Multiple network algorithm for epigenetic modules via the integration of genome-wide DNA methylation and gene expression data

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Background

With the increase in the amount of DNA methylation and gene expression data, the epigenetic mechanisms of cancers can be extensively investigate. Available methods integrate the DNA methylation and gene expression data into a network by specifying the anti-correlation between them. However, the correlation between methylation and expression is usually unknown and difficult to determine.

Results

To address this issue, we present a novel multiple network framework for epigenetic modules, namely, Epigenetic Module based on Differential Networks ( EMDN) algorithm, by simultaneously analyzing DNA methylation and gene expression data. The EMDN algorithm prevents the specification of the correlation between methylation and expression. The accuracy of EMDN algorithm is more efficient than that of modern approaches. On the basis of The Cancer Genome Atlas (TCGA) breast cancer data, we observe that the EMDN algorithm can recognize positively and negatively correlated modules and these modules are significantly more enriched in the known pathways than those obtained by other algorithms. These modules can serve as bio-markers to predict breast cancer subtypes by using methylation profiles, where positively and negatively correlated modules are of equal importance in the classification of cancer subtypes. Epigenetic modules also estimate the survival time of patients, and this factor is critical for cancer therapy.

Conclusions

The proposed model and algorithm provide an effective method for the integrative analysis of DNA methylation and gene expression. The algorithm is freely available as an R-package at https://github.com/william0701/EMDN.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1490-6) contains supplementary material, which is available to authorized users.

Related collections

Most cited references 21

Record: found
Abstract: found
Article: not found

Gene Ontology: tool for the unification of biology

Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)

Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.

0 comments Cited 15297 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Modularity and community structure in networks

M. Newman (2006)

Many networks of interest in the sciences, including a variety of social and biological networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure has attracted considerable recent attention. One of the most sensitive detection methods is optimization of the quality function known as "modularity" over the possible divisions of a network, but direct application of this method using, for instance, simulated annealing is computationally costly. Here we show that the modularity can be reformulated in terms of the eigenvectors of a new characteristic matrix for the network, which we call the modularity matrix, and that this reformulation leads to a spectral algorithm for community detection that returns results of better quality than competing methods in noticeably shorter running times. We demonstrate the algorithm with applications to several network data sets.

0 comments Cited 1190 times – based on 0 reviews

Preprint

     Review now

Bookmark

Record: found
Abstract: found
Article: not found

Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning.

Shawn Cokus, Suhua Feng, Xiaoyu Zhang … (2008)

Cytosine DNA methylation is important in regulating gene expression and in silencing transposons and other repetitive sequences. Recent genomic studies in Arabidopsis thaliana have revealed that many endogenous genes are methylated either within their promoters or within their transcribed regions, and that gene methylation is highly correlated with transcription levels. However, plants have different types of methylation controlled by different genetic pathways, and detailed information on the methylation status of each cytosine in any given genome is lacking. To this end, we generated a map at single-base-pair resolution of methylated cytosines for Arabidopsis, by combining bisulphite treatment of genomic DNA with ultra-high-throughput sequencing using the Illumina 1G Genome Analyser and Solexa sequencing technology. This approach, termed BS-Seq, unlike previous microarray-based methods, allows one to sensitively measure cytosine methylation on a genome-wide scale within specific sequence contexts. Here we describe methylation on previously inaccessible components of the genome and analyse the DNA methylation sequence composition and distribution. We also describe the effect of various DNA methylation mutants on genome-wide methylation patterns, and demonstrate that our newly developed library construction and computational methods can be applied to large genomes such as that of mouse.

0 comments Cited 796 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Xiaoke Ma: xkma@xidian.edu.cn

Zaiyi Liu: zyliu@163.com

Zhongyuan Zhang: zhyuanzh@gmail.com

Xiaotai Huang: xthuang@xidian.edu.cn

Wanxin Tang: tangwx@scu.edu.cn

Journal

Journal ID (nlm-ta): BMC Bioinformatics

Journal ID (iso-abbrev): BMC Bioinformatics

Title: BMC Bioinformatics

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2105

Publication date (Electronic): 31 January 2017

Publication date PMC-release: 31 January 2017

Publication date Collection: 2017

Volume: 18

Electronic Location Identifier: 72

Affiliations

[1 ]ISNI 0000 0001 0707 115X, GRID grid.440736.2, School of Computer Science and Technology, , Xidian University, ; No.2 South TaiBai Road, Xi’an, People’s Republic of China

[2 ]Xidian-Ningbo Information Technology Institute, Xidian University, No. 777 Zhongguanxi Road, Ningbo, People’s Republic of China

[3 ]GRID grid.410643.4, Department of Radiology, Guangdong General Hospital, , Guangdong Academy of Medical Sciences, ; Zhongshan Road, Guangzhou, People’s Republic of China

[4 ]ISNI 0000 0000 9894 8211, GRID grid.411054.5, School of Statistics and Mathematics, , Central University of Finance and Economics, ; 39 South College Road, Haidian District, Beijing, People’s Republic of China

[5 ]ISNI 0000 0001 0807 1581, GRID grid.13291.38, Department of Nephrology, West China Hospital, , Sichuan University, ; Wuhou District, Chengdu, People’s Republic of China

Article

Publisher ID: 1490

DOI: 10.1186/s12859-017-1490-6

PMC ID: 5282853

PubMed ID: 28137264

SO-VID: 87980b9c-05ab-4d8f-9a64-614c77059e6d

License:

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.