Characterizing the properties of bisulfite sequencing data: maximizing power and sensitivity to identify differences in DNA methylation
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ABSTRACT:
ORGANISM(S): Mus musculus Rattus norvegicus Homo sapiens
PROVIDER: GSE169235 | GEO | 2021/03/20
REPOSITORIES: GEO
Similar Datasets
Project description:Background The combination of sodium bisulfite treatment with highly-parallel sequencing is a common method for quantifying DNA methylation across the genome. The power to detect between-group differences in DNA methylation using bisulfite-sequencing approaches is influenced by both experimental (e.g. read depth, missing data and sample size) and biological (e.g. mean level of DNA methylation and difference between groups) parameters. There is, however, no consensus about the optimal thresholds for filtering bisulfite sequencing data with implications for the reproducibility of findings in epigenetic epidemiology. Results We used a large reduced representation bisulfite sequencing (RRBS) dataset to assess the distribution of read depth across DNA methylation sites and the extent of missing data. To investigate how various study variables influence power to identify DNA methylation differences between groups, we developed a framework for simulating bisulfite sequencing data. As expected, sequencing read depth, group size, and the magnitude of DNA methylation difference between groups all impacted upon statistical power. The influence on power was not dependent on one specific parameter, but reflected the combination of study-specific variables. As a resource to the community, we have developed a tool, POWEREDBiSeq, which utilizes our simulation framework to predict study-specific power for the identification of DNAm differences between groups, taking into account user-defined read depth filtering parameters and the minimum sample size per group. Conclusions Our data-driven approach highlights the importance of filtering bisulfite-sequencing data by minimum read depth and illustrates how the choice of threshold is influenced by the specific study design and the expected differences between groups being compared. The POWEREDBiSeq tool can help users identify the level of data filtering needed to optimize power and aims to improve the reproducibility of bisulfite sequencing studies.
2021-03-20 | GSE169234 | GEO
Project description:Background The combination of sodium bisulfite treatment with highly-parallel sequencing is a common method for quantifying DNA methylation across the genome. The power to detect between-group differences in DNA methylation using bisulfite-sequencing approaches is influenced by both experimental (e.g. read depth, missing data and sample size) and biological (e.g. mean level of DNA methylation and difference between groups) parameters. There is, however, no consensus about the optimal thresholds for filtering bisulfite sequencing data with implications for the reproducibility of findings in epigenetic epidemiology. Results We used a large reduced representation bisulfite sequencing (RRBS) dataset to assess the distribution of read depth across DNA methylation sites and the extent of missing data. To investigate how various study variables influence power to identify DNA methylation differences between groups, we developed a framework for simulating bisulfite sequencing data. As expected, sequencing read depth, group size, and the magnitude of DNA methylation difference between groups all impacted upon statistical power. The influence on power was not dependent on one specific parameter, but reflected the combination of study-specific variables. As a resource to the community, we have developed a tool, POWEREDBiSeq, which utilizes our simulation framework to predict study-specific power for the identification of DNAm differences between groups, taking into account user-defined read depth filtering parameters and the minimum sample size per group. Conclusions Our data-driven approach highlights the importance of filtering bisulfite-sequencing data by minimum read depth and illustrates how the choice of threshold is influenced by the specific study design and the expected differences between groups being compared. The POWEREDBiSeq tool can help users identify the level of data filtering needed to optimize power and aims to improve the reproducibility of bisulfite sequencing studies.
2021-03-20 | GSE169218 | GEO
Project description:BackgroundThe combination of sodium bisulfite treatment with highly-parallel sequencing is a common method for quantifying DNA methylation across the genome. The power to detect between-group differences in DNA methylation using bisulfite-sequencing approaches is influenced by both experimental (e.g. read depth, missing data and sample size) and biological (e.g. mean level of DNA methylation and difference between groups) parameters. There is, however, no consensus about the optimal thresholds for filtering bisulfite sequencing data with implications for the reproducibility of findings in epigenetic epidemiology.ResultsWe used a large reduced representation bisulfite sequencing (RRBS) dataset to assess the distribution of read depth across DNA methylation sites and the extent of missing data. To investigate how various study variables influence power to identify DNA methylation differences between groups, we developed a framework for simulating bisulfite sequencing data. As expected, sequencing read depth, group size, and the magnitude of DNA methylation difference between groups all impacted upon statistical power. The influence on power was not dependent on one specific parameter, but reflected the combination of study-specific variables. As a resource to the community, we have developed a tool, POWEREDBiSeq, which utilizes our simulation framework to predict study-specific power for the identification of DNAm differences between groups, taking into account user-defined read depth filtering parameters and the minimum sample size per group.ConclusionsOur data-driven approach highlights the importance of filtering bisulfite-sequencing data by minimum read depth and illustrates how the choice of threshold is influenced by the specific study design and the expected differences between groups being compared. The POWEREDBiSeq tool, which can be applied to different types of bisulfite sequencing data (e.g. RRBS, whole genome bisulfite sequencing (WGBS), targeted bisulfite sequencing and amplicon-based bisulfite sequencing), can help users identify the level of data filtering needed to optimize power and aims to improve the reproducibility of bisulfite sequencing studies.
| S-EPMC8204428 | biostudies-literature
Project description:Characterizing the properties of bisulfite sequencing data: maximizing power and sensitivity to identify differences in DNA methylation
| PRJNA715753 | ENA
Project description:Characterizing the properties of bisulfite sequencing data: maximizing power and sensitivity to identify differences in DNA methylation [Array]
| PRJNA715743 | ENA
Project description:Characterizing the properties of bisulfite sequencing data: maximizing power and sensitivity to identify differences in DNA methylation [RRBS]
| PRJNA715761 | ENA
Project description:DNA methylation is an important regulator of gene function. Fetal sex is associated with the risk of several specific pregnancy complications related to placental function. However, the association between fetal sex and placental DNA methylation remains poorly understood. We carried out whole-genome oxidative bisulfite sequencing in the placentas of two healthy female and two healthy male pregnancies generating an average genome depth of coverage of 25x. Most highly ranked differentially methylated regions (DMRs) were located on the X chromosome but we identified a 225 kb sex-specific DMR in the body of the CUB and Sushi Multiple Domains 1 (CSMD1) gene on chromosome 8. The sex-specific differential methylation pattern observed in this region was validated in additional placentas using in-solution target capture. In a new RNA-seq data set from 64 female and 67 male placentas, CSMD1 mRNA was 1.8-fold higher in male than in female placentas (P value = 8.5 × 10-7, Mann-Whitney test). Exon-level quantification of CSMD1 mRNA from these 131 placentas suggested a likely placenta-specific CSMD1 isoform not detected in the 21 somatic tissues analyzed. We show that the gene body of an autosomal gene, CSMD1, is differentially methylated in a sex- and placental-specific manner, displaying sex-specific differences in placental transcript abundance.
| S-EPMC5989156 | biostudies-literature
Project description:Epigenetic changes in the DNA methylome are increasingly shown to play an integral role in regulating gene expression necessary for plants’ adaption to environmental stressors. Plants subjected to the novel environment of spaceflight onboard the International Space Station (ISS), show stress-related transcriptomic changes most notably associated with pathogen stress response. Here, we investigate how known terrestrial stress associated epigenetic modulations might play a role in spaceflight adaptation. To examine the role of 5mCyt in spaceflight adaptation, the APEX04-EPEX experiment conducted onboard the ISS evaluated the spaceflight altered genome wide methylation profiles of two methylation regulating gene mutants (methyltransferase 1 (met1-7) and elongator complex subunit 2 (elp2-5)) that are involved in pathogen defense response, along with a wild type Col-0 control. MethylSeq and RNAseq analyses were performed on both spaceflight grown samples and ground grown controls. In addition, the epigenetics effects that may contribute to the differential gene expression patterns observed between leaf and root tissues were also investigated in an organ-specific manner.
2021-08-13 | GSE118483 | GEO
Project description:Whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) are widely used for measuring DNA methylation levels on a genome-wide scale. Both methods have limitations: WGBS is expensive and prohibitive for most large-scale projects; RRBS only interrogates 6-12% of the CpGs in the human genome. Here, we introduce methylation-sensitive restriction enzyme bisulfite sequencing (MREBS) which has the reduced sequencing requirements of RRBS, but significantly expands the coverage of CpG sites in the genome. We built a multiple regression model that combines the two features of MREBS: the bisulfite conversion ratios of single cytosines (as in WGBS and RRBS) as well as the number of reads that cover each locus (as in MRE-seq). This combined approach allowed us to estimate differential methylation across 60% of the genome using read count data alone, and where counts were sufficiently high in both samples (about 1.5% of the genome), our estimates were significantly improved by the single CpG conversion information. We show that differential DNA methylation values based on MREBS data correlate well with those based on WGBS and RRBS. This newly developed technique combines the sequencing cost of RRBS and DNA methylation estimates on a portion of the genome similar to WGBS, making it ideal for large-scale projects of mammalian genomes.
| S-EPMC6448829 | biostudies-literature
Project description:Transgenerationally heritable epialleles are defined by the stable propagation of alternative transcriptional states through mitotic and meiotic cell cycles. Given that the propagation of DNA methylation at CpG sites, mediated in Arabidopsis by MET1, plays a central role in epigenetic inheritance, we examined genome-wide DNA methylation in partial and complete loss-of-function met1 mutants. We interpreted the data in relation to transgenerational epiallelic stability and provide evidence that DNA sequence features such as density of CpGs and genomic repetitiveness of the loci largely determine their susceptibility to epiallelic switching. The importance of this rule was confirmed by analyses of common epialleles in natural Arabidopsis accessions and verified in rice.
2017-01-09 | GSE89591 | GEO