MethGo: a comprehensive tool for analyzing whole-genome bisulfite sequencing data.
ABSTRACT: DNA methylation is a major epigenetic modification regulating several biological processes. A standard approach to measure DNA methylation is bisulfite sequencing (BS-Seq). BS-Seq couples bisulfite conversion of DNA with next-generation sequencing to profile genome-wide DNA methylation at single base resolution. The analysis of BS-Seq data involves the use of customized aligners for mapping bisulfite converted reads and the bioinformatic pipelines for downstream data analysis.Here we developed MethGo, a software tool designed for the analysis of data from whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS). MethGo provides both genomic and epigenomic analyses including: 1) coverage distribution of each cytosine; 2) global cytosine methylation level; 3) cytosine methylation level distribution; 4) cytosine methylation level of genomic elements; 5) chromosome-wide cytosine methylation level distribution; 6) Gene-centric cytosine methylation level; 7) cytosine methylation levels at transcription factor binding sites (TFBSs); 8) single nucleotide polymorphism (SNP) calling, and 9) copy number variation (CNV) calling.MethGo is a simple and effective tool for the analysis of BS-Seq data including both WGBS and RRBS. It contains 9 analyses in 5 major modules to profile (epi)genome. It profiles genome-wide DNA methylation in global and in gene level scale. It can also analyze the methylation pattern around the transcription factor binding sites, and assess genetic variations such as SNPs and CNVs. MethGo is coded in Python and is publically available at http://paoyangchen-laboratory.github.io/methgo/.
Project description:BACKGROUND: DNA methylation is an important epigenetic modification involved in many biological processes. Bisulfite treatment coupled with high-throughput sequencing provides an effective approach for studying genome-wide DNA methylation at base resolution. Libraries such as whole genome bisulfite sequencing (WGBS) and reduced represented bisulfite sequencing (RRBS) are widely used for generating DNA methylomes, demanding efficient and versatile tools for aligning bisulfite sequencing data. RESULTS: We have developed BS-Seeker2, an updated version of BS Seeker, as a full pipeline for mapping bisulfite sequencing data and generating DNA methylomes. BS-Seeker2 improves mappability over existing aligners by using local alignment. It can also map reads from RRBS library by building special indexes with improved efficiency and accuracy. Moreover, BS-Seeker2 provides additional function for filtering out reads with incomplete bisulfite conversion, which is useful in minimizing the overestimation of DNA methylation levels. We also defined CGmap and ATCGmap file formats for full representations of DNA methylomes, as part of the outputs of BS-Seeker2 pipeline together with BAM and WIG files. CONCLUSIONS: Our evaluations on the performance show that BS-Seeker2 works efficiently and accurately for both WGBS data and RRBS data. BS-Seeker2 is freely available at http://pellegrini.mcdb.ucla.edu/BS_Seeker2/ and the Galaxy server.
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.
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:Whole-genome bisulfite sequencing (WGBS) has been widely used to quantify cytosine DNA methylation frequency in an expanding array of cell and tissue types. Because of the denaturing conditions used, this method ultimately leads to the measurement of methylation frequencies at single cytosines. Hence, the methylation frequency of CpG dyads (two complementary CG dinucleotides) can be only indirectly inferred by overlaying the methylation frequency of two cytosines measured independently. Furthermore, hemi-methylated CpGs (hemiCpGs) have not been previously analyzed in WGBS studies. We recently developed in silico strand annealing (iSA), a bioinformatics method applicable to WGBS data, to resolve the methylation status of CpG dyads into unmethylated, hemi-methylated, and methylated. HemiCpGs account for 4-20% of the DNA methylome in different cell types, and some can be inherited across cell divisions, suggesting a role as a stable epigenetic mark. Therefore, it is important to resolve hemiCpGs from fully methylated CpGs in WGBS studies. This protocol describes step-by-step commands to accomplish this task, including dividing alignments by strand, pairing alignments between strands, and extracting single-fragment methylation calls. The versatility of iSA enables its application downstream of other WGBS-related methods such as nasBS-seq (nascent DNA bisulfite sequencing), ChIP-BS-seq (ChIP followed by bisulfite sequencing), TAB-seq, oxBS-seq, and fCAB-seq. iSA is also tunable for analyzing the methylation status of cytosines in any sequence context. We exemplify this flexibility by uncovering the single-fragment non-CpG methylome. This protocol provides enough details for users with little experience in bioinformatic analysis and takes 2-7 h.
Project description:DNA methylation is an important epigenetic modification critical in regulation and transgenerational inheritance. The methylation level can be estimated at single-nucleotide resolution by whole-genome bisulfite sequencing (BS-seq; WGBS). Current bisulfite aligners provide pipelines for processing the reads by WGBS; however, few are able to analyze the BS-seqs in a reasonable timeframe that meets the needs of the rapid expansion of epigenome sequencing in biomedical research.We introduce BS-Seeker3, an extensively improved and optimized implementation of BS-Seeker2 that leverages the available computational power of a standard bioinformatics lab. BS-Seeker3 adopts all alignment features of BS-Seeker2. It performs ultrafast alignments and achieves both high accuracy and high mappability, more than twice that of the other aligners that we evaluated. Moreover, BS Seeker 3 is well linked with downstream analyzer MethGo for up to 9 types of genomic and epigenomic analyses.BS-Seeker3 is an accurate, versatile, ultra-fast pipeline for processing bisulfite-converted reads. It also helps the user better visualize the methylation data.
Project description:Background 5? methylation of cytosines in DNA molecules is an important epigenetic mark in eukaryotes. Bisulfite sequencing is the gold standard of DNA methylation detection, and whole-genome bisulfite sequencing (WGBS) has been widely used to detect methylation at single-nucleotide resolution on a genome-wide scale. However, sodium bisulfite is known to severely degrade DNA, which, in combination with biases introduced during PCR amplification, leads to unbalanced base representation in the final sequencing libraries. Enzymatic conversion of unmethylated cytosines to uracils can achieve the same end product for sequencing as does bisulfite treatment and does not affect the integrity of the DNA; enzymatic methylation sequencing may, thus, provide advantages over bisulfite sequencing. Results Using an enzymatic methyl-seq (EM-seq) technique to selectively deaminate unmethylated cytosines to uracils, we generated and sequenced libraries based on different amounts of Arabidopsis input DNA and different numbers of PCR cycles, and compared these data to results from traditional whole-genome bisulfite sequencing. We found that EM-seq libraries were more consistent between replicates and had higher mapping and lower duplication rates, lower background noise, higher average coverage, and higher coverage of total cytosines. Differential methylation region (DMR) analysis showed that WGBS tended to over-estimate methylation levels especially in CHG and CHH contexts, whereas EM-seq detected higher CG methylation levels in certain highly methylated areas. These phenomena can be mostly explained by a correlation of WGBS methylation estimation with GC content and methylated cytosine density. We used EM-seq to compare methylation between leaves and flowers, and found that CHG methylation level is greatly elevated in flowers, especially in pericentromeric regions. Conclusion We suggest that EM-seq is a more accurate and reliable approach than WGBS to detect methylation. Compared to WGBS, the results of EM-seq are less affected by differences in library preparation conditions or by the skewed base composition in the converted DNA. It may therefore be more desirable to use EM-seq in methylation studies.
Project description:DNA methylation (5mC) and hydroxymethylation (5hmC) are chemical modifications of cytosine bases which play a crucial role in epigenetic gene regulation. However, cost, data complexity and unavailability of comprehensive analytical tools is one of the major challenges in exploring these epigenetic marks. Hydroxymethylation-and Methylation-Sensitive Tag sequencing (HMST-seq) is one of the most cost-effective techniques that enables simultaneous detection of 5mC and 5hmC at single base pair resolution. We present HMST-Seq-Analyzer as a comprehensive and robust method for performing simultaneous differential methylation analysis on 5mC and 5hmC data sets. HMST-Seq-Analyzer can detect Differentially Methylated Regions (DMRs), annotate them, give a visual overview of methylation status and also perform preliminary quality check on the data. In addition to HMST-Seq, our tool can be used on whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) data sets as well. The tool is written in Python with capacity to process data in parallel and is available at (https://hmst-seq.github.io/hmst/).
Project description:BACKGROUND:Whole genome bisulfite sequencing (WGBS) also known as BS-seq has been widely used to measure the methylation of whole genome at single-base resolution. One of the key steps in the assay is converting unmethylated cytosines into thymines (BS conversion). Incomplete conversion of unmethylated cytosines can introduce false positive methylation call. Developing a quick method to evaluate bisulfite conversion ratio (BCR) is benefit for both quality control and data analysis of WGBS. RESULTS:Here we provide a computational method named "BCREval" to estimate the unconverted rate (UCR) by using telomeric repetitive DNA as native spike-in control. We tested the method by using public WGBS data and found that it is very stable and most of BS conversion assays can achieve>?99.5% efficiency. The non-CpG DNA methylation at telomere fits a binomial model and may result from a random process with very low possibility (the ratio?<?0.4%). And the comparison between BCREval and Bismark (Krueger and Andrews, Bioinformatics 27:1571-1572, 2011), a widely used BCR evaluator, suggests that our algorithm is much faster and more efficient than the latter. CONCLUSION:Our method is a simple but robust method to QC and speculates BCR for WGBS experiments to make sure it achieves acceptable level. It is faster and more efficient than current tools and can be easily integrated into presented WGBS pipelines.
Project description:Understanding the role of DNA methylation often requires accurate assessment and comparison of these modifications in a genome-wide fashion. Sequencing-based DNA methylation profiling provides an unprecedented opportunity to map and compare complete DNA CpG methylomes. These include whole genome bisulfite sequencing (WGBS), Reduced-Representation Bisulfite-Sequencing (RRBS), and enrichment-based methods such as MeDIP-seq, MBD-seq, and MRE-seq. An investigator needs a method that is flexible with the quantity of input DNA, provides the appropriate balance among genomic CpG coverage, resolution, quantitative accuracy, and cost, and comes with robust bioinformatics software for analyzing the data. In this chapter, we describe four protocols that combine state-of-the-art experimental strategies with state-of-the-art computational algorithms to achieve this goal. We first introduce two experimental methods that are complementary to each other. MeDIP-seq, or methylation-dependent immunoprecipitation followed by sequencing, uses an anti-methylcytidine antibody to enrich for methylated DNA fragments, and uses massively parallel sequencing to reveal identity of enriched DNA. MRE-seq, or methylation-sensitive restriction enzyme digestion followed by sequencing, relies on a collection of restriction enzymes that recognize CpG containing sequence motifs, but only cut when the CpG is unmethylated. Digested DNA fragments enrich for unmethylated CpGs at their ends, and these CpGs are revealed by massively parallel sequencing. The two computational methods both implement advanced statistical algorithms that integrate MeDIP-seq and MRE-seq data. M&M is a statistical framework to detect differentially methylated regions between two samples. methylCRF is a machine learning framework that predicts CpG methylation levels at single CpG resolution, thus raising the resolution and coverage of MeDIP-seq and MRE-seq to a comparable level of WGBS, but only incurring a cost of less than 5% of WGBS. Together these methods form an effective, robust, and affordable platform for the investigation of genome-wide DNA methylation.
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.