A survey of the approaches for identifying differential methylation using bisulfite sequencing data

Shafi, Adib; Mitrea, Cristina; Nguyen, Tin; Drăghici, Sorin

doi:10.1093/bib/bbx013

Cited by 65 publications

(57 citation statements)

References 88 publications

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“…Zero and one inflation can easily overcome by lowering the extremes by a small offset. Sequencing data have also some technical issues such as the over-represented methylation due to higher cycles of PCR [40] or the 'Spatial correlation' between the methylation levels of the neighboring [8]. There are other general concerns that need also to be taken into account in the analysis of methylation data such as batch effects [6], tissue heterogeneity, filtering [41], missing values, SNP overlapping and copy number affectation.…”

Section: Discussionmentioning

confidence: 99%

“…In small sample size experiments, where distribution assumptions may be inaccurate, a Fisher's exact test is often applied. Other classical hypothesis testing methods, such as the chi-square test, regression approaches, t-test and analysis of variance; are used to identify DMSs [7,8]. Limma [9] is also an extended method to assess DMSs using standard linear regression models.…”

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confidence: 99%

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Are methylation beta-values simplex distributed?

Nonell

González

2019

Preprint

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DNA methylation plays an important role in the development and progression of disease. Beta-values are the standard methylation measures. Different statistical methods have been proposed to assess differences in methylation between conditions. However, most of them do not completely account for the distribution of beta-values. The simplex distribution can accommodate beta-values data. We hypothesize that simplex is a quite flexible distribution which is able to model methylation data.To test our hypothesis, we conducted several analyses using four real data sets obtained from microarrays and sequencing technologies. Standard data distributions were studied and modelled in comparison to the simplex. Besides, some simulations were conducted in different scenarios encompassing several distribution assumptions, regression models and sample sizes. Finally, we compared DNA methylation between females and males in order to benchmark the assessed methodologies under different scenarios.According to the results obtained by the simulations and real data analyses, DNA methylation data are concordant with the simplex distribution in many situations. Simplex regression models work well in small sample size data sets. However, when sample size increases, other models such as the beta regression or even the linear regression can be employed to assess group comparisons and obtain unbiased results. Based on these results, we can provide some practical recommendations when analyzing methylation data: 1) use data sets of at least 10 samples per studied condition for microarray data sets or 30 in NGS data sets, 2) apply a simplex or beta regression model for microarray data, 3) apply a linear model in any other case.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Are methylation beta-values simplex distributed?

Nonell

González

2019

Preprint

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“…It is reassuring that MET turns out to be a by-product of measuring methylation diversity by JSD-together, they define the state of each cytosine in the population. Different approaches based on Shannon entropy already exist to detect differentially methylated sites or regions (reviewed in [44]). However, with the metamethylome concept, we aim at characterizing the state of a population rather than merely measuring the statistical evidence for differential methylation (although JSD can be used in this way as it generalizes the chisquared test [22]).…”

Section: Discussionmentioning

confidence: 99%

Cell Type-specific Genome Scans of DNA Methylation Diversity Indicate an Important Role for Transposable Elements

Kartal

Schmid

Grossniklaus

2019

Preprint

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The epigenome modulates the activity of genes and supports the stability of the genome. The epigenome can also contain phenotypically relevant, heritable marks that may vary at the organismic and population level. Such non-genetic standing variation may be relevant to ecological and evolutionary processes. To identify loci susceptible to selection, it is common to profile large populations at the genome scale, yet methods to perform such scans for epigenetic diversity are barely tapped. Here, we develop a scalable, information-theoretic approach to assess epigenome diversity based on Jensen-Shannon divergence (JSD) and demonstrate its practicality by measuring cell type-specific methylation diversity in the model plant Arabidopsis thaliana. DNA methylation diversity tends to be increased in the CG as compared to the non-CG (CHG and CHH) sequence context but the tissue or cell type has an impact on diversity at non-CG sites. Less accessible, more heterochromatic states of chromatin exhibit increased diversity. Genes tend to carry more single-methylation polymorphisms when they harbor gene body-like chromatin signatures and flank transposable elements (TEs). In conclusion, the analysis of DNA methylation with JSD in Arabidopsis demonstrates that the genomic location of a gene dominates its methylation diversity, in particular the proximity to TEs which are increasingly viewed as drivers of evolution. Importantly, the JSD-based approach we implemented here is applicable to any population-level epigenomic data set to analyze variation in epigenetic marks among individuals, tissues, or cells of any organism, including the epigenetic heterogeneity of cells in healthy or diseased organs such as tumors.

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“…Analysis of bisulfite sequencing data and the subsequent tertiary analysis are a complex topic. While a full discussion is beyond the scope of this review, recent publications provide greater detail (Bock 2012;Krueger et al 2012;Baubec and Akalin 2016;Shafi et al 2017). Nonetheless, the basic steps of sequencing data analysis consist of the following: quality control, alignment, quantitation, differential methylation determination, and tertiary analysis where patterns of methylation are integrated with other forms of annotation such as genomic features, other epigenomic data (e.g., chromatin and enhancers), gene expression, and pathways.…”

Section: Bioinformaticsmentioning

confidence: 99%

Analysis of DNA modifications in aging research

et al. 2018

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As geroscience research extends into the role of epigenetics in aging and age-related disease, researchers are being confronted with unfamiliar molecular techniques and data analysis methods that can be difficult to integrate into their work. In this review, we focus on the analysis of DNA modifications, namely cytosine methylation and hydroxymethylation, through nextgeneration sequencing methods. While older techniques for modification analysis performed relative quantitation across regions of the genome or examined average genome levels, these analyses lack the desired specificity, rigor, and genomic coverage to firmly establish the nature of genomic methylation patterns and their response to aging. With recent methodological advances, such as whole genome bisulfite sequencing (WGBS), bisulfite oligonucleotide capture sequencing (BOCS), and bisulfite amplicon sequencing (BSAS), cytosine modifications can now be readily analyzed with base-specific, absolute quantitation at both cytosine-guanine dinucleotide (CG) and non-CG sites throughout the genome or within specific regions of interest by next-generation sequencing. Additional advances, such as oxidative bisulfite conversion to differentiate methylation from hydroxymethylation and analysis of limited input/single-cells, have great promise for continuing to expand epigenomic capabilities. This review provides a background on DNA modifications, the current state-of-theart for sequencing methods, bioinformatics tools for converting these large data sets into biological insights, and perspectives on future directions for the field.

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A survey of the approaches for identifying differential methylation using bisulfite sequencing data

Cited by 65 publications

References 88 publications

Are methylation beta-values simplex distributed?

Are methylation beta-values simplex distributed?

Cell Type-specific Genome Scans of DNA Methylation Diversity Indicate an Important Role for Transposable Elements

Analysis of DNA modifications in aging research

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