MADA: a web service for analysing DNA methylation array data

Abstract

Background DNA methylation in the human genome is acknowledged to be widely associated with biological processes and complex diseases. The Illumina Infinium methylation arrays have been approved as one of the most efficient and universal technologies to investigate the whole genome changes of methylation patterns. As methylation arrays may still be the dominant method for detecting methylation in the anticipated future, it is crucial to develop a reliable workflow to analysis methylation array data. Results In this study, we develop a web service MADA for the whole process of methylation arrays data analysis, which includes the steps of a comprehensive differential methylation analysis pipeline: pre-processing (data loading, quality control, data filtering, and normalization), batch effect correction, differential methylation analysis, and downstream analysis. In addition, we provide the visualization of pre-processing, differentially methylated probes or regions, gene ontology, pathway and cluster analysis results. Moreover, a customization function for users to define their own workflow is also provided in MADA. Conclusions With the analysis of two case studies, we have shown that MADA can complete the whole procedure of methylation array data analysis. MADA provides a graphical user interface and enables users with no computational skills and limited bioinformatics background to carry on complicated methylation array data analysis. The web server is available at: http://120.24.94.89:8080/MADA

A novel approach toward optimal workflow selection for DNA methylation biomarker discovery

“…Although these packages provide a flexible analysis method for differential methylation, they do not introduce a pipeline for the entire analysis process and are limited to the R programming environment. Recently, more general start-to-finish tools such as RnBeads [ 11 ], MADA [ 12 ], Ewastools (integrated into Galaxy) [ 13 ], and ADMIRE (Analysis of DNA Methylation In genomic Regions) [ 14 ] have helped overcome some of these challenges. However, these tools do not include best-practices guidelines for selection among the various parameters and options in each of the analysis steps.…”

“…Some have only considered preprocessing (quality control, normalization and batch effect correction) steps in their comparisons [ 15 – 17 ] and others have focused on comparing across differential-methylation analysis algorithms [ 18 ]. Furthermore, for evaluation and comparison of different analysis methods, previous studies have mostly used array [ 12 – 14 ] or sequencing data [ 15 , 16 ] in limited numbers and across a few contexts as the ground truth. Since the exact location of true differences between cases and controls is not accurately known beforehand, some studies have attempted to use matched methylation sequencing data as gold standard for true differentially methylated regions [ 15 , 16 ].…”

Methods in DNA methylation array dataset analysis: A review

“…The scalability of the methods can range from moderate (done for multiple samples) to high (done for large amounts of samples). Many bioinformatics methods and pipelines, including Bigmelon [17] , EpiScanpy [18] , EpiMOLAS [19] , MADA [20] , AmpliconDesign [21] , COHCAP [22] , Bicycle [23] , and ChAMP [24] , have been developed for analyzing the extent of high throughput methylation dataset produced by the various platforms for conducting epigenome-wide association studies, whose output is in the proclaimed repositories.…”