Dataset Information

EPIC-TABSAT: analysis tool for targeted bisulfite sequencing experiments and array-based methylation studies.

ABSTRACT: DNA methylation is one of the major epigenetic modifications and has frequently demonstrated its suitability as diagnostic and prognostic biomarker. In addition to chip and sequencing based epigenome wide methylation profiling methods, targeted bisulfite sequencing (TBS) has been established as a cost-effective approach for routine diagnostics and target validation applications. Yet, an easy-to-use tool for the analysis of TBS data in combination with array-based methylation results has been missing. Consequently, we have developed EPIC-TABSAT, a user-friendly web-based application for the analysis of targeted sequencing data that additionally allows the integration of array-based methylation results. The tool can handle multiple targets as well as multiple sequencing files in parallel and covers the complete data analysis workflow from calculation of quality metrics to methylation calling and interactive result presentation. The graphical user interface offers an unprecedented way to interpret TBS data alone or in combination with array-based methylation studies. Together with the computation of target-specific epialleles it is useful in validation, research, and routine diagnostic environments. EPIC-TABSAT is freely accessible to all users at https://tabsat.ait.ac.at/.

SUBMITTER: Krainer J

PROVIDER: S-EPMC6602470 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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EPIC-TABSAT: analysis tool for targeted bisulfite sequencing experiments and array-based methylation studies.

Krainer Julie J Weinhäusel Andreas A Hanak Karel K Pulverer Walter W Özen Seza S Vierlinger Klemens K Pabinger Stephan S

Nucleic acids research 20190701 W1

DNA methylation is one of the major epigenetic modifications and has frequently demonstrated its suitability as diagnostic and prognostic biomarker. In addition to chip and sequencing based epigenome wide methylation profiling methods, targeted bisulfite sequencing (TBS) has been established as a cost-effective approach for routine diagnostics and target validation applications. Yet, an easy-to-use tool for the analysis of TBS data in combination with array-based methylation results has been mis ...[more]

PMID: 31106358

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Project description:Cytosine methylation is an important DNA epigenetic modification. In vertebrates, methylation occurs at CpG sites, which are dinucleotides where a cytosine is immediately followed by a guanine in the DNA sequence from 5' to 3'. When located in the promoter region of a gene, DNA methylation is often associated with transcriptional silencing of the gene. Aberrant DNA methylation is associated with the development of various diseases such as cancer. Bisulfite sequencing (BS-seq) is the current "gold-standard" technology for high-resolution profiling of DNA methylation. Reduced representation bisulfite sequencing (RRBS) is an efficient form of BS-seq that targets CpG-rich DNA regions in order to save sequencing costs. A typical bioinformatics aim is to identify CpGs that are differentially methylated (DM) between experimental conditions. This workflow demonstrates that differential methylation analysis of RRBS data can be conducted using software and methodology originally developed for RNA-seq data. The RNA-seq pipeline is adapted to methylation by adding extra columns to the design matrix to account for read coverage at each CpG, after which the RRBS and RNA-seq pipelines are almost identical. This approach is statistically natural and gives analysts access to a rich collection of analysis tools including generalized linear models, gene set testing and pathway analysis. The article presents a complete start to finish case study analysis of RRBS profiles of different cell populations from the mouse mammary gland using the Bioconductor package edgeR. We show that lineage-committed cells are typically hyper-methylated compared to progenitor cells and this is true on all the autosomes but not the sex chromosomes. We demonstrate a strong negative correlation between methylation of promoter regions and gene expression as measured by RNA-seq for the same cell types, showing that methylation is a regulatory mechanism involved in epithelial linear commitment.

Project description:BackgroundThere has been a steady increase in the number of studies aiming to identify DNA methylation differences associated with complex phenotypes. Many of the challenges of epigenetic epidemiology regarding study design and interpretation have been discussed in detail, however there are analytical concerns that are outstanding and require further exploration. In this study we seek to address three analytical issues. First, we quantify the multiple testing burden and propose a standard statistical significance threshold for identifying DNA methylation sites that are associated with an outcome. Second, we establish whether linear regression, the chosen statistical tool for the majority of studies, is appropriate and whether it is biased by the underlying distribution of DNA methylation data. Finally, we assess the sample size required for adequately powered DNA methylation association studies.ResultsWe quantified DNA methylation in the Understanding Society cohort (n = 1175), a large population based study, using the Illumina EPIC array to assess the statistical properties of DNA methylation association analyses. By simulating null DNA methylation studies, we generated the distribution of p-values expected by chance and calculated the 5% family-wise error for EPIC array studies to be 9 × 10- 8. Next, we tested whether the assumptions of linear regression are violated by DNA methylation data and found that the majority of sites do not satisfy the assumption of normal residuals. Nevertheless, we found no evidence that this bias influences analyses by increasing the likelihood of affected sites to be false positives. Finally, we performed power calculations for EPIC based DNA methylation studies, demonstrating that existing studies with data on ~ 1000 samples are adequately powered to detect small differences at the majority of sites.ConclusionWe propose that a significance threshold of P < 9 × 10- 8 adequately controls the false positive rate for EPIC array DNA methylation studies. Moreover, our results indicate that linear regression is a valid statistical methodology for DNA methylation studies, despite the fact that the data do not always satisfy the assumptions of this test. These findings have implications for epidemiological-based studies of DNA methylation and provide a framework for the interpretation of findings from current and future studies.

Project description:Background:DNA methylation has been implicated as a promising biomarker for precise cancer diagnosis. However, limited DNA methylation-based biomarkers have been described in esophageal squamous cell carcinoma (ESCC). Methods:A high-throughput DNA methylation dataset (100 samples) of ESCC from The Cancer Genome Atlas (TCGA) project was analyzed and validated along with another independent dataset (12 samples) from the Gene Expression Omnibus (GEO) database. The methylation status of peripheral blood mononuclear cells and peripheral blood leukocytes from healthy controls was also utilized for biomarker selection. The candidate CpG sites as well as their adjacent regions were further validated in 94 pairs of ESCC tumor and adjacent normal tissues from the Chinese Han population using the targeted bisulfite sequencing method. Logistic regression and several machine learning methods were applied for evaluation of the diagnostic ability of our panel. Results:In the discovery stage, five hyper-methylated CpG sites were selected as candidate biomarkers for further analysis as shown below: cg15830431, P?=?2.20?×?10-4; cg19396867, P?=?3.60?×?10-4; cg20655070, P?=?3.60?×?10-4; cg26671652, P?=?5.77?×?10-4; and cg27062795, P?=?3.60?×?10-4. In the validation stage, the methylation status of both the five CpG sites and their adjacent genomic regions were tested. The diagnostic model based on the combination of these five genomic regions yielded a robust performance (sensitivity?=?0.75, specificity?=?0.88, AUC?=?0.85). Eight statistical models along with five-fold cross-validation were further applied, in which the SVM model reached the best accuracy in both training and test dataset (accuracy?=?0.82 and 0.80, respectively). In addition, subgroup analyses revealed a significant difference in diagnostic performance between the alcohol use and non-alcohol use subgroups. Conclusions:Methylation profiles of the five genomic regions covering cg15830431 (STK3), cg19396867, cg20655070, cg26671652 (ZNF418), and cg27062795 (ZNF542) can be used for effective methylation-based testing for ESCC diagnosis.

Project description:Targeted sequencing of PCR amplicons generated from bisulfite deaminated DNA is a flexible, cost-effective way to study methylation of a sample at single CpG resolution and perform subsequent multi-target, multi-sample comparisons. Currently, no platform specific protocol, support, or analysis solution is provided to perform targeted bisulfite sequencing on a Personal Genome Machine (PGM). Here, we present a novel tool, called TABSAT, for analyzing targeted bisulfite sequencing data generated on Ion Torrent sequencers. The workflow starts with raw sequencing data, performs quality assessment, and uses a tailored version of Bismark to map the reads to a reference genome. The pipeline visualizes results as lollipop plots and is able to deduce specific methylation-patterns present in a sample. The obtained profiles are then summarized and compared between samples. In order to assess the performance of the targeted bisulfite sequencing workflow, 48 samples were used to generate 53 different Bisulfite-Sequencing PCR amplicons from each sample, resulting in 2,544 amplicon targets. We obtained a mean coverage of 282X using 1,196,822 aligned reads. Next, we compared the sequencing results of these targets to the methylation level of the corresponding sites on an Illumina 450k methylation chip. The calculated average Pearson correlation coefficient of 0.91 confirms the sequencing results with one of the industry-leading CpG methylation platforms and shows that targeted amplicon bisulfite sequencing provides an accurate and cost-efficient method for DNA methylation studies, e.g., to provide platform-independent confirmation of Illumina Infinium 450k methylation data. TABSAT offers a novel way to analyze data generated by Ion Torrent instruments and can also be used with data from the Illumina MiSeq platform. It can be easily accessed via the Platomics platform, which offers a web-based graphical user interface along with sample and parameter storage. TABSAT is freely available under a GNU General Public License version 3.0 (GPLv3) at https://github.com/tadkeys/tabsat/ and http://demo.platomics.com/.

Dataset Information

EPIC-TABSAT: analysis tool for targeted bisulfite sequencing experiments and array-based methylation studies.

Publications

EPIC-TABSAT: analysis tool for targeted bisulfite sequencing experiments and array-based methylation studies.

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