Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

MOABS: model based analysis of bisulfite sequencing data.

ABSTRACT: Bisulfite sequencing (BS-seq) is the gold standard for studying genome-wide DNA methylation. We developed MOABS to increase the speed, accuracy, statistical power and biological relevance of BS-seq data analysis. MOABS detects differential methylation with 10-fold coverage at single-CpG resolution based on a Beta-Binomial hierarchical model and is capable of processing two billion reads in 24 CPU hours. Here, using simulated and real BS-seq data, we demonstrate that MOABS outperforms other leading algorithms, such as Fisher's exact test and BSmooth. Furthermore, MOABS analysis can be easily extended to differential 5hmC analysis using RRBS and oxBS-seq. MOABS is available at http://code.google.com/p/moabs/.

SUBMITTER: Sun D

PROVIDER: S-EPMC4054608 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

MOABS: model based analysis of bisulfite sequencing data.

Sun Deqiang D Xi Yuanxin Y Rodriguez Benjamin B Park Hyun Jung HJ Tong Pan P Meong Mira M Goodell Margaret A MA Li Wei W

Genome biology 20140224 2

Bisulfite sequencing (BS-seq) is the gold standard for studying genome-wide DNA methylation. We developed MOABS to increase the speed, accuracy, statistical power and biological relevance of BS-seq data analysis. MOABS detects differential methylation with 10-fold coverage at single-CpG resolution based on a Beta-Binomial hierarchical model and is capable of processing two billion reads in 24 CPU hours. Here, using simulated and real BS-seq data, we demonstrate that MOABS outperforms other leadi ...[more]

PMID: 24565500

Similar Datasets

WBSA: web service for bisulfite sequencing data analysis.

Project description:Whole-Genome Bisulfite Sequencing (WGBS) and genome-wide Reduced Representation Bisulfite Sequencing (RRBS) are widely used to study DNA methylation. However, data analysis is complicated, lengthy, and hampered by a lack of seamless analytical pipelines. To address these issues, we developed a convenient, stable, and efficient web service called Web Service for Bisulfite Sequencing Data Analysis (WBSA) to analyze bisulfate sequencing data. WBSA focuses on not only CpG methylation, which is the most common biochemical modification in eukaryotic DNA, but also non-CG methylation, which have been observed in plants, iPS cells, oocytes, neurons and stem cells of human. WBSA comprises three main modules as follows: WGBS data analysis, RRBS data analysis, and differentially methylated region (DMR) identification. The WGBS and RRBS modules execute read mapping, methylation site identification, annotation, and advanced analysis, whereas the DMR module identifies actual DMRs and annotates their correlations to genes. WBSA can be accessed and used without charge either online or local version. WBSA also includes the executables of the Portable Batch System (PBS) and standalone versions that can be downloaded from the website together with the installation instructions. WBSA is available at no charge for academic users at http://wbsa.big.ac.cn.

| S-EPMC3907392 | biostudies-literature

BiSulfite Bolt: A bisulfite sequencing analysis platform.

Project description:Bisulfite sequencing is commonly used to measure DNA methylation. Processing bisulfite sequencing data is often challenging owing to the computational demands of mapping a low-complexity, asymmetrical library and the lack of a unified processing toolset to produce an analysis-ready methylation matrix from read alignments. To address these shortcomings, we have developed BiSulfite Bolt (BSBolt), a fast and scalable bisulfite sequencing analysis platform. BSBolt performs a pre-alignment sequencing read assessment step to improve efficiency when handling asymmetrical bisulfite sequencing libraries. We evaluated BSBolt against simulated and real bisulfite sequencing libraries. We found that BSBolt provides accurate and fast bisulfite sequencing alignments and methylation calls. We also compared BSBolt to several existing bisulfite alignment tools and found BSBolt outperforms Bismark, BSSeeker2, BISCUIT, and BWA-Meth based on alignment accuracy and methylation calling accuracy. BSBolt offers streamlined processing of bisulfite sequencing data through an integrated toolset that offers support for simulation, alignment, methylation calling, and data aggregation. BSBolt is implemented as a Python package and command line utility for flexibility when building informatics pipelines. BSBolt is available at https://github.com/NuttyLogic/BSBolt under an MIT license.

| S-EPMC8106542 | biostudies-literature

HBS-Tools for Hairpin Bisulfite Sequencing Data Processing and Analysis.

Project description:The emerging genome-wide hairpin bisulfite sequencing (hairpin-BS-Seq) technique enables the determination of the methylation pattern for DNA double strands simultaneously. Compared with traditional bisulfite sequencing (BS-Seq) techniques, hairpin-BS-Seq can determine methylation fidelity and increase mapping efficiency. However, no computational tool has been designed for the analysis of hairpin-BS-Seq data yet. Here we present HBS-tools, a set of command line based tools for the preprocessing, mapping, methylation calling, and summarizing of genome-wide hairpin-BS-Seq data. It accepts paired-end hairpin-BS-Seq reads to recover the original (pre-bisulfite-converted) sequences using global alignment and then calls the methylation statuses for cytosines on both DNA strands after mapping the original sequences to the reference genome. After applying to hairpin-BS-Seq datasets, we found that HBS-tools have a reduced mapping time and improved mapping efficiency compared with state-of-the-art mapping tools. The HBS-tools source scripts, along with user guide and testing data, are freely available for download.

| S-EPMC4698518 | biostudies-literature

BiSpark: a Spark-based highly scalable aligner for bisulfite sequencing data.

Project description:BACKGROUND:Bisulfite sequencing is one of the major high-resolution DNA methylation measurement method. Due to the selective nucleotide conversion on unmethylated cytosines after treatment with sodium bisulfite, processing bisulfite-treated sequencing reads requires additional steps which need high computational demands. However, a dearth of efficient aligner that is designed for bisulfite-treated sequencing becomes a bottleneck of large-scale DNA methylome analyses. RESULTS:In this study, we present a highly scalable, efficient, and load-balanced bisulfite aligner, BiSpark, which is designed for processing large volumes of bisulfite sequencing data. We implemented the BiSpark algorithm over the Apache Spark, a memory optimized distributed data processing framework, to achieve the maximum data parallel efficiency. The BiSpark algorithm is designed to support redistribution of imbalanced data to minimize delays on large-scale distributed environment. CONCLUSIONS:Experimental results on methylome datasets show that BiSpark significantly outperforms other state-of-the-art bisulfite sequencing aligners in terms of alignment speed and scalability with respect to dataset size and a number of computing nodes while providing highly consistent and comparable mapping results. AVAILABILITY:The implementation of BiSpark software package and source code is available at https://github.com/bhi-kimlab/BiSpark/ .

| S-EPMC6288881 | biostudies-literature

Investigating bisulfite short-read mapping failure with hairpin bisulfite sequencing data.

Project description:DNA methylation is an important epigenetic mark relevant to normal development and disease genesis. A common approach to characterizing genome-wide DNA methylation is using Next Generation Sequencing technology to sequence bisulfite treated DNA. The short sequence reads are mapped to the reference genome to determine the methylation statuses of Cs. However, despite intense effort, a much smaller proportion of the reads derived from bisulfite treated DNA (usually about 40-80%) can be mapped than regular short reads mapping (> 90%), and it is unclear what factors lead to this low mapping efficiency.To address this issue, we used the hairpin bisulfite sequencing technology to determine sequences of both DNA double strands simultaneously. This enabled the recovery of the original non-bisulfite-converted sequences. We used Bismark for bisulfite read mapping and Bowtie2 for recovered read mapping. We found that recovering the reads improved unique mapping efficiency by 9-10% compared to the bisulfite reads. Such improvement in mapping efficiency is related to sequence entropy.The hairpin recovery technique improves mapping efficiency, and sequence entropy relates to mapping efficiency.

| S-EPMC4652484 | biostudies-literature

Comprehensive Evaluation of Differential Methylation Analysis Methods for Bisulfite Sequencing Data.

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

LuxRep: a technical replicate-aware method for bisulfite sequencing data analysis.

Project description:BackgroundDNA methylation is commonly measured using bisulfite sequencing (BS-seq). The quality of a BS-seq library is measured by its bisulfite conversion efficiency. Libraries with low conversion rates are typically excluded from analysis resulting in reduced coverage and increased costs.ResultsWe have developed a probabilistic method and software, LuxRep, that implements a general linear model and simultaneously accounts for technical replicates (libraries from the same biological sample) from different bisulfite-converted DNA libraries. Using simulations and actual DNA methylation data, we show that including technical replicates with low bisulfite conversion rates generates more accurate estimates of methylation levels and differentially methylated sites. Moreover, using variational inference speeds up computation time necessary for whole genome analysis.ConclusionsIn this work we show that taking into account technical replicates (i.e. libraries) of BS-seq data of varying bisulfite conversion rates, with their corresponding experimental parameters, improves methylation level estimation and differential methylation detection.

| S-EPMC8760685 | 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

NGS-based deep bisulfite sequencing.

Project description:We have developed an NGS-based deep bisulfite sequencing protocol for the DNA methylation analysis of genomes. This approach allows the rapid and efficient construction of NGS-ready libraries with a large number of PCR products that have been individually amplified from bisulfite-converted DNA. This approach also employs a bioinformatics strategy to sort the raw sequence reads generated from NGS platforms and subsequently to derive DNA methylation levels for individual loci. The results demonstrated that this NGS-based deep bisulfite sequencing approach provide not only DNA methylation levels but also informative DNA methylation patterns that have not been seen through other existing methods.•This protocol provides an efficient method generating NGS-ready libraries from individually amplified PCR products.•This protocol provides a bioinformatics strategy sorting NGS-derived raw sequence reads.•This protocol provides deep bisulfite sequencing results that can measure DNA methylation levels and patterns of individual loci.

| S-EPMC4924566 | biostudies-literature

An integrative approach for efficient analysis of whole genome bisulfite sequencing data.

Project description:BackgroundWhole genome bisulfite sequencing (WGBS) is a high-throughput technique for profiling genome-wide DNA methylation at single nucleotide resolution. However, the applications of WGBS are limited by low accuracy resulting from bisulfite-induced damage on DNA fragments. Although many computer programs have been developed for accurate detecting, most of the programs have barely succeeded in improving either quantity or quality of the methylation results. To improve both, we attempted to develop a novel integration of most widely used bisulfite-read mappers: Bismark, BSMAP, and BS-seeker2.ResultsA comprehensive analysis of the three mappers revealed that the mapping results of the mappers were mutually complementary under diverse read conditions. Therefore, we sought to integrate the characteristics of the mappers by scoring them to gain robustness against artifacts. As a result, the integration significantly increased detection accuracy compared with the individual mappers. In addition, the amount of detected cytosine was higher than that by Bismark. Furthermore, the integration successfully reduced the fluctuation of detection accuracy induced by read conditions. We applied the integration to real WGBS samples and succeeded in classifying the samples according to the originated tissues by both CpG and CpH methylation patterns.ConclusionsIn this study, we improved both quality and quantity of methylation results from WGBS data by integrating the mapping results of three bisulfite-read mappers. Also, we succeeded in combining and comparing WGBS samples by reducing the effects of read heterogeneity on methylation detection. This study contributes to DNA methylation researches by improving efficiency of methylation detection from WGBS data and facilitating the comprehensive analysis of public WGBS data.

| S-EPMC4682396 | 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