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.
Project description:Introgressed variants from other species can be an important source of genetic variation because they may arise rapidly, can include multiple mutations on a single haplotype, and have often been pretested by selection in the species of origin. Although introgressed alleles are generally deleterious, several studies have reported introgression as the source of adaptive alleles-including the rodenticide-resistant variant of Vkorc1 that introgressed from Mus spretus into European populations of Mus musculus domesticus. Here, we conducted bidirectional genome scans to characterize introgressed regions into one wild population of M. spretus from Spain and three wild populations of M. m. domesticus from France, Germany, and Iran. Despite the fact that these species show considerable intrinsic postzygotic reproductive isolation, introgression was observed in all individuals, including in the M. musculus reference genome (GRCm38). Mus spretus individuals had a greater proportion of introgression compared with M. m. domesticus, and within M. m. domesticus, the proportion of introgression decreased with geographic distance from the area of sympatry. Introgression was observed on all autosomes for both species, but not on the X-chromosome in M. m. domesticus, consistent with known X-linked hybrid sterility and inviability genes that have been mapped to the M. spretus X-chromosome. Tract lengths were generally short with a few outliers of up to 2.7 Mb. Interestingly, the longest introgressed tracts were in olfactory receptor regions, and introgressed tracts were significantly enriched for olfactory receptor genes in both species, suggesting that introgression may be a source of functional novelty even between species with high barriers to gene flow.
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.
Project description:By 4C-seq protocol we investigated DNA contacts across the genome by the FLC gene in the model plant Arabidopsis thaliana in order to explore a potential role of long-distance chromosomal interactions in the regulation of flowering.
Project description:Translational research is commonly performed in the C57B6/J mouse strain, chosen for its genetic homogeneity and phenotypic uniformity. Here, we evaluate the suitability of the white-footed deer mouse (Peromyscus leucopus) as a model organism for aging research, offering a comparative analysis against C57B6/J and diversity outbred (DO) Mus musculus strains. Our study includes comparisons of body composition, skeletal muscle function, and cardiovascular parameters, shedding light on potential applications and limitations of P. leucopus in aging studies. Notably, P. leucopus exhibits distinct body composition characteristics, emphasizing reduced muscle force exertion and a unique metabolism, particularly in fat mass. Cardiovascular assessments showed changes in arterial stiffness, challenging conventional assumptions and highlighting the need for a nuanced interpretation of aging-related phenotypes. Our study also highlights inherent challenges associated with maintaining and phenotyping P. leucopus cohorts. Behavioral considerations, including anxiety-induced responses during handling and phenotyping assessment, pose obstacles in acquiring meaningful data. Moreover, the unique anatomy of P. leucopus necessitates careful adaptation of protocols designed for Mus musculus. While showcasing potential benefits, further extensive analyses across broader age ranges and larger cohorts are necessary to establish the reliability of P. leucopus as a robust and translatable model for aging studies.
Project description:Drosophila Insulator proteins mediate long-range chromosomal interactions. ChIP-seq revealed that binding of insulator proteins to some specific DNA sites was regulated by poly(ADP-ribosyl)ation in S2 cells. Three insulator sites regulated by poly(ADP-ribosyl)ation were used as baits to map their distant interacting sites using 4C assay in control S2 cells. Mapping the chromosomal interactions of three specific insulator binding sites with 4C assay in control S2 cells.
Project description:A transcriptome study in mouse hematopoietic stem cells was performed using a sensitive SAGE method, in an attempt to detect medium and low abundant transcripts expressed in these cells. Among a total of 31,380 unique transcript, 17,326 (55%) known genes were detected, 14,054 (45%) low-copy transcripts that have no matches to currently known genes. 3,899 (23%) were alternatively spliced transcripts of the known genes and 3,754 (22%) represent anti-sense transcripts from known genes.