DMRFusion: A differentially methylated region detection tool based on the ranked fusion method

Abstract

DNA methylation is an important epigenetic modification involved in many biological processes and diseases. Computational analysis of differentially methylated regions (DMRs) could explore the underlying reasons of methylation. DMRFusion is presented as a useful tool for comprehensive DNA methylation analysis of DMRs on methylation sequencing data. This tool is designed base on the integration of several ranking methods; Information gain, Between versus within Class scatter ratio, Fisher ratio, Z-score and Welch's t-test. In this study, DMRFusion on reduced representation bisulfite sequencing (RRBS) data in chronic lymphocytic leukemia cancer displayed 30 nominated regions and CpG sites with a maximum methylation difference detected in the hypermethylation DMRs. We realized that DMRFusion is able to process methylation sequencing data in an efficient and accurate manner and to provide annotation and visualization for DMRs with high fold difference score (p-value and FDR<0.05 and type I error: 0.04).

Crosstalk between DNA methylation and gene expression in colorectal cancer, a potential plasma biomarker for tracing this tumor

“…To detect DMRs, we used the DMRFusion tool which could assess the optimal and significant hyper-and hypo-methylation DMRs in the genome with minimum redundancy and maximum relevance between cancer and control groups. DMRFusion describes the annotation of DMRs such as the nearest transcription start site (TSS), CGIs/shores/shelves, and regions within genome and visualizes DMRs with high fold difference score (p-value and FDR < 0.05 and type I error < 0.01) as described earlier 17 . The Human Methyl-Seq analysis was composed of three steps in the pre-processing stage before detecting DMRs.…”

“…It represents a sophisticated molecular mechanism for annotating genetic information 16 , and in most cases, modulates the genetic expression level. Although the mechanism and the role of DNA methylation is not completely understood, it is assumed that DNA methylation could affect the binding of the transcription factors to their DNA target sites and subsequently, alter the expression of downstream genes 17 . In tumor cells, abnormal DNA methylation could be commonly classified into two groups: (1) site specific CpG island promoter hypermethylation, (2) global DNA hypomethylation.…”

“…The detection of methylation alterations between cancer patients and normal individuals requires to take the variation of relative methylation within each group into consideration. Such variations could be associated with several technical and biological issues including unequal cytosine conversion rates, different library preparation protocols, different technical methylation assays and the existence of the natural epigenetic variations among individuals 17 .…”

DNA methylation profiles capturing breast cancer heterogeneity

“…Breast cancers are highly heterogeneous, which can at least be classified into luminal, HER2 positive (HER2p) and triple negative (TN) types of tumors given their intrinsic differences in the transcriptional expression pattern and clinical outcome association [1, 2]. TN breast cancers are malignant, lack effective targeted therapy, and is not homogeneous that complicates its diagnosis and therapeutics.…”

A-Lister: a tool for analysis of differentially expressed omics entities across multiple pairwise comparisons

“…control vs. drug A). In addition to the entity names themselves, fold changes, p -values, and many other categories of information are commonly listed within the differential expression files [3–6]. Filtering and comparing files of differentially expressed entities is a common task that is usually done by writing custom Perl, Python, or R scripts.…”