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.

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.

Project description:Muscle mass and strength reduce during ageing with a steeper decline in women around the age of 50, which suggests that the menopausal transition is one of the mechanisms for age-induced neuromuscular deteriorations. At the moment, there is no clear explanation for the decline in muscle properties although both physical exercise and maintaining near premenopausal estrogen levels by hormonal replacement have been helpful in their preservation. However, the molecular consequences of both treatments in skeletal muscle are mostly unknown. The goal of this genome-wide study was to investigate the transcriptional networks through which power training (PT) comprising of jumping activities and estrogen containing hormone replacement therapy (HRT) may aid in the prevention of functional limitation ensued from insufficient muscle mass and physical performance after menopause. We used m. vastus lateralis samples obtained from postmenopausal women participating in a yearlong randomized double-blinded placebo-controlled trial with PT and HRT interventions. Muscle samples were available from eight PT, ten HRT and nine control women. Women in the PT group participated in a progressive PT program comprising mostly of jumping exercises for lower limbs with additional resistance exercises for the upper body. HRT intervention was double-blinded. All participants used either estradiol/noretisterone acetate preparation or placebo. Using statistical threshold p-value <0.05 and |fold change| >1.2 we identified 665 genes being differentially expressed among the studied 50-57-yr-old postmenopausal women. We used the hierarchical clustering method with extensive enrichment analysis in order to reveal enrichment of known GO or KEGG functional categories among co-regulated gene-clusters. In the enrichment analysis false discovery rate was set to be less than 15%. Transcription of “response to contraction”-, “insulin signaling”- and “adipocytokine signaling”-genes were upregulated by PT. Both PT and HRT were similarly effective on transcriptional regulation of glycolytic energy metabolism and calcium signaling. The HRT specifically affected “mitochondrion”-genes. In addition, we identified functional categories, such as “muscle development”, which were changed during the trial in all study groups. Our study revealed transcriptional changes in several functional categories in thigh muscle tissue of early postmenopausal women. The results emphasize that during the first years of the postmenopausal period, muscle properties are under transcriptional modulation, which both PT and HRT partially counteract leading to preservation of muscle mass and performance. More specifically, our findings indicate that PT and HRT may function through improving energy metabolism, improving the response to contraction, preserving functionality of mitochondria and reducing reorganization of muscle tissue. Therefore these treatments are recommended for preservation of adequate muscle mass and function. 54 samples, which formed 3 study groups with 2 timepoints, i.e., 8 power training subjects, 10 hormone replacement subjects and 9 controls. 6 control subjects had replicated samples resulting in a total of 60 hybridizations.

Dataset Information

Characterizing the properties of bisulfite sequencing data: maximizing power and sensitivity to identify differences in DNA methylation [Array]

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