FourSig: a method for determining chromosomal interactions in 4C-Seq data.
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ABSTRACT:
SUBMITTER: Williams RL
PROVIDER: S-EPMC4005674 | biostudies-literature | 2014 Apr
REPOSITORIES: biostudies-literature
Publications
fourSig: a method for determining chromosomal interactions in 4C-Seq data.
Williams Rex L RL Starmer Joshua J Mugford Joshua W JW Calabrese J Mauro JM Mieczkowski Piotr P Yee Della D Magnuson Terry T
Nucleic acids research 20140220 8
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 data sets and it is imperative that signal is properly distinguished from noise. Currently, there are a limited ...[more]
Similar Datasets
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: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: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.
| S-EPMC4777514 | biostudies-literature
Project description:In bacteria, determination of the 3' termini of transcripts plays an essential role in regulation of gene expression, affecting the functionality and stability of the transcript. Several experimental approaches were developed to identify the 3' termini of transcripts, however, these were applied only to a limited number of bacteria and growth conditions. Here we present a straightforward approach to identify 3' termini from widely available RNA-seq data without the need for additional experiments. Our approach relies on the observation that the RNAtag-seq sequencing protocol results in overabundance of reads mapped to transcript 3' termini. We present TRS (Termini by Read Starts), a computational pipeline exploiting this property to identify 3' termini in RNAtag-seq data, and show that the identified 3' termini are highly reliable. Since RNAtag-seq data are widely available for many bacteria and growth conditions, our approach paves the way for studying bacterial transcription termination in an unprecedented scope.
| S-EPMC10687069 | biostudies-literature
Project description:Circularized Chromosome Conformation Capture followed by deep sequencing (4C-Seq) is a powerful technique to identify genome-wide partners interacting with a pre-specified genomic locus. Here, we present a computational and statistical approach to analyze 4C-Seq data generated from both enzyme digestion and sonication fragmentation-based methods. We implemented a command line software tool and a web interface called w4CSeq, which takes in the raw 4C sequencing data (FASTQ files) as input, performs automated statistical analysis and presents results in a user-friendly manner. Besides providing users with the list of candidate interacting sites/regions, w4CSeq generates figures showing genome-wide distribution of interacting regions, and sketches the enrichment of key features such as TSSs, TTSs, CpG sites and DNA replication timing around 4C sites.Availability and implementationUsers can establish their own web server by downloading source codes at https://github.com/WGLab/w4CSeq Additionally, a demo web server is available at http://w4cseq.wglab.org CONTACT: kaiwang@usc.edu or wangelu@usc.eduSupplementary information: Supplementary data are available at Bioinformatics online.
| S-EPMC5079477 | biostudies-literature
Project description:MotivationWith its capacity for high-resolution data output in one region of interest, chromosome conformation capture combined with high-throughput sequencing (4C-seq) is a state-of-the-art next-generation sequencing technique that provides epigenetic insights, and regularly advances current medical research. However, 4C-seq data are complex and prone to biases, and while specialized programs exist, an unbiased, extensive benchmarking is still lacking. Furthermore, neither substantial datasets with fully characterized ground truth, nor simulation programs for realistic 4C-seq data have been published.ResultsWe conducted a benchmarking study on 66 4C-seq samples from 20 datasets, and developed a novel 4C-seq simulation software, Basic4CSim, to allow for detailed comparisons of 4C-seq algorithms on 50 simulated datasets with 10-120 samples each. Simulations and benchmarking were adapted to address different characteristics of 4C-seq data. Simulated data were compared with published samples to validate simulation settings. We identified differences between 4C-seq algorithms in terms of precision, recall, interaction structure, and run time, and observed general trends. Novel differential pipeline versions of single-sample based 4C-seq algorithms were included in the benchmarking. While no single tool was optimally suited for both near-cis and far-cis, and both single-sample and differential analyses, choosing a high-performing algorithm variant did improve results considerably. For near-cis scenarios, r3Cseq, peakC and FourCSeq offered high precision, while fourSig demonstrated high overall F1 scores in far-cis analyses. Finally, 4C-seq simulations may aid in the development of improved analysis algorithms.Availability and implementationBasic4CSim is available at https://github.com/walter-ca/Basic4CSim.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6901067 | biostudies-literature
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:Use of low resolution single cell DNA FISH and population based high resolution chromosome conformation capture techniques have highlighted the importance of pairwise chromatin interactions in gene regulation. However, it is unlikely that associations involving regulatory elements act in isolation of other interacting partners that also influence their impact. Indeed, the influence of multi-loci interactions remains something of an enigma as beyond low-resolution DNA FISH we do not have the appropriate tools to analyze these. Here we present a method that uses standard 4C-seq data to identify multi-loci interactions from the same cell. We demonstrate the feasibility of our method using 4C-seq data sets that identify known pairwise and novel tri-loci interactions involving the Tcrb and Igk antigen receptor enhancers. We further show that the three Igk enhancers, MiEκ, 3'Eκ and Edκ, interact simultaneously in this super-enhancer cluster, which add to our previous findings showing that loss of one element decreases interactions between all three elements as well as reducing their transcriptional output. These findings underscore the functional importance of simultaneous interactions and provide new insight into the relationship between enhancer elements. Our method opens the door for studying multi-loci interactions and their impact on gene regulation in other biological settings.
| S-EPMC5062970 | biostudies-literature
Project description:BackgroundCircular chromosome conformation capture, when coupled with next-generation sequencing (4C-Seq), can be used to identify genome-wide interaction of a given locus (a "bait" sequence) with all of its interacting partners. Conventional 4C approaches used restriction enzyme digestion to fragment chromatin, and recently sonication approach was also applied for this purpose. However, bioinformatics pipelines for analyzing sonication-based 4C-Seq data are not well developed. In addition, data consistency as well as similarity between the two methods has not been explored previously. Here we present a comparative analysis of 4C-Seq data generated by both methods, using an enhancer element of Pou5f1 gene in mouse embryonic stem (ES) cells.ResultsFrom biological replicates, we found good correlation (r>0.6) for inter-chromosomal interactions identified in either enzyme or sonication method. Compared to enzyme approach, sonication method generated less distal intra-chromosomal interactions, possibly due to the difference in chromatin fragmentation. From all mapped interactions, we further applied statistical models to identify enriched interacting regions. Interestingly, data generated from the two methods showed 30% overlap of the reproducible interacting regions. The interacting sites in the reproducible regions from both methods are similarly enriched with active histone marks. In addition, the interacting sites identified from sonication-based data are enriched with ChIP-Seq signals of transcription factors Oct4, Klf4, Esrrb, Tcfcp2i1, and Zfx that are critical for reprogramming and pluripotency.ConclusionsBoth enzyme-based and sonication-based 4C-Seq methods are valuable tools to explore long-range chromosomal interactions. Due to the nature of sonication-based method, correlation analysis of the 4C interactions with transcription factor binding should be more straightforward.
| S-EPMC3679908 | biostudies-literature
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