Comparison of enzymatic and bisulfite-based methods for sequencing-based cell-free DNA methylation profiling
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE306695 | GEO | 2025/12/31
REPOSITORIES: GEO
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Project description:Comparison of enzymatic and bisulfite-based methods for sequencing-based cell-free DNA methylation profiling
| PRJNA1311825 | ENA
Project description:Targeted quantification of DNA methylation allows for interrogation of the most informative loci across many samples quickly and cost-effectively. Here we report improved bisulfite padlock probes (BSPPs) with a design algorithm to generate efficient padlock probes, a library-free protocol that dramatically reduces sample-preparation cost and time and is compatible with automation, and an efficient bioinformatics pipeline to accurately obtain both methylation levels and genotypes from sequencing of bisulfite-converted DNA.
| S-EPMC3461232 | biostudies-literature
Project description:Different methodological approaches are available to assess DNA methylation biomarkers. In this study, we evaluated two sodium bisulfite conversion-dependent methods, namely pyrosequencing and methylation-specific qPCR (MS-qPCR), with the aim of measuring the closeness of agreement of methylation values between these two methods and its effect when setting a cut-off. Methylation of tumor suppressor gene p16/INK4A was evaluated in 80 lung cancer patients from which cytological lymph node samples were obtained. Cluster analyses were used to establish methylated and unmethylated groups for each method. Agreement and concordance between pyrosequencing and MS-qPCR was evaluated with Pearson's correlation, Bland-Altman, Cohen's kappa index and ROC curve analyses. Based on these analyses, cut-offs were derived for MS-qPCR. An acceptable correlation (Pearson's R2 = 0.738) was found between pyrosequencing (PYRmean) and MS-qPCR (NMP; normalized methylation percentage), providing similar clinical results when categorizing data as binary using cluster analysis. Compared to pyrosequencing, MS-qPCR tended to underestimate methylation for values between 0 and 15%, while for methylation >30% overestimation was observed. The estimated cut-off for MS-qPCR data based on cluster analysis, kappa-index agreement and ROC curve analysis were much lower than that derived from pyrosequencing. In conclusion, our results indicate that independently of the approach used for estimating the cut-off, the methylation percentage obtained through MS-qPCR is lower than that calculated for pyrosequencing. These differences in data and therefore in the cut-off should be examined when using methylation biomarkers in the clinical practice.
| S-EPMC7730915 | biostudies-literature
Project description:Background: With advances in next-generation sequencing technologies, the bisulfite conversion of genomic DNA followed by sequencing has become the predominant technique for quantifying genome-wide DNA methylation at single-base resolution. A large number of computational approaches are available in literature for identifying differentially methylated regions in bisulfite sequencing data, and more are being developed continuously. Results: Here, we focused on a comprehensive evaluation of commonly used differential methylation analysis methods and describe the potential strengths and limitations of each method. We found that there are large differences among methods, and no single method consistently ranked first in all benchmarking. Moreover, smoothing seemed not to improve the performance greatly, and a small number of replicates created more difficulties in the computational analysis of BS-seq data than low sequencing depth. Conclusions: Data analysis and interpretation should be performed with great care, especially when the number of replicates or sequencing depth is limited.
| S-EPMC8345583 | biostudies-literature
Project description:Liquid biopsy based on cell-free DNA (cfDNA) enables non-invasive dynamic assessment of disease status in patients with cancer, both in the early and advanced settings. The analysis of DNA-methylation (DNAm) from cfDNA samples holds great promise due to the intrinsic characteristics of DNAm being more prevalent, pervasive, and cell- and tumor-type specific than genomics, for which established cfDNA assays already exist. Herein, we report on recent advances on experimental strategies for the analysis of DNAm in cfDNA samples. We describe the main steps of DNAm-based analysis workflows, including pre-analytics of cfDNA samples, DNA treatment, assays for DNAm evaluation, and methods for data analysis. We report on protocols, biomolecular techniques, and computational strategies enabling DNAm evaluation in the context of cfDNA analysis, along with practical considerations on input sample requirements and costs. We provide an overview on existing studies exploiting cell-free DNAm biomarkers for the detection and monitoring of cancer in early and advanced settings, for the evaluation of drug resistance, and for the identification of the cell-of-origin of tumors. Finally, we report on DNAm-based tests approved for clinical use and summarize their performance in the context of liquid biopsy.
| S-EPMC7765488 | biostudies-literature
Project description:Single-cell multi-omics are powerful means to study cell-to-cell heterogeneity. Here, we present a single-tube, bisulfite-free method for the simultaneous, genome-wide analysis of DNA methylation and genetic variants in single cells: epigenomics and genomics of single cells analyzed by restriction (epi-gSCAR). By applying this method, we obtained DNA methylation measurements of up to 506,063 CpGs and up to 1,244,188 single-nucleotide variants from single acute myeloid leukemia-derived cells. We demonstrate that epi-gSCAR generates accurate and reproducible measurements of DNA methylation and allows to differentiate between cell lines based on the DNA methylation and genetic profiles.
| S-EPMC7851132 | biostudies-literature
Project description:Whole Genome Bisulfite Sequencing (WGBS) is the current standard for DNA methylation profiling. However, this approach is costly as it requires sequencing coverage over the entire genome. Here we introduce Anchor-Based Bisulfite Sequencing (ABBS). ABBS captures accurate DNA methylation information in Escherichia coli and mammals, while requiring up to 10 times fewer sequencing reads than WGBS. ABBS interrogates the entire genome and is not restricted to the CpG islands assayed by methods like Reduced Representation Bisulfite Sequencing (RRBS). The ABBS protocol is simple and can be performed in a single day.
| S-EPMC9203462 | biostudies-literature
Project description:BackgroundcfMeDIP-seq is a low-cost method for determining the DNA methylation status of cell-free DNA and it has been successfully combined with statistical methods for accurate cancer diagnostics. We investigate the diagnostic classification aspect by applying statistical tests and dimension reduction techniques for feature selection and probabilistic modeling for the cancer type classification, and we also study the effect of sequencing depth.MethodsWe experiment with a variety of statistical methods that use different feature selection and feature extraction methods as well as probabilistic classifiers for diagnostic decision making. We test the (moderated) t-tests and the Fisher's exact test for feature selection, principal component analysis (PCA) as well as iterative supervised PCA (ISPCA) for feature generation, and GLMnet and logistic regression methods with sparsity promoting priors for classification. Probabilistic programming language Stan is used to implement Bayesian inference for the probabilistic models.Results and conclusionsWe compare overlaps of differentially methylated genomic regions as chosen by different feature selection methods, and evaluate probabilistic classifiers by evaluating the area under the receiver operating characteristic scores on discovery and validation cohorts. While we observe that many methods perform equally well as, and occasionally considerably better than, GLMnet that was originally proposed for cfMeDIP-seq based cancer classification, we also observed that performance of different methods vary across sequencing depths, cancer types and study cohorts. Overall, methods that seem robust and promising include Fisher's exact test and ISPCA for feature selection as well as a simple logistic regression model with the number of hyper and hypo-methylated regions as features.
| S-EPMC8978416 | biostudies-literature
Project description:DNA methylation in higher organisms has become an expanding field of study as it often involves the regulation of gene expression. Although Whole Genome Bisulfite Sequencing (WG-BS) based on next-generation sequencing (NGS) is the most versatile method, this is a costly technique that lacks in-depth analytic power. There are no conventional methods based on NGS that enable researchers to easily compare the level of DNA methylation from the practical number of samples handled in the laboratory. Although the targeted BS method based on Sanger sequencing is generally used in this case, it lacks in-depth analytic power. Therefore, we propose a new method that combines the high throughput analytic power of NGS and bioinformatics with the specificity and focus offered by PCR-amplification-based bisulfite sequencing methods. We use in silico size sieving of DNA-fragments and primer matchings instead of whole-fragment alignment in our bioinformatics analyses, and named our method SIMON (Simple Inference for Methylome based On NGS). The results of our targeted BS method based on NGS (SIMON method) show that small variations in DNA methylation patterns can be precisely and efficiently measured at a single nucleotide resolution. SIMON method combines pre-existing techniques to provide a cost-effective technique for in-depth studies that focus on pre-identified loci. It offers significant improvements with regard to workflow and the quality of the acquired DNA methylation information. Because of the high accuracy of the analysis, small variations of DNA methylation levels can be precisely determined even with large numbers of samples and loci.
| S-EPMC6978500 | biostudies-literature
Project description:Sequencing-based approaches have led to new insights about DNA methylation. While many different techniques for genome-scale mapping of DNA methylation have been employed, throughput has been a key limitation for most. To further facilitate the mapping of DNA methylation, we describe a protocol for gel-free multiplexed reduced representation bisulfite sequencing (mRRBS) that reduces the workload dramatically and enables processing of 96 or more samples per week. mRRBS achieves similar CpG coverage to the original RRBS protocol, while the higher throughput and lower cost make it better suited for large-scale DNA methylation mapping studies, including cohorts of cancer samples.
| S-EPMC3491420 | biostudies-literature