HPG-DHunter: an ultrafast, friendly tool for DMR detection and visualization

Abstract

Background Software tools for analyzing DNA methylation do not provide graphical results which can be easily identified, but huge text files containing the alignment of the samples and their methylation status at a resolution of base pairs. There have been proposed different tools and methods for finding Differentially Methylated Regions (DMRs) among different samples, but the execution time required by these tools is large, and the visualization of their results is far from being interactive. Additionally, these methods show more accurate results when identifying simulated DM regions that are long and have small within-group variation, but they have low concordance when used with real datasets, probably due to the different approaches they use for DMR identification. Thus, a tool which automatically detects DMRs among different samples and interactively visualizes DMRs at different scales (from a bunch to ten of millions of DNA locations) can be the key for shortening the DNA methylation analysis process in many studies. Results In this paper, we propose a software tool based on the wavelet transform. This mathematical tool allows the fast automatic DMR detection by simple comparison of different signals at different resolution levels. Also, it allows an interactive visualization of the DMRs found at different resolution levels. The tool is publicly available at https://grev-uv.github.io/, and it is part of a complete suite of tools which allow to carry out the complete process of DNA alignment and methylation analysis, creation of methylation maps of the whole genome, and the detection and visualization of DMRs between different samples. Conclusions The validation of the developed software tool shows similar concordance with other well-known and extended tools when used with real and synthetic data. The batch mode of the tool is capable of automatically detecting the existing DMRs for half (twelve) of the human chromosomes between two sets of six samples (whose.csv files after the alignment and mapping procedures have an aggregated size of 108 Gigabytes) in around three hours and a half. When compared to other well-known tools, HPG-DHunter only requires around 15% of the execution time required by other tools for detecting the DMRs.

Ten Years of EWAS

“…MetS is extensively influenced by environmental factors, and diet is one of the most routine environmental factors that everyone faces in their daily lives. [103,104] A number of dietinfluenced methylation sites have been identified as being involved in metabolism-related regulatory pathways, including those related to lipid metabolism, immunity, and cellular Detection of differentially methylated region/loci HPG-DHunter [ 172] Detection of differentially methylated regions 2020 Software https://grev-uv.github.io/ DMRcaller [ 173] Differentially methylated regions caller 2018 R package http://bioconductor.org/packages/ DMRcaller/ DiMmeR [ 174] Discovery of multiple differentially methylated regions 2017 Java package http://dimmer.compbio.sdu.dk…”