Browse
Submit Data
Databases
API
Help

Dataset Information

12 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation.

ABSTRACT: Identifying disease-associated changes in DNA methylation can help us gain a better understanding of disease etiology. Bisulfite sequencing allows the generation of high-throughput methylation profiles at single-base resolution of DNA. However, optimally modeling and analyzing these sparse and discrete sequencing data is still very challenging due to variable read depth, missing data patterns, long-range correlations, data errors, and confounding from cell type mixtures. We propose a regression-based hierarchical model that allows covariate effects to vary smoothly along genomic positions and we have built a specialized EM algorithm, which explicitly allows for experimental errors and cell type mixtures, to make inference about smooth covariate effects in the model. Simulations show that the proposed method provides accurate estimates of covariate effects and captures the major underlying methylation patterns with excellent power. We also apply our method to analyze data from rheumatoid arthritis patients and controls. The method has been implemented in R package SOMNiBUS.

SUBMITTER: Zhao K

PROVIDER: S-EPMC8359306 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

MethVisual - visualization and exploratory statistical analysis of DNA methylation profiles from bisulfite sequencing.

Project description:BackgroundExploration of DNA methylation and its impact on various regulatory mechanisms has become a very active field of research. Simultaneously there is an arising need for tools to process and analyse the data together with statistical investigation and visualisation.FindingsMethVisual is a new application that enables exploratory analysis and intuitive visualization of DNA methylation data as is typically generated by bisulfite sequencing. The package allows the import of DNA methylation sequences, aligns them and performs quality control comparison. It comprises basic analysis steps as lollipop visualization, co-occurrence display of methylation of neighbouring and distant CpG sites, summary statistics on methylation status, clustering and correspondence analysis. The package has been developed for methylation data but can be also used for other data types for which binary coding can be inferred. The application of the package, as well as a comparison to existing DNA methylation analysis tools and its workflow based on two datasets is presented in this paper.ConclusionsThe R package MethVisual offers various analysis procedures for data that can be binarized, in particular for bisulfite sequenced methylation data. R/Bioconductor has become one of the most important environments for statistical analysis of various types of biological and medical data. Therefore, any data analysis within R that allows the integration of various data types as provided from different technological platforms is convenient. It is the first and so far the only specific package for DNA methylation analysis, in particular for bisulfite sequenced data available in R/Bioconductor enviroment. The package is available for free at http://methvisual.molgen.mpg.de/ and from the Bioconductor Consortium http://www.bioconductor.org.

| S-EPMC3012040 | biostudies-literature

epiG: statistical inference and profiling of DNA methylation from whole-genome bisulfite sequencing data.

Project description:The study of epigenetic heterogeneity at the level of individual cells and in whole populations is the key to understanding cellular differentiation, organismal development, and the evolution of cancer. We develop a statistical method, epiG, to infer and differentiate between different epi-allelic haplotypes, annotated with CpG methylation status and DNA polymorphisms, from whole-genome bisulfite sequencing data, and nucleosome occupancy from NOMe-seq data. We demonstrate the capabilities of the method by inferring allele-specific methylation and nucleosome occupancy in cell lines, and colon and tumor samples, and by benchmarking the method against independent experimental data.

| S-EPMC5320668 | biostudies-literature

DNA methylation estimation using methylation-sensitive restriction enzyme bisulfite sequencing (MREBS).

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

Anchor-based bisulfite sequencing determines genome-wide DNA methylation

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. Anchor-Based Bisulfite Sequencing (ABBS) is a sequencing based method that enables analysis of DNA methylation states, based on specific hybridization of bisulfite-refractory methylcytosines with 8-aza-7-deaza-2-deoxyguanosine, incorporated in a primer at the end of 5 random nucleotides.

| S-EPMC9203462 | biostudies-literature

GENE-SWITCH Pig DNA methylation with Whole Genome bisulfite sequencing (WGBS)

Project description: Not available

| S-BSST612 | biostudies-other

GENE-SWITCH Pig DNA methylation with Reduced representation bisulfite sequencing (RRBS)

Project description: Not available

| S-BSST615 | biostudies-other

GENE-SWITCH Chicken DNA methylation with Whole Genome bisulfite sequencing (WGBS)

Project description: Not available

| S-BSST618 | biostudies-other

GENE-SWITCH Chicken DNA methylation with Reduced representation bisulfite sequencing (RRBS)

Project description: Not available

| S-BSST613 | biostudies-other

BatMeth: improved mapper for bisulfite sequencing reads on DNA methylation.

Project description:DNA methylation plays a crucial role in higher organisms. Coupling bisulfite treatment with next generation sequencing enables the interrogation of 5-methylcytosine sites in the genome. However, bisulfite conversion introduces mismatches between the reads and the reference genome, which makes mapping of Illumina and SOLiD reads slow and inaccurate. BatMeth is an algorithm that integrates novel Mismatch Counting, List Filtering, Mismatch Stage Filtering and Fast Mapping onto Two Indexes components to improve unique mapping rate, speed and precision. Experimental results show that BatMeth is faster and more accurate than existing tools. BatMeth is freely available at http://code.google.com/p/batmeth/.

| S-EPMC3491410 | biostudies-literature

Genome-wide quantitative analysis of DNA methylation from bisulfite sequencing data.

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

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data