Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA281124 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR1974050.fastq.gz | Fastqsanger.gz | |||
| SRR1974060.fastq.gz | Fastqsanger.gz | |||
| SRR1974070.fastq.gz | Fastqsanger.gz | |||
| SRR1974080.fastq.gz | Fastqsanger.gz | |||
| SRR1974051.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 35 |
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Project description:DNA methylation is a complex epigenetic marker that can be analysed using a wide variety of methods. Interpretation and visualisation of DNA methylation data can mask complexity in terms of methylation status at each CpG site, cellular heterogeneity of samples and allelic DNA methylation patterns within a given DNA strand. Bisulfite sequencing is considered the gold standard, however visualisation of massively parallel sequencing results remains a significant challenge. We created a program called Methpat that facilitates visualisation and interpretation of bisulfite sequencing data generated by massively parallel sequencing. To demonstrate this, we performed multiplex PCR that targeted 48 regions of interest across 95 human samples. The regions selected included known gene promoters associated with cancer, repetitive elements, known imprinted regions and mitochondrial genomic sequences. We interrogated a range of samples including human cell lines, primary tumours and primary tissue samples. Methpat generates two forms of output: a tab delimited text file for each sample that summarises DNA methylation patterns and their read counts for each amplicon and a HTML file that summarises this data visually. Methpat can be used with publicly available whole genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) datasets with sufficient read depths. Using Methpat, complex DNA methylation data derived from massively parallel sequencing can be summarised and visualised for biological interpretation. By accounting for allelic DNA methylation states and their abundance in a sample, Methpat can unmask the complexity of DNA methylation and reveal further biological insight in existing datasets. Multiplex bisulfite PCR and Next Generation sequencing of primary human samples and breast cancer cell lines.
2015-11-23 | E-GEOD-71804 | biostudies-arrayexpress
Project description:DNA methylation is a complex epigenetic marker that can be analysed using a wide variety of methods. Interpretation and visualisation of DNA methylation data can mask complexity in terms of methylation status at each CpG site, cellular heterogeneity of samples and allelic DNA methylation patterns within a given DNA strand. Bisulfite sequencing is considered the gold standard, however visualisation of massively parallel sequencing results remains a significant challenge. We created a program called Methpat that facilitates visualisation and interpretation of bisulfite sequencing data generated by massively parallel sequencing. To demonstrate this, we performed multiplex PCR that targeted 48 regions of interest across 95 human samples. The regions selected included known gene promoters associated with cancer, repetitive elements, known imprinted regions and mitochondrial genomic sequences. We interrogated a range of samples including human cell lines, primary tumours and primary tissue samples. Methpat generates two forms of output: a tab delimited text file for each sample that summarises DNA methylation patterns and their read counts for each amplicon and a HTML file that summarises this data visually. Methpat can be used with publicly available whole genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) datasets with sufficient read depths. Using Methpat, complex DNA methylation data derived from massively parallel sequencing can be summarised and visualised for biological interpretation. By accounting for allelic DNA methylation states and their abundance in a sample, Methpat can unmask the complexity of DNA methylation and reveal further biological insight in existing datasets.
2015-11-23 | GSE71804 | GEO
Project description:DNA methylation at a gene promoter region has the potential to regulate gene transcription. Their patterns are often complex with the region showing multiple allelic patterns in a sample. This complexity is commonly obscured when DNA methylation data is summarised as an average percentage value for each CpG site (or aggregated across CpG sites). The methylation state at adjacent CpG sites is therefore lost when data is summarised this way. Methylation patterns can only be characterised by clonal analysis. Deep sequencing provides the ability to investigate clonal DNA methylation patterns in unprecedented detail and scale, enabling the proper characterisation of the heterogeneity of methylation patterns. However, the sheer amount of sequencing data requires new synoptic approaches to visualise the distribution of allelic patterns. We have developed an analysis and visualisation software tool "Methpat", that extracts and displays clonal DNA methylation patterns from massively parallel sequencing data aligned using Bismark. We have performed multiplex bisulfite amplicon sequencing on a range of CpG island targets across a panel of human cell lines and primary tissues. Using Methpat, we demonstrate clonal diversity of epialleles analysed at specific gene promoter regions. We also describe the existence of DNA methylation within the mitochondrial genome. Multiplex bisulfite PCR and Next Generation sequencing of 35 samples
2015-11-23 | E-GEOD-67856 | biostudies-arrayexpress
Project description:DNA methylation at a gene promoter region has the potential to regulate gene transcription. Their patterns are often complex with the region showing multiple allelic patterns in a sample. This complexity is commonly obscured when DNA methylation data is summarised as an average percentage value for each CpG site (or aggregated across CpG sites). The methylation state at adjacent CpG sites is therefore lost when data is summarised this way. Methylation patterns can only be characterised by clonal analysis. Deep sequencing provides the ability to investigate clonal DNA methylation patterns in unprecedented detail and scale, enabling the proper characterisation of the heterogeneity of methylation patterns. However, the sheer amount of sequencing data requires new synoptic approaches to visualise the distribution of allelic patterns. We have developed an analysis and visualisation software tool "Methpat", that extracts and displays clonal DNA methylation patterns from massively parallel sequencing data aligned using Bismark. We have performed multiplex bisulfite amplicon sequencing on a range of CpG island targets across a panel of human cell lines and primary tissues. Using Methpat, we demonstrate clonal diversity of epialleles analysed at specific gene promoter regions. We also describe the existence of DNA methylation within the mitochondrial genome.
2015-11-23 | GSE67856 | GEO
Project description:DNA methylation has been comprehensively profiled in normal and cancer cells, but the dynamics that form, maintain and reprogram differentially methylated regions remain enigmatic. We show that methylation patterns within populations of cells from individual somatic tissues are heterogeneous and polymorphic. Using in vitro evolution of immortalized fibroblasts for over 300 generations, we track the dynamics of polymorphic methylation at regions developing significant differential methylation on average. The data indicate that changes in population-averaged methylation occur through a stochastic process that generates a stream of local and uncorrelated methylation aberrations. Despite the stochastic nature of the process, nearly deterministic epigenetic remodeling emerges on average at loci that lose or gain resistance to methylation accumulation. Changes in the susceptibility to methylation accumulation are correlated with changes in histone modifications and CTCF occupancy. Characterizing epigenomic polymorphism within cell populations is therefore critical for understanding methylation dynamics in normal and cancer cells. Genomewide sequencing data is included herein: H3K27me3 profiled throughout the microevolutionary timecourse using ChIP-seq, DNA methylation profiled through the timecourse using MeDIP-seq, and H3K4me3 and CTCF profiled in late and early timepoints using ChIP-seq. Deep Bisulfite sequencing amplicon data and results can be obtained at: http://compgenomics.weizmann.ac.il/tanay/?page_id=99 .
2012-09-24 | E-GEOD-41048 | biostudies-arrayexpress
Project description:Assessment of measurable residual disease (MRD) upon treatment of acute myeloid leukemia (AML) remains a challenge. It is usually addressed by highly sensitive PCR-based screening of disease-specific mutations or multiparametric flow cytometry. However, not all patients have suitable mutations to track the disease and heterogeneity of applied surface markers hampers standardization in clinical routine. In this study, we propose an alternative approach to estimate MRD based on AML-associated DNA methylation (DNAm) patterns. We identified four CG dinucleotides (CpGs) that commonly reveal aberrant DNAm in AML and their combination could reliably discern healthy and AML samples. Interestingly, many of these aberrations are shown to affect both alleles. Using bisulfite amplicon sequencing (BA-seq), we captured local DNAm patterns flanking these AML associated CpGs, and trained shallow-learning and deep-learning algorithms to identify anomalous, non healthy DNAm patterns within the amplicons. The method was then tested on follow-up samples with and without MRD status. Notably, many MRD negative samples revealed higher anomaly ratios than healthy controls. Our results demonstrate that targeted DNAm analysis facilitates reliable discrimination of malignant and healthy samples. However, even after successful treatment aberrant DNAm patterns persist in some samples that were classified as MRD negative.
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Project description:Omental adipose tissue was obtained from 24 women aged 25-40 years with a healthy singleton pregnancy and a pre-pregnancy BMI of 18.5-26 kg/m2. Genomic DNA was extracted for promoter DNA methylation analysis of candidate genes LEP, TNF, NPY, POMC and SOCS3 using bisulfite amplicon sequencing.
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Project description:Several organisms belonging to diverse animal groups have retained Dnmt2 as their only bona fide DNA methyltransferase gene. However, recent studies have shown that Dnmt2 functions as a tRNA methyltransferase, which prompted us to analyze the methylomes of Dnmt2-only organisms at single-base resolution. Using whole-genome bisulfite sequencing we show here that the genomes of Schistosoma mansoni and Drosophila melanogaster lack detectable DNA methylation patterns. Residual unconverted cytosine residues shared many attributes with bisulfite deamination artifacts and were observed at comparable levels in a Dnmt2-deficient fly strain. Furthermore, genetically modified mouse embryonic stem cells that had retained Dnmt2 as their only bona fide DNA methyltransferase gene, did not show any detectable DNA methylation patterns. Our results thus uncover fundamental differences among animal methylomes and suggest that Dnmt2-only organisms lack biologically relevant DNA methylation patterns. Whole methylome analysis of Mus musculus. One sample was analyzed containing DNA from Dnmt1-/-, Dnmt3a-/- and Dnmt3b-/- mice.
2013-05-06 | E-GEOD-42170 | biostudies-arrayexpress
Project description:Several organisms belonging to diverse animal groups have retained Dnmt2 as their only bona fide DNA methyltransferase gene. However, recent studies have shown that Dnmt2 functions as a tRNA methyltransferase, which prompted us to analyze the methylomes of Dnmt2-only organisms at single-base resolution. Using whole-genome bisulfite sequencing we show here that the genomes of Schistosoma mansoni and Drosophila melanogaster lack detectable DNA methylation patterns. Residual unconverted cytosine residues shared many attributes with bisulfite deamination artifacts and were observed at comparable levels in a Dnmt2-deficient fly strain. Furthermore, genetically modified mouse embryonic stem cells that had retained Dnmt2 as their only bona fide DNA methyltransferase gene, did not show any detectable DNA methylation patterns. Our results thus uncover fundamental differences among animal methylomes and suggest that Dnmt2-only organisms lack biologically relevant DNA methylation patterns.
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