4C) sequencing in a neuronal cell line, SH-SY5Y, and a non-neuronal control, HEK. We found that, in a neuronal cell line, the MEF2C promoter interacts with several putative enhancer elements in its upstream region, forming a complex interaction network. Moreover, these interactions are completely absent in HEK cells.
2018-10-22 | GSE121549 | GEO
Project description:Capture Hi-C (CHi-C) is a new technique for assessing genome organization based on chromosome conformation capture coupled to oligonucleotide capture of regions of interest, such as gene promoters. Chromatin loop detection is challenging because existing Hi-C/4C-like tools, which make different assumptions about the technical biases presented, are often unsuitable. We describe a new approach, ChiCMaxima, which uses local maxima combined with limited filtering to detect DNA looping interactions, integrating information from biological replicates. ChiCMaxima shows more stringency and robustness compared to previously developed tools. The tool includes a GUI browser for flexible visualization of CHi-C profiles alongside epigenomic tracks. The results of the analysis on existing mouse ES Capture Hi-C data (ArrayExpress E-MTAB-2414) were validated by two 4C-seq experiments, submitted here.
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