CMS: A Web-Based System for Visualization and Analysis of Genome-Wide Methylation Data of Human Cancers

Abstract

BackgroundDNA methylation of promoter CpG islands is associated with gene suppression, and its unique genome-wide profiles have been linked to tumor progression. Coupled with high-throughput sequencing technologies, it can now efficiently determine genome-wide methylation profiles in cancer cells. Also, experimental and computational technologies make it possible to find the functional relationship between cancer-specific methylation patterns and their clinicopathological parameters.Methodology/Principal FindingsCancer methylome system (CMS) is a web-based database application designed for the visualization, comparison and statistical analysis of human cancer-specific DNA methylation. Methylation intensities were obtained from MBDCap-sequencing, pre-processed and stored in the database. 191 patient samples (169 tumor and 22 normal specimen) and 41 breast cancer cell-lines are deposited in the database, comprising about 6.6 billion uniquely mapped sequence reads. This provides comprehensive and genome-wide epigenetic portraits of human breast cancer and endometrial cancer to date. Two views are proposed for users to better understand methylation structure at the genomic level or systemic methylation alteration at the gene level. In addition, a variety of annotation tracks are provided to cover genomic information. CMS includes important analytic functions for interpretation of methylation data, such as the detection of differentially methylated regions, statistical calculation of global methylation intensities, multiple gene sets of biologically significant categories, interactivity with UCSC via custom-track data. We also present examples of discoveries utilizing the framework.Conclusions/SignificanceCMS provides visualization and analytic functions for cancer methylome datasets. A comprehensive collection of datasets, a variety of embedded analytic functions and extensive applications with biological and translational significance make this system powerful and unique in cancer methylation research. CMS is freely accessible at: http://cbbiweb.uthscsa.edu/KMethylomes/.

Genome-Wide Association Studies and Epigenome-Wide Association Studies Go Together in Cancer Control

“…These clusters were reported in both ER + and ER -breast cancer tumors. Other investigators used a quantitative approach based on statistical methods and machine learning algorithms to quantify methylation differences in cases and controls and identify differentially methylated regions from genome-wide methylation profiles [140,141]. To elucidate the role of estrogen in the coordinated repression of gene clusters located at 16p11.2 in breast cancer, the concept of long-range epigenetic silencing (LRES) was studied.…”

A Novel Data Visualization Algorithm Based on the Graph Theory

“…Graph theory model is built, but also to grasp the method of model transformation. Such as the example shown in the figure one, the model can be expressed as the follows [8].…”

Genome-wide DNA methylome analysis reveals novel epigenetically dysregulated non-coding RNAs in human breast cancer

“…We performed comprehensive comparison analyses of DNA methylation profiling in normal controls and breast cancers, which were downloaded from the Cancer Methylome System (CMS) (Gu et al 2013). Pair-wise comparison of average genome-wide methylation intensity across all breast cancers and normal cohorts showed that normal and breast cancer samples correlated reasonably well within classes, but not inter-classes ( Figure 1A).…”

“…The genome-wide DNA methylation profiles from the Cancer Methylome System (CMS, http://cbbiweb.uthscsa.edu/KMethylomes) were used in the current study (Gu et al 2013). …”

“…Here, we reported use of high throughput sequencing technology to map DNA methylation in a cohort of 87 breast samples (77 cancers and 10 normal controls) (Gu et al 2013). …”