Differential methylation analysis for bisulfite sequencing using DSS.
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ABSTRACT:
SUBMITTER: Feng H
PROVIDER: S-EPMC8171293 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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.
| S-EPMC5747346 | 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:BackgroundWhole-genome bisulfite sequencing currently provides the highest-precision view of the epigenome, with quantitative information about populations of cells down to single nucleotide resolution. Several studies have demonstrated the value of this precision: meaningful features that correlate strongly with biological functions can be found associated with only a few CpG sites. Understanding the role of DNA methylation, and more broadly the role of DNA accessibility, requires that methylation differences between populations of cells are identified with extreme precision and in complex experimental designs.ResultsIn this work we investigated the use of beta-binomial regression as a general approach for modeling whole-genome bisulfite data to identify differentially methylated sites and genomic intervals.ConclusionsThe regression-based analysis can handle medium- and large-scale experiments where it becomes critical to accurately model variation in methylation levels between replicates and account for influence of various experimental factors like cell types or batch effects.
| S-EPMC4230021 | biostudies-literature
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:DNA methylation plays a major role in the epigenetic regulation of gene expression. Although a few DNA methylation profiling studies of porcine genome which is one of the important biomedical models for human diseases have been reported, the available data are still limited. We tried to study methylation patterns of diverse pig tissues as a study of the International Swine Methylome Consortium to generate the swine reference methylome map to extensively evaluate the methylation profile of the pig genome at a single base resolution. We generated and analysed the DNA methylome profiles of five different tissues and a cell line originated from pig. On average, 39.85 and 62.1% of cytosine and guanine dinucleotides (CpGs) of CpG islands and 2 kb upstream of transcription start sites were covered, respectively. We detected a low rate (an average of 1.67%) of non-CpG methylation in the six samples except for the neocortex (2.3%). The observed global CpG methylation patterns of pigs indicated high similarity to other mammals including humans. The percentage of CpG methylation associated with gene features was similar among the tissues but not for a 3D4/2 cell line. Our results provide essential information for future studies of the porcine epigenome.
| S-EPMC4596400 | biostudies-literature
Project description:BackgroundDNA methylation has important roles in the regulation of gene expression and cellular specification. Reduced representation bisulfite sequencing (RRBS) has prevailed in methylation studies due to its cost-effectiveness and single-base resolution. The rapid accumulation of RRBS data demands well designed analytical tools.FindingsTo streamline the data processing of DNA methylation from multiple RRBS samples, we present a flexible pipeline named SMAP, whose features include: (i) handling of single-and/or paired-end diverse bisulfite sequencing data with reduced false-positive rates in differentially methylated regions; (ii) detection of allele-specific methylation events with improved algorithms; (iii) a built-in pipeline for detection of novel single nucleotide polymorphisms (SNPs); (iv) support of multiple user-defined restriction enzymes; (v) conduction of all methylation analyses in a single-step operation when well configured.ConclusionsSimulation and experimental data validated the high accuracy of SMAP for SNP detection and methylation identification. Most analyses required in methylation studies (such as estimation of methylation levels, differentially methylated cytosine groups, and allele-specific methylation regions) can be executed readily with SMAP. All raw data from diverse samples could be processed in parallel and 'packetized' streams. A simple user guide to the methylation applications is also provided.
| S-EPMC4488126 | biostudies-literature
Project description:DNA methylation is one of the most extensively studied epigenetic modifications of genomic DNA. In recent years, sequencing of bisulfite-converted DNA, particularly via next-generation sequencing technologies, has become a widely popular method to study DNA methylation. This method can be readily applied to a variety of species, dramatically expanding the scope of DNA methylation studies beyond the traditionally studied human and mouse systems. In parallel to the increasing wealth of genomic methylation profiles, many statistical tools have been developed to detect differentially methylated loci (DMLs) or differentially methylated regions (DMRs) between biological conditions. We discuss and summarize several key properties of currently available tools to detect DMLs and DMRs from sequencing of bisulfite-converted DNA. However, the majority of the statistical tools developed for DML/DMR analyses have been validated using only mammalian data sets, and less priority has been placed on the analyses of invertebrate or plant DNA methylation data. We demonstrate that genomic methylation profiles of non-mammalian species are often highly distinct from those of mammalian species using examples of honey bees and humans. We then discuss how such differences in data properties may affect statistical analyses. Based on these differences, we provide three specific recommendations to improve the power and accuracy of DML and DMR analyses of invertebrate data when using currently available statistical tools. These considerations should facilitate systematic and robust analyses of DNA methylation from diverse species, thus advancing our understanding of DNA methylation.
| S-EPMC6357555 | biostudies-literature
Project description:BackgroundDNA methylation is an epigenetic regulatory form that plays an important role in regulating the gene expression and the tissues development.. However, DNA methylation regulators involved in sheep muscle development remain unclear. To explore the functional importance of genome-scale DNA methylation during sheep muscle growth, this study systematically investigated the genome-wide DNA methylation profiles at key stages of Hu sheep developmental (fetus and adult) using deep whole-genome bisulfite sequencing (WGBS).ResultsOur study found that the expression levels of DNA methyltransferase (DNMT)-related genes were lower in fetal muscle than in the muscle of adults. The methylation levels in the CG context were higher than those in the CHG and CHH contexts, and methylation levels were highest in introns, followed by exons and downstream regions. Subsequently, we identified 48,491, 17, and 135 differentially methylated regions (DMRs) in the CG, CHG, and CHH sequence contexts and 11,522 differentially methylated genes (DMGs). The results of bisulfite sequencing PCR (BSP) correlated well with the WGBS-Seq data. Moreover, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional annotation analysis revealed that some DMGs were involved in regulating skeletal muscle development and fatty acid metabolism. By combining the WGBS-Seq and previous RNA-Seq data, a total of 159 overlap genes were obtained between differentially expressed genes (DEGs) and DMGs (FPKM >?10 and fold change >?4). Finally, we found that 9 DMGs were likely to be involved in muscle growth and metabolism of Hu sheep.ConclusionsWe systemically studied the global DNA methylation patterns of fetal and adult muscle development in Hu sheep, which provided new insights into a better understanding of the epigenetic regulation of sheep muscle development.
| S-EPMC7191724 | biostudies-literature
Project description:Whole-genome bisulfite sequencing (WGBS) allows genome-wide DNA methylation profiling, but the associated high sequencing costs continue to limit its widespread application. We used several high-coverage reference data sets to experimentally determine minimal sequencing requirements. We present data-derived recommendations for minimum sequencing depth for WGBS libraries, highlight what is gained with increasing coverage and discuss the trade-off between sequencing depth and number of assayed replicates.
| S-EPMC4344394 | biostudies-literature
Project description:UnlabelledHere we present the open-source R/Bioconductor software package BEAT (BS-Seq Epimutation Analysis Toolkit). It implements all bioinformatics steps required for the quantitative high-resolution analysis of DNA methylation patterns from bisulfite sequencing data, including the detection of regional epimutation events, i.e. loss or gain of DNA methylation at CG positions relative to a reference. Using a binomial mixture model, the BEAT package aggregates methylation counts per genomic position, thereby compensating for low coverage, incomplete conversion and sequencing errors.Availability and implementationBEAT is freely available as part of Bioconductor at www.bioconductor.org/packages/devel/bioc/html/BEAT.html. The package is distributed under the GNU Lesser General Public License 3.0.
| S-EPMC4071208 | biostudies-literature