Article

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

Cancer Therapy & Research Center, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America.
PLoS ONE (Impact Factor: 3.23). 04/2013; 8(4):e60980. DOI: 10.1371/journal.pone.0060980
Source: PubMed

ABSTRACT

Background
DNA 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 Findings
Cancer 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/Significance
CMS 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/.

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Available from: Juan Carlos Roa
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    • "The BC database was constructed with 77 breast tumors, 10 normal breast samples (mammary reduction) and 41 BC cell lines. The datasets were obtained with the MBDCap-seq protocol, a technique used to capture methylated DNA by using a methyl-CpG binding domain (MBD) protein column followed by next-generation sequencing[25,29]. We performed comparative analyses of DNA methylation profiles between normal controls and BC samples, which were downloaded from the CMS. "
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    ABSTRACT: Background Reprimo (RPRM), a highly glycosylated protein, is a new downstream effector of p53-induced cell cycle arrest at the G2/M checkpoint, and a putative tumor suppressor gene frequently silenced via methylation of its promoter region in several malignances. The aim of this study was to characterize the epigenetic inactivation and its biological function in BC cell lines. Methods The correlation between RPRM methylation and loss of mRNA expression was assessed in six breast cancer cell lines by methylation specific PCR (MSP), 5′-Aza-2′-deoxycytidine treatment and RT-PCR assays. MDA-MB-231 cells were chosen to investigate the phenotypic effect of RPRM in cell proliferation, cell cycle, cell death, cell migration and invasion. Results In the cancer methylome system (CMS) (web-based system for visualizing and analyzing genome-wide methylation data of human cancers), the CpG island region of RPRM (1.1 kb) was hypermethylated in breast cancer compared to normal breast tissue; more interesting still was that ERα(+) tumors showed higher methylation intensity than ERα(−). Downregulation of RPRM mRNA by methylation was confirmed in MDA-MB-231 and BT-20 cell lines. In addition, overexpression of RPRM in MDA-MB-231 cells resulted in decreased rates of cell migration, wound healing and invasion in vitro. However, RPRM overexpression did not alter cell viability, phosphatidylserine (PS) translocation or G2/M cell cycle transition. Conclusion Taken together, these data suggest that RPRM is involved in decreased cell migration and invasion in vitro, acting as a potential tumor suppressor gene in the MDA-MB-231 cell line.
    Full-text · Article · Dec 2016 · Biological research
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    • "Moreover, although it has been shown that certain human diseases may have evolutionary epigenetic origins [11] [12], it remains largely unknown how patterns of DNA methylation differ between closely related species and whether such differences contribute to species-specific phenotypes [11]. Some methylation databases [13] [14] [15] and CGI databases [16] have been developed , but, to our knowledge, no existing genome browser addresses specifically the evolutionary relationships between the CGIs from different species. To help describing and understanding the function as well as the mechanisms generating and maintaining CGIs within an evolutionary context , we develop here CpGislandEVO (http://bioinfo2.ugr.es/ "
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    ABSTRACT: Hypomethylated, CpG-rich DNA segments (CpG islands, CGIs) are epigenome markers involved in key biological processes. Aberrant methylation is implicated in the appearance of several disorders as cancer, immunodeficiency, or centromere instability. Furthermore, methylation differences at promoter regions between human and chimpanzee strongly associate with genes involved in neurological/psychological disorders and cancers. Therefore, the evolutionary comparative analyses of CGIs can provide insights on the functional role of these epigenome markers in both health and disease. Given the lack of specific tools, we developed CpGislandEVO. Briefly, we first compile a database of statistically significant CGIs for the best assembled mammalian genome sequences available to date. Second, by means of a coupled browser front-end, we focus on the CGIs overlapping orthologous genes extracted from OrthoDB, thus ensuring the comparison between CGIs located on truly homologous genome segments. This allows comparing the main compositional features between homologous CGIs. Finally, to facilitate nucleotide comparisons, we lifted genome coordinates between assemblies from different species, which enables the analysis of sequence divergence by direct count of nucleotide substitutions and indels occurring between homologous CGIs. The resulting CpGislandEVO database, linking together CGIs and single-cytosine DNA methylation data from several mammalian species, is freely available at our website.
    Full-text · Article · Sep 2013
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    ABSTRACT: Whole genome methylation profiling at a single cytosine resolution is now feasible due to the advent of high-throughput sequencing techniques together with bisulfite treatment of the DNA. To obtain the methylation value of each individual cytosine, the bisulfite-treated sequence reads are first aligned to a reference genome, and then the profiling of the methylation levels is done from the alignments. A huge effort has been made to quickly and correctly align the reads and many different algorithms and programs to do this have been created. However, the second step is just as crucial and non-trivial, but much less attention has been paid to the final inference of the methylation states. Important error sources do exist, such as sequencing errors, bisulfite failure, clonal reads, and single nucleotide variants. We developed MethylExtract, a user friendly tool to: i) generate high quality, whole genome methylation maps and ii) detect sequence variation within the same sample preparation. The program is implemented into a single script and takes into account all major error sources. MethylExtract detects variation (SNVs - Single Nucleotide Variants) in a similar way to VarScan, a very sensitive method extensively used in SNV and genotype calling based on non-bisulfite-treated reads. The usefulness of MethylExtract is shown by means of extensive benchmarking based on artificial bisulfite-treated reads and a comparison to a recently published method, called Bis-SNP. MethylExtract is able to detect SNVs within High-Throughput Sequencing experiments of bisulfite treated DNA at the same time as it generates high quality methylation maps. This simultaneous detection of DNA methylation and sequence variation is crucial for many downstream analyses, for example when deciphering the impact of SNVs on differential methylation. An exclusive feature of MethylExtract, in comparison with existing software, is the possibility to assess the bisulfite failure in a statistical way. The source code, tutorial and artificial bisulfite datasets are available at http://bioinfo2.ugr.es/MethylExtract/ and http://sourceforge.net/projects/methylextract/, and also permanently accessible from 10.5281/zenodo.7144.
    Full-text · Article · Jan 2013 · F1000 Research
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