SMAP: a streamlined methylation analysis pipeline for bisulfite sequencing

Uncovering Alterations in Cancer Epigenetics via Trans-Dimensional Markov Chain Monte Carlo and Hidden Markov Models

Epigenetic alterations are key drivers in the development and progression of cancer. Identifying differentially methylated cytosines (DMCs) in cancer samples is a crucial step toward understanding these changes. In this paper, we propose a trans-dimensional Markov chain Monte Carlo (TMCMC) approach that uses hidden Markov models (HMMs) with binomial emission, and bisulfite sequencing (BS-Seq) data, called DMCTHM, to identify DMCs in cancer epigenetic studies. We introduce the Expander-Collider penalty to tackle under and over-estimation in TMCMC-HMMs. We address all known challenges inherent in BS-Seq data by introducing novel approaches for capturing functional patterns and autocorrelation structure of the data, as well as for handling missing values, multiple covariates, multiple comparisons, and family-wise errors. We demonstrate the effectiveness of DMCTHM through comprehensive simulation studies. The results show that our proposed method outperforms other competing methods in identifying DMCs. Notably, with DMCTHM, we uncovered new DMCs and genes in Colorectal cancer that were significantly enriched in the Tp53 pathway.

Challenges and Perspectives in the Epigenetics of Climate Change-Induced Forests Decline

Forest tree species are highly vulnerable to the effects of climate change. As sessile organisms with long generation times, their adaptation to a local changing environment may rely on epigenetic modifications when allele frequencies are not able to shift fast enough. However, the current lack of knowledge on this field is remarkable, due to many challenges that researchers face when studying this issue. Huge genome sizes, absence of reference genomes and annotation, and having to analyze huge amounts of data are among these difficulties, which limit the current ability to understand how climate change drives tree species epigenetic modifications. In spite of this challenging framework, some insights on the relationships among climate change-induced stress and epigenomics are coming. Advances in DNA sequencing technologies and an increasing number of studies dealing with this topic must boost our knowledge on tree adaptive capacity to changing environmental conditions. Here, we discuss challenges and perspectives in the epigenetics of climate change-induced forests decline, aiming to provide a general overview of the state of the art.

DNA methylation data by sequencing: experimental approaches and recommendations for tools and pipelines for data analysis

Sequencing technologies have changed not only our approaches to classical genetics, but also the field of epigenetics. Specific methods allow scientists to identify novel genome-wide epigenetic patterns of DNA methylation down to single-nucleotide resolution. DNA methylation is the most researched epigenetic mark involved in various processes in the human cell, including gene regulation and development of diseases, such as cancer. Increasing numbers of DNA methylation sequencing datasets from human genome are produced using various platforms-from methylated DNA precipitation to the whole genome bisulfite sequencing. Many of those datasets are fully accessible for repeated analyses. Sequencing experiments have become routine in laboratories around the world, while analysis of outcoming data is still a challenge among the majority of scientists, since in many cases it requires advanced computational skills. Even though various tools are being created and published, guidelines for their selection are often not clear, especially to non-bioinformaticians with limited experience in computational analyses. Separate tools are often used for individual steps in the analysis, and these can be challenging to manage and integrate. However, in some instances, tools are combined into pipelines that are capable to complete all the essential steps to achieve the result. In the case of DNA methylation sequencing analysis, the goal of such pipeline is to map sequencing reads, calculate methylation levels, and distinguish differentially methylated positions and/or regions. The objective of this review is to describe basic principles and steps in the analysis of DNA methylation sequencing data that in particular have been used for mammalian genomes, and more importantly to present and discuss the most pronounced computational pipelines that can be used to analyze such data. We aim to provide a good starting point for scientists with limited experience in computational analyses of DNA methylation and hydroxymethylation data, and recommend a few tools that are powerful, but still easy enough to use for their own data analysis.

CGmapTools improves the precision of heterozygous SNV calls and supports allele-specific methylation detection and visualization in bisulfite-sequencing data

Motivation

DNA methylation is important for gene silencing and imprinting in both plants and animals. Recent advances in bisulfite sequencing allow detection of single nucleotide variations (SNVs) achieving high sensitivity, but accurately identifying heterozygous SNVs from partially C-to-T converted sequences remains challenging.

Results

We designed two methods, BayesWC and BinomWC, that substantially improved the precision of heterozygous SNV calls from ∼80% to 99% while retaining comparable recalls. With these SNV calls, we provided functions for allele-specific DNA methylation (ASM) analysis and visualizing the methylation status on reads. Applying ASM analysis to a previous dataset, we found that an average of 1.5% of investigated regions showed allelic methylation, which were significantly enriched in transposon elements and likely to be shared by the same cell-type. A dynamic fragment strategy was utilized for DMR analysis in low-coverage data and was able to find differentially methylated regions (DMRs) related to key genes involved in tumorigenesis using a public cancer dataset. Finally, we integrated 40 applications into the software package CGmapTools to analyze DNA methylomes. This package uses CGmap as the format interface, and designs binary formats to reduce the file size and support fast data retrieval, and can be applied for context-wise, gene-wise, bin-wise, region-wise and sample-wise analyses and visualizations.

Availability and implementation

The CGmapTools software is freely available at https://cgmaptools.github.io/.

Contact

guoweilong@cau.edu.cn.

Supplementary information

Supplementary data are available at Bioinformatics online.

BS-virus-finder: virus integration calling using bisulfite sequencing data

Background

DNA methylation plays a key role in the regulation of gene expression and carcinogenesis. Bisulfite sequencing studies mainly focus on calling single nucleotide polymorphism, different methylation region, and find allele-specific DNA methylation. Until now, only a few software tools have focused on virus integration using bisulfite sequencing data.

Findings

We have developed a new and easy-to-use software tool, named BS-virus-finder (BSVF, RRID:SCR_015727), to detect viral integration breakpoints in whole human genomes. The tool is hosted at https://github.com/BGI-SZ/BSVF.

Conclusions

BS-virus-finder demonstrates high sensitivity and specificity. It is useful in epigenetic studies and to reveal the relationship between viral integration and DNA methylation. BS-virus-finder is the first software tool to detect virus integration loci by using bisulfite sequencing data.

Early microbial colonization affects DNA methylation of genes related to intestinal immunity and metabolism in preterm pigs

Epigenetic regulation may play an important role in mediating microbe-host interactions and adaptation of intestinal gene expression to bacterial colonization just after birth. This is particularly important after preterm birth because the immature intestine is hypersensitive to invading bacteria. We compared the intestinal DNA methylome and microbiome between conventional (CON) and antibiotics-treated (AB) preterm pigs, used as a model for preterm infants. Oral AB treatment reduced bacterial density (∼100-fold), diversity and fermentation, improved the resistance to necrotizing enterocolitis (NEC) and changed the genome-wide DNA methylation in the distal small intestine. Integration of epigenome data with previously obtained proteome data showed that intestinal immune-metabolic pathways were affected by the AB-induced delay in bacterial colonization. DNA methylation and expression of intestinal genes, related to innate immune response, phagocytosis, endothelial homeostasis and tissue metabolism (e.g. CPN1, C3, LBP, HIF1A, MicroRNA-126, PTPRE), differed between AB and CON pigs even before any evidence of NEC lesions. Our findings document that the newborn immature intestine is influenced by bacterial colonization via DNA methylation changes. Microbiota-dependent epigenetic programming of genes related to gut immunity, vascular integrity and metabolism may be critical for short- and long-term intestinal health in preterm neonates.

Integrating DNA Methylation and Hydroxymethylation Data with the Mint Pipeline

DNA methylation (5mC) plays important roles in mammalian development, oncogenesis, treatment response, and responses to the environment. DNA hydroxymethylation (5hmC) is also an informative epigenetic mark with distinct roles in regulation and cancer. Gold-standard, widely used technologies (bisulfite conversion, followed by deep sequencing) cannot distinguish between 5mC and 5hmC. Therefore, additional experiments are required to differentiate the two marks, and in silico methods are needed to analyze, integrate, and interpret these data. We developed the Methylation INTegration (mint) pipeline to support the comprehensive analysis of bisulfite conversion and immunoprecipitation-based methylation and hydroxymethylation assays, with additional steps toward integration, visualization, and interpretation. The pipeline is available as both a command line and a Galaxy graphical user interface tool. Both implementations require minimal configuration while remaining flexible to experiment specific needs. Cancer Res; 77(21); e27-30. ©2017 AACR.

Profiling genome-wide DNA methylation

DNA methylation is an epigenetic modification that plays an important role in regulating gene expression and therefore a broad range of biological processes and diseases. DNA methylation is tissue-specific, dynamic, sequence-context-dependent and trans-generationally heritable, and these complex patterns of methylation highlight the significance of profiling DNA methylation to answer biological questions. In this review, we surveyed major methylation assays, along with comparisons and biological examples, to provide an overview of DNA methylation profiling techniques. The advances in microarray and sequencing technologies make genome-wide profiling possible at a single-nucleotide or even a single-cell resolution. These profiling approaches vary in many aspects, such as DNA input, resolution, genomic region coverage, and bioinformatics analysis, and selecting a feasible method requires knowledge of these methods. We first introduce the biological background of DNA methylation and its pattern in plants, animals and fungi. We present an overview of major experimental approaches to profiling genome-wide DNA methylation and hydroxymethylation and then extend to the single-cell methylome. To evaluate these methods, we outline their strengths and weaknesses and perform comparisons across the different platforms. Due to the increasing need to compute high-throughput epigenomic data, we interrogate the computational pipeline for bisulfite sequencing data and also discuss the concept of identifying differentially methylated regions (DMRs). This review summarizes the experimental and computational concepts for profiling genome-wide DNA methylation, followed by biological examples. Overall, this review provides researchers useful guidance for the selection of a profiling method suited to specific research questions.

Tumor purity and differential methylation in cancer epigenomics

DNA methylation is an epigenetic modification of DNA molecule that plays a vital role in gene expression regulation. It is not only involved in many basic biological processes, but also considered an important factor for tumorigenesis and other human diseases. Study of DNA methylation has been an active field in cancer epigenomics research. With the advances of high-throughput technologies and the accumulation of enormous amount of data, method development for analyzing these data has gained tremendous interests in the fields of computational biology and bioinformatics. In this review, we systematically summarize the recent developments of computational methods and software tools in high-throughput methylation data analysis with focus on two aspects: differential methylation analysis and tumor purity estimation in cancer studies.

SMAP: a streamlined methylation analysis pipeline for bisulfite sequencing

Background

DNA 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.

Findings

To 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.

Conclusions

Simulation 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.

Electronic supplementary material

The online version of this article (doi:10.1186/s13742-015-0070-9) contains supplementary material, which is available to authorized users.