Bayesian epiallele detection
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB21102 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR1994985_1.fastq.gz | Fastqsanger.gz | |||
| ERR1994985_2.fastq.gz | Fastqsanger.gz | |||
| ERR1994986_1.fastq.gz | Fastqsanger.gz | |||
| ERR1994986_2.fastq.gz | Fastqsanger.gz | |||
| ERR1994987_1.fastq.gz | Fastqsanger.gz |
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Similar Datasets
Project description:BackgroundEpigenetic heterogeneity within a tumour can play an important role in tumour evolution and the emergence of resistance to treatment. It is increasingly recognised that the study of DNA methylation (DNAm) patterns along the genome - so-called 'epialleles' - offers greater insight into epigenetic dynamics than conventional analyses which examine DNAm marks individually.ResultsWe have developed a Bayesian model to infer which epialleles are present in multiple regions of the same tumour. We apply our method to reduced representation bisulfite sequencing (RRBS) data from multiple regions of one lung cancer tumour and a matched normal sample. The model borrows information from all tumour regions to leverage greater statistical power. The total number of epialleles, the epiallele DNAm patterns, and a noise hyperparameter are all automatically inferred from the data. Uncertainty as to which epiallele an observed sequencing read originated from is explicitly incorporated by marginalising over the appropriate posterior densities. The degree to which tumour samples are contaminated with normal tissue can be estimated and corrected for. By tracing the distribution of epialleles throughout the tumour we can infer the phylogenetic history of the tumour, identify epialleles that differ between normal and cancer tissue, and define a measure of global epigenetic disorder.ConclusionsDetection and comparison of epialleles within multiple tumour regions enables phylogenetic analyses, identification of differentially expressed epialleles, and provides a measure of epigenetic heterogeneity. R code is available at github.com/james-e-barrett.
| S-EPMC5526259 | biostudies-literature
Project description:The transgenerational stability of DNA methylation changes is important in setting up genomic DNA methylation patterns and in the formation and transmission of epialleles. It is generally assumed that DNA methylation changes at genomic regions targeted by the de novo, RNA-directed DNA methylation (RdDM) pathway are unstable. Here, we show that RdDM targets in Arabidopsis can be classified into two groups based on their transgenerational epiallele stability following restoration of NRPD1 function in nrpd1 mutant plants: remethylable loci and non-remethylable loci. Compared to the remethylable loci, non-remethylable ones contain higher levels of the euchromatic marks H3K4me3 and H3K18ac, which interferes with the recruitment of the RdDM molecular machinery and helps to recruit the DNA demethylase ROS1 to antagonize RdDM, respectively. Using targeted de-methylation by CRISPR/dCas9-TET1, we demonstrate that mCG and mCHG are memory marks required for targeting the RdDM machinery to remethylable loci. Our results show that histone and DNA methylation marks are critical in determining the capacity of RdDM target loci to form stable epialleles, and contribute to understanding the formation and transmission of epialleles.
2020-04-17 | GSE140566 | GEO
Project description:Background: Epigenetic heterogeneity within a tumour can play an important role in tumour evolution and the emergence of resistance to treatment. It is increasingly recognised that the study of DNA methylation (DNAm) patterns along the genome - so-called `epialleles' - offers greater insight into epigenetic dynamics than conventional analyses which examine DNAm marks individually. Results: We have developed a Bayesian model to infer which epialleles are present in multiple regions of the same tumour. We apply our method to reduced representation bisulfite sequencing (RRBS) data from multiple regions of one lung cancer tumour and a matched normal sample. The model borrows information from all tumour regions to leverage greater statistical power. The total number of epialleles, the epiallele DNAm patterns, and a noise hyperparameter are all automatically inferred from the data. Uncertainty as to which epiallele an observed sequencing read originated from is explicitly incorporated by marginalising over the appropriate posterior densities. The degree to which tumour samples are contaminated with normal tissue can be estimated and corrected for. By tracing the distribution of epialleles throughout the tumour we can infer the phylogenetic history of the tumour, identify epialleles that differ between normal and cancer tissue, and define a measure of global epigenetic disorder. Conclusions: Detection and comparison of epialleles within multiple tumour regions enables phylogenetic analyses, identification of differentially expressed epialleles, and provides a measure of epigenetic heterogeneity.
| EGAS00001002484 | EGA
Project description:Epialleles are meiotically inherited variations in expression states that are not linked to changes in DNA sequence. Although they are well documented to persist in plant genomes, their molecular origins are unknown. Here, we show using a variety of mutant and experimental populations that epialleles in Arabidopsis thaliana result from feedback regulation of pathways that primarily function to maintain DNA methylation at heterochromatin. Perturbations to maintenance of heterochromatin methylation leads to feedback regulation of DNA methylation in genes, with a preference for genes with pre-existing DNA methylation. Using epigenetic recombinant inbred lines (epiRIL), we show that epiallelic variation is enriched in euchromatin, yet, associated with QTL primarily located in heterochromatin. Mapping three-dimensional chromatin contacts reveals that genes that are hotspots for epiallelic variation have increased contact frequencies with regions possessing H3K9me2. Altogether, these data show that feedback regulation of pathways that evolved to maintain heterochromatin silencing leads to the origins of spontaneous epialleles.
2021-11-01 | GSE171157 | GEO
Project description:We conducted reduced representation bisulfite sequencing (RRBS) on myonuclei and interstitial nuclei and observed stark differences in methylation patterns during adaptation
2020-04-13 | GSE144774 | GEO
Project description:This research uses consecutive generations of two independent mutation accumulation (MA) lines in model organism A. thaliana to understand transgenerational stability of epialleles via self-fertilization. With whole-genome bisulfite sequencing, regions of instability were identified and quantified. The vast majority of the methylated genome is stably inherited to offspring and the identified unstable regions do not change frequently between generations. Additionally, an epigenetic cross of two MA lines was created to understand inheritance patterns of epialleles via outcrossing in the absence of genetic variation. Whole-genome bisulfite sequencing was used to predict epigenotype of the offspring without single nucleotide polymorphisms. In regions of differential methylation between the parents, about half of regions show predictable inheritance.
2017-07-24 | GSE94298 | GEO
Project description:Single-cell analysis of the transcriptome deepens our understanding of an individual cell's contribution to its microenvironment. Using single-cell analysis to study complex biological processes requires state-of-the-art computational tools. Assessing similarity is highly important for bioinformatics algorithms in order to determine correlations between biological information. Similarity can appear by chance, particularly for low expressed entities. This is especially relevant in single cell RNA-seq (scRNA-seq) because the read counts obtained are lower compared to bulk RNA-sequencing and therefore classic bioinformatics tools are insufficient to obtain reproducible results. Recently, a Bayesian correlation scheme, that assigns low correlation values to correlations coming from low expressed genes, has been proposed to assess similarity for bulk RNA-seq and miRNA. This Bayesian method uses a prior distribution before using empirical evidence. Our goal was to extend the properties of this Bayesian correlation scheme to scRNA-seq data. We assessed 3 ways to compute similarity. First, we computed the similarity of each pair of genes over all cells. Second, we identified specific cell populations and computed the correlation in those specific cells. Third, we computed the similarity of each pair of genes over all clusters, by including the total mRNA expression in those cells. To study the effect of the number of cells on the method, we did not rely on simulated data, we generated 4 scRNA-seq mouse liver cell libraries with a varying number of input cells. Results: We show that Bayesian correlations are more reproducible than Pearson correlations in all the scenarios studied. Compared to Pearson correlations, Bayesian correlations have a smaller dependence on the number of input cells. We demonstrate that the Bayesian correlation algorithm assigns high similarity values to genes with a biological relevance in a specific population. Significance: Our results demonstrate that Bayesian correlation is a robust similarity measure for scRNA-seq datasets. The Bayesian method allows researchers to study similarity between pairs of genes without discarding low expressed entities and to minimize biasing the results by fake correlations. Taken together, using our method of Bayesian correlation the reproducibility of scRNA-seq experiments is increased significantly.
2019-12-31 | GSE134134 | GEO
Project description:Using reduced representation bisulfite sequencing (RRBS) analysis, we examined how I-PpoI-induced DSBs affected DNA methylation patterns in peripheral blood cells of 16-week-old I-PpoI mice.
2024-12-24 | GSE284752 | GEO
Project description:To assess variation and inheritance of genome-wide patterns of DNA methylation simultaneously in humans, we applied reduced representation bisulfite sequencing (RRBS) to somatic DNA from six members of a three-generation family.
2011-07-01 | GSE30253 | GEO
Project description:To assess variation and inheritance of genome-wide patterns of DNA methylation simultaneously in humans, we applied reduced representation bisulfite sequencing (RRBS) to somatic DNA from six members of a three-generation family. Reduced representation bisulfite sequencing was applied to genomic DNA from leukocytes of 6 family members and two unrelated individuals.
2011-07-01 | E-GEOD-30253 | biostudies-arrayexpress