LuxGLM: a probabilistic covariate model for quantification of DNA methylation modifications with complex experimental designs

LuxHMM: DNA methylation analysis with genome segmentation via hidden Markov model

BACKGROUND

DNA methylation plays an important role in studying the epigenetics of various biological processes including many diseases. Although differential methylation of individual cytosines can be informative, given that methylation of neighboring CpGs are typically correlated, analysis of differentially methylated regions is often of more interest.

RESULTS

We have developed a probabilistic method and software, LuxHMM, that uses hidden Markov model (HMM) to segment the genome into regions and a Bayesian regression model, which allows handling of multiple covariates, to infer differential methylation of regions. Moreover, our model includes experimental parameters that describe the underlying biochemistry in bisulfite sequencing and model inference is done using either variational inference for efficient genome-scale analysis or Hamiltonian Monte Carlo (HMC).

CONCLUSIONS

Analyses of real and simulated bisulfite sequencing data demonstrate the competitive performance of LuxHMM compared with other published differential methylation analysis methods.

A comprehensive approach for genome-wide efficiency profiling of DNA modifying enzymes

A precise understanding of DNA methylation dynamics is of great importance for a variety of biological processes including cellular reprogramming and differentiation. To date, complex integration of multiple and distinct genome-wide datasets is required to realize this task. We present GwEEP (genome-wide epigenetic efficiency profiling) a versatile approach to infer dynamic efficiencies of DNA modifying enzymes. GwEEP relies on genome-wide hairpin datasets, which are translated by a hidden Markov model into quantitative enzyme efficiencies with reported confidence around the estimates. GwEEP predicts de novo and maintenance methylation efficiencies of Dnmts and furthermore the hydroxylation efficiency of Tets. Its design also allows capturing further oxidation processes given available data. We show that GwEEP predicts accurately the epigenetic changes of ESCs following a Serum-to-2i shift and applied to Tet TKO cells confirms the hypothesized mutual interference between Dnmts and Tets.

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

BACKGROUND

DNA 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.

RESULTS

We 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.

CONCLUSIONS

In 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.

LuxUS: DNA methylation analysis using generalized linear mixed model with spatial correlation

Motivation

DNA methylation is an important epigenetic modification, which has multiple functions. DNA methylation and its connections to diseases have been extensively studied in recent years. It is known that DNA methylation levels of neighboring cytosines are correlated and that differential DNA methylation typically occurs rather as regions instead of individual cytosine level.

Results

We have developed a generalized linear mixed model, LuxUS, that makes use of the correlation between neighboring cytosines to facilitate analysis of differential methylation. LuxUS implements a likelihood model for bisulfite sequencing data that accounts for experimental variation in underlying biochemistry. LuxUS can model both binary and continuous covariates, and mixed model formulation enables including replicate and cytosine random effects. Spatial correlation is included to the model through a cytosine random effect correlation structure. We show with simulation experiments that using the spatial correlation, we gain more power to the statistical testing of differential DNA methylation. Results with real bisulfite sequencing dataset show that LuxUS is able to detect biologically significant differentially methylated cytosines.

Availability and implementation

The tool is available at https://github.com/hallav/LuxUS.

Supplementary information

Supplementary data are available at Bioinformatics online.

pWGBSSimla: a profile-based whole-genome bisulfite sequencing data simulator incorporating methylation QTLs, allele-specific methylations and differentially methylated regions

MOTIVATION

DNA methylation plays an important role in regulating gene expression. DNA methylation is commonly analyzed using bisulfite sequencing (BS-seq)-based designs, such as whole-genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS) and oxidative bisulfite sequencing (oxBS-seq). Furthermore, there has been growing interest in investigating the roles that genetic variants play in changing the methylation levels (i.e. methylation quantitative trait loci or meQTLs), how methylation regulates the imprinting of gene expression (i.e. allele-specific methylation or ASM) and the differentially methylated regions (DMRs) among different cell types. However, none of the current simulation tools can generate different BS-seq data types (e.g. WGBS, RRBS and oxBS-seq) while modeling meQTLs, ASM and DMRs.

RESULTS

We developed profile-based whole-genome bisulfite sequencing data simulator (pWGBSSimla), a profile-based bisulfite sequencing data simulator, which simulates WGBS, RRBS and oxBS-seq data for different cell types based on real data. meQTLs and ASM are modeled based on the block structures of the methylation status at CpGs, whereas the simulation of DMRs is based on observations of methylation rates in real data. We demonstrated that pWGBSSimla adequately simulates data and allows performance comparisons among different methylation analysis methods.

AVAILABILITY AND IMPLEMENTATION

pWGBSSimla is available at https://omicssimla.sourceforge.io.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

TET enzymes augment activation-induced deaminase (AID) expression via 5-hydroxymethylcytosine modifications at the Aicda superenhancer

TET enzymes are dioxygenases that promote DNA demethylation by oxidizing the methyl group of 5-methylcytosine to 5-hydroxymethylcytosine (5hmC). Here, we report a close correspondence between 5hmC-marked regions, chromatin accessibility and enhancer activity in B cells, and a strong enrichment for consensus binding motifs for basic region-leucine zipper (bZIP) transcription factors at TET-responsive genomic regions. Functionally, Tet2 and Tet3 regulate class switch recombination (CSR) in murine B cells by enhancing expression of Aicda, which encodes the activation-induced cytidine deaminase (AID) enzyme essential for CSR. TET enzymes deposit 5hmC, facilitate DNA demethylation, and maintain chromatin accessibility at two TET-responsive enhancer elements, TetE1 and TetE2, located within a superenhancer in the Aicda locus. Our data identify the bZIP transcription factor, ATF-like (BATF) as a key transcription factor involved in TET-dependent Aicda expression. 5hmC is not deposited at TetE1 in activated Batf-deficient B cells, indicating that BATF facilitates TET recruitment to this Aicda enhancer. Our study emphasizes the importance of TET enzymes for bolstering AID expression and highlights 5hmC as an epigenetic mark that captures enhancer dynamics during cell activation.

Generative Models for Quantification of DNA Modifications

There are multiple chemical modifications of cytosine that are important to the regulation and ultimately the functional expression of the genome. To date no single experiment can capture these separate modifications, and integrative experimental designs are needed to fully characterize cytosine methylation and chemical modification. This chapter describes a generative probabilistic model, Lux, for integrative analysis of cytosine methylation and its oxidized variants. Lux simultaneously analyzes partially orthogonal bisulfite sequencing data sets to estimate proportions of different cytosine methylation modifications and estimate multiple cytosine modifications for a single sample by integrating across experimental designs composed of multiple parallel destructive genomic measurements. Lux also considers the variation in measurements introduced by different imperfect experimental steps; the experimental variation can be quantified by using appropriate spike-in controls, allowing Lux to deconvolve the measurements and recover accurately the underlying signal.

TET Methylcytosine Oxidases in T Cell and B Cell Development and Function

DNA methylation is established by DNA methyltransferases and is a key epigenetic mark. Ten-eleven translocation (TET) proteins are enzymes that oxidize 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC) and further oxidization products (oxi-mCs), which indirectly promote DNA demethylation. Here, we provide an overview of the effect of TET proteins and altered DNA modification status in T and B cell development and function. We summarize current advances in our understanding of the role of TET proteins and 5hmC in T and B cells in both physiological and pathological contexts. We describe how TET proteins and 5hmC regulate DNA modification, chromatin accessibility, gene expression, and transcriptional networks and discuss potential underlying mechanisms and open questions in the field.