MethylC-analyzer: a comprehensive downstream pipeline for the analysis of genome-wide DNA methylation

BSXplorer: analytical framework for exploratory analysis of BS-seq data

BACKGROUND

Bisulfite sequencing detects and quantifies DNA methylation patterns, contributing to our understanding of gene expression regulation, genome stability maintenance, conservation of epigenetic mechanisms across divergent taxa, epigenetic inheritance and, eventually, phenotypic variation. Graphical representation of methylation data is crucial in exploring epigenetic regulation on a genome-wide scale in both plants and animals. This is especially relevant for non-model organisms with poorly annotated genomes and/or organisms where genome sequences are not yet assembled on chromosome level. Despite being a technology of choice to profile DNA methylation for many years now there are surprisingly few lightweight and robust standalone tools available for efficient graphical analysis of data in non-model systems. This significantly limits evolutionary studies and agrigenomics research. BSXplorer is a tool specifically developed to fill this gap and assist researchers in explorative data analysis and in visualising and interpreting bisulfite sequencing data more easily.

RESULTS

BSXplorer provides in-depth graphical analysis of sequencing data encompassing (a) profiling of methylation levels in metagenes or in user-defined regions using line plots and heatmaps, generation of summary statistics charts, (b) enabling comparative analyses of methylation patterns across experimental samples, methylation contexts and species, and (c) identification of modules sharing similar methylation signatures at functional genomic elements. The tool processes methylation data quickly and offers API and CLI capabilities, along with the ability to create high-quality figures suitable for publication.

CONCLUSIONS

BSXplorer facilitates efficient methylation data mining, contrasting and visualization, making it an easy-to-use package that is highly useful for epigenetic research.

MetageneCluster: a Python package for filtering conflicting signal trends in metagene plots

BACKGROUND

Metagene plots provide a visualization of biological signal trends over subsections of the genome and are used to perform high-level analysis of experimental data by aggregating genome-level data to create an average profile. The generation of metagene plots is useful for summarizing the results of many sequencing-based applications. Despite their prevalence and utility, the standard metagene plot is blind to conflicting signals within data. If multiple distinct trends occur, they can interact destructively, creating a plot that does not accurately represent any of the underlying trends.

RESULTS

We present MetageneCluster, a Python tool to generate a collection of representative metagene plots based on k-means clustering of genomic regions of interest. Clustering the data by similarity allows us to identify patterns within the features of interest. We are then able to summarize each pattern present in the data, rather than averaging across the entire feature space. We show that our method performs well when used to identify conflicting signals in real-world genome-level data.

CONCLUSIONS

Overall, MetageneCluster is a user-friendly tool for the creation of metagene plots that capture distinct patterns in underlying sequence data.

Integrative epigenome profiling of 47XXY provides insights into whole genomic DNA hypermethylation and active chromatin accessibility

Klinefelter syndrome (KS, 47XXY) is a disorder characterized by sex chromosomal aneuploidy, which may lead to changes in epigenetic regulations of gene expression. To define epigenetic architectures in 47XXY, we annotated DNA methylation in euploid males (46XY) and females (46XX), and 47XXY individuals using whole genome bisulfite sequencing (WGBS) and integrated chromatin accessbilty, and detected abnormal hypermethylation in 47XXY. Furthermore, we detected altered chromatin accessibility in 47XXY, in particular in chromosome X, using Assay for Transposase-Accessible Chromatin sequencing (ATAC-seq) in cultured amniotic cells. Our results construct the whole genome-wide DNA methylation map in 47XXY, and provide new insights into the early epigenomic dysregulation resulting from an extra chromosome X in 47XXY.