7cbaadf2-3d7f-4bb2-b239-a265d4f05965 - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001010897 | EGA |
REPOSITORIES: EGA
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Project description:Hi-C libraries have been prepared by enzymatic digestion with MboI restriction enzyme and sonication. Illumina single-indexig primers have been use to amplify ligated fragments. Hi-C experiments have been performed on 10 slices (30um thick) of fresh-frozen tissues derived from 3 healthy and 3 tumor endometrial tissues of post-menopausal patients. Raw paired-end fastq.gz files are provided.
| EGAD00001010898 | EGA
Project description:The human endometrium is a highly regenerative tissue that undergoes cyclical proliferation, differentiation and shedding each month. The upper functionalis layer of the endometrium is shed in response to circulating levels of estrogen and progesterone, while the lower basalis layer remains. Clonogenic epithelial stem/progenitor cells likely responsible for regenerating endometrial epithelium have been identified in pre-menopausal (Pre-M) and post-menopausal (Post-M) endometrium and may reside in the basalis layer. We undertook a transcriptional profiling of purified epithelial cells from full-thickness Pre-M and Post-M endometrium to identify differentially expressed genes. The hypothesis tested in the present study was that Post-M endometrial epithelial gene profile would be similar to the quiescent basalis epithelium of Pre-M endometrium. We found striking differential gene expression of many Wnt family members between Pre-M and Post-M and other stem cell network genes. Comparative analysis of our endometrial epithelial gene expression profiles to that of endometrial epithelial cells in remodelling endometrium also provides new evidence showing that Post-M endometrial epithelium has a similar gene signature to that of basalis epithelium of menstrual endometrium. Human endometrial tissue was obtained from 8 pre-menopausal and 3 post-menopausal women undergoing hysterectomy for various benign gynaecologic conditions. Endometrial tissue was digested and isolated using combination of DNase and collagenase. Anti-human EpCAM antibody-coated magnetic Dynabeads was used to positively select total epithelial cells from the digested single cell suspensions. Total RNA was extracted from the purified endometrial epithelial cells and hybridised to Illumina Sentrix HT12 beadchip. Resulting data was compared between pre-menopausal and post-menopausal samples.
2012-04-10 | E-GEOD-35221 | 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: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:ChIP-seq has been perfomed on 4 healthy and 5 tumor fresh-frozen endometrial tissues from post-menopausal patients. Immunoprecipitation has been performed for H3K27ac and ERa. Raw single-end fastq data have been aligned using bwa-mem using Hg19 genome assebly as reference. Aligment bam files are provided.
| EGAD00001010896 | EGA
Project description:Insight on the understanding of the effect of maternal diabetes on embryonic nervous system development in embryos without neural tube defects. Control Group: Each sample (n) contained the RNA obtained by TRIZOL of a bulk of 30 prosencephalons (embryo day 12) of embryos from both sexes obtained from pregnant rats that were intraperitoneally injected with citrate buffer. n=3 Experimental group: Each sample (n) contained the RNA obtained by TRIZOL of A bulk of 30 prosencephalons (embryo day 12) of embryos from both sexes obtained from pregnant rats that were diabetic induced with an intraperitoneal administration of 50 mg/kg of streptozotocin. n=3 The sequence liberties were obtained through paired-end sequencing experiments. Codes for the files are as follows: Control E12: AM1_S1_R1_001.fastq.gz AM1_S1_R2_001.fastq.gz AM2_S2_R1_001.fastq.gz AM2_S2_R2_001.fastq.gz AM3_S3_R1_001.fastq.gz AM3_S3_R2_001.fastq.gz Diabetic E12: AM4_S4_R1_001.fastq.gz AM4_S4_R2_001.fastq.gz AM5_S5_R1_001.fastq.gz AM5_S5_R2_001.fastq.gz AM6_S6_R1_001.fastq.gz AM6_S6_R2_001.fastq.gz Were S = sample, R1 = sense sequencing, and R2 = antisense sequencing. Sequencing was performed on a NextSeq500 using the TruSeq HT kit from Illumina.
2025-05-15 | E-MTAB-15111 | biostudies-arrayexpress
Project description:Chromosome Conformation Capture Sequencing (4C-seq) was performed to assess interaction between the MYC super-enhancer and the EVI1 promoter under different conditions. Sample preparation was performed as previously described (van de Werken et al., 2012). In short, genomic regions that are spatially proximal in the cell nucleus were fixated by formaldehyde-induced crosslinks. The DNA was fragmented with DpnII as a primary restriction enzyme, Csp6I as a secondary 4 bp-cutter. To identify and quantify fragments that were ligated to the genomic region of interest, a two-step PCR was performed as previously described (Krijger et al., 2020). In the second PCR step, universal primers were used that contain the Illumina adapters. The amplicons were analyzed using next generation sequencing on the IIlumina NovaSeq platform.
2021-07-26 | E-MTAB-9958 | biostudies-arrayexpress
Project description:High-resolution methods such as 4C and Capture-C enable the study of chromatin loops such as those formed between promoters and enhancers or CTCF/cohesin binding sites. An important aspect of 4C/CapC analyses is the identification of robust peaks in the data for the identification of chromatin loops. Here we present an R package for the analysis of 4C/CapC data. We generated 4C data for 10 viewpoints in 2 tissues in triplicate to test our methods. We developed a non-parametric peak caller based on rank-products. Sampling analysis shows that not read depth but template quality is the most important determinant of success in 4C experiments. By performing peak calling on single experiments we show that the peak calling results are similar to the replicate experiments, but that false positive rates are significantly reduced by performing replicates.
2018-05-14 | GSE105177 | GEO
Project description:The aim of the underlying study was to identify protein signatures for the detection of endometrial cancer in minimally invasive samples such as cervico-vaginal fluid and blood plasma. Plasma and Delphi Screener-collected cervico-vaginal fluid samples were acquired from post-menopausal women who were symptomatic with (n=53)and without(n=65)endometrial cancer. Digitised proteomic maps were developed for each sample by sequential window acquisition of all theoretical mass spectra (SWATH-MS). Machine learning was employed to identify the most discriminatory proteins and a set of high-perfoming biomarker signatures obtained.
2024-06-23 | PXD050276 | Pride