MethyCancer: the database of human DNA methylation and cancer

Cancer is ranked as one of the top killers in all human diseases and continues to have a devastating effect on the population around the globe. Current research efforts are aiming to accelerate our understanding of the molecular basis of cancer and develop effective means for cancer diagnostics, treatment and prognosis. An altered pattern of epigenetic modifications, most importantly DNA methylation events, plays a critical role in tumorigenesis through regulating oncogene activation, tumor suppressor gene silencing and chromosomal instability. To study interplay of DNA methylation, gene expression and cancer, we developed a publicly accessible database for human DNA Methylation and Cancer (MethyCancer, http://methycancer.genomics.org.cn). MethyCancer hosts both highly integrated data of DNA methylation, cancer-related gene, mutation and cancer information from public resources, and the CpG Island (CGI) clones derived from our large-scale sequencing. Interconnections between different data types were analyzed and presented. Furthermore, a powerful search tool is developed to provide user-friendly access to all the data and data connections. A graphical MethyView shows DNA methylation in context of genomics and genetics data facilitating the research in cancer to understand genetic and epigenetic mechanisms that make dramatic changes in gene expression of tumor cells

MeInfoText: associated gene methylation and cancer information from text mining

<p>Abstract</p> <p>Background</p> <p>DNA methylation is an important epigenetic modification of the genome. Abnormal DNA methylation may result in silencing of tumor suppressor genes and is common in a variety of human cancer cells. As more epigenetics research is published electronically, it is desirable to extract relevant information from biological literature. To facilitate epigenetics research, we have developed a database called MeInfoText to provide gene methylation information from text mining.</p> <p>Description</p> <p>MeInfoText presents comprehensive association information about gene methylation and cancer, the profile of gene methylation among human cancer types and the gene methylation profile of a specific cancer type, based on association mining from large amounts of literature. In addition, MeInfoText offers integrated protein-protein interaction and biological pathway information collected from the Internet. MeInfoText also provides pathway cluster information regarding to a set of genes which may contribute the development of cancer due to aberrant methylation. The extracted evidence with highlighted keywords and the gene names identified from each methylation-related abstract is also retrieved. The database is now available at <url>http://mit.lifescience.ntu.edu.tw/</url>.</p> <p>Conclusion</p> <p>MeInfoText is a unique database that provides comprehensive gene methylation and cancer association information. It will complement existing DNA methylation information and will be useful in epigenetics research and the prevention of cancer.</p

DiseaseMeth: a human disease methylation database

DNA methylation is an important epigenetic modification for genomic regulation in higher organisms that plays a crucial role in the initiation and progression of diseases. The integration and mining of DNA methylation data by methylation-specific PCR and genome-wide profiling technology could greatly assist the discovery of novel candidate disease biomarkers. However, this is difficult without a comprehensive DNA methylation repository of human diseases. Therefore, we have developed DiseaseMeth, a human disease methylation database (http://bioinfo.hrbmu.edu.cn/diseasemeth). Its focus is the efficient storage and statistical analysis of DNA methylation data sets from various diseases. Experimental information from over 14 000 entries and 175 high-throughput data sets from a wide number of sources have been collected and incorporated into DiseaseMeth. The latest release incorporates the gene-centric methylation data of 72 human diseases from a variety of technologies and platforms. To facilitate data extraction, DiseaseMeth supports multiple search options such as gene ID and disease name. DiseaseMeth provides integrated gene methylation data based on cross-data set analysis for disease and normal samples. These can be used for in-depth identification of differentially methylated genes and the investigation of gene–disease relationship

NGSmethDB: an updated genome resource for high quality, single-cytosine resolution methylomes

The updated release of ‘NGSmethDB’ (http://bioinfo2.ugr.es/NGSmethDB) is a repository for single-base whole-genome methylome maps for the best-assembled eukaryotic genomes. Short-read data sets from NGS bisulfite-sequencing projects of cell lines, fresh and pathological tissues are first pre-processed and aligned to the corresponding reference genome, and then the cytosine methylation levels are profiled. One major improvement is the application of a unique bioinformatics protocol to all data sets, thereby assuring the comparability of all values with each other. We implemented stringent quality controls to minimize important error sources, such as sequencing errors, bisulfite failures, clonal reads or single nucleotide variants (SNVs). This leads to reliable and high-quality methylomes, all obtained under uniform settings. Another significant improvement is the detection in parallel of SNVs, which might be crucial for many downstream analyses (e.g. SNVs and differential-methylation relationships). A next-generation methylation browser allows fast and smooth scrolling and zooming, thus speeding data download/upload, at the same time requiring fewer server resources. Several data mining tools allow the comparison/retrieval of methylation levels in different tissues or genome regions. NGSmethDB methylomes are also available as native tracks through a UCSC hub, which allows comparison with a wide range of third-party annotations, in particular phenotype or disease annotations.Spanish Government [BIO2008-01353 to J.L.O. and BIO2010-20219 to M.H.], and Basque country ‘AE’ grant (to G.B.). Funding for open access charge: Department of Genetics, University of Granada, Spain

EpiFactors : a comprehensive database of human epigenetic factors and complexes

Altres ajuts: Russian Fund For Basic Research(RFFI)grant 14-04-0018 i grant 15-34-20423, Ake Olsson's foundation, Swedish Cancer foundation, Swedish Childhood cancer foundation, Dynasty Foundation Fellowship, RIKEN Omics Science Center, RIKEN Preventive Medicine and Diagnosis Innovation Program i RIKEN Center for Life Science Technologies.Abstract: Epigenetics refers to stable and long-term alterations of cellular traits that are not caused by changes in the DNA sequence per se. Rather, covalent modifications of DNA and histones affect gene expression and genome stability via proteins that recognize and act upon such modifications. Many enzymes that catalyse epigenetic modifications or are critical for enzymatic complexes have been discovered, and this is encouraging investigators to study the role of these proteins in diverse normal and pathological processes. Rapidly growing knowledge in the area has resulted in the need for a resource that compiles, organizes and presents curated information to the researchers in an easily accessible and user-friendly form. Here we present EpiFactors, a manually curated database providing information about epigenetic regulators, their complexes, targets and products. EpiFactors contains information on 815 proteins, including 95 histones and protamines. For 789 of these genes, we include expressions values across several samples, in particular a collection of 458 human primary cell samples (for approximately 200 cell types, in many cases from three individual donors), covering most mammalian cell steady states, 255 different cancer cell lines (representing approximately 150 cancer subtypes) and 134 human postmortem tissues. Expression values were obtained by the FANTOM5 consortium using Cap Analysis of Gene Expression technique. EpiFactors also contains information on 69 protein complexes that are involved in epigenetic regulation. The resource is practical for a wide range of users, including biologists, pharmacologists and clinicians

eL-DASionator: an LDAS upload file generator

BACKGROUND: The Distributed Annotation System (DAS) allows merging of DNA sequence annotations from multiple sources and provides a single annotation view. A straightforward way to establish a DAS annotation server is to use the "Lightweight DAS" server (LDAS). Onto this type of server, annotations can be uploaded as flat text files in a defined format. The popular Ensembl ContigView uses the same format for the transient upload and display of user data. RESULTS: In order to easily generate LDAS upload files we developed a software tool that is accessible via a web-interface . Users can submit their DNA sequences of interest. Our program (i) aligns these sequences to the reference sequences of Ensembl, (ii) determines start and end positions of each sequence on the reference sequence, and (iii) generates a formatted annotation file. This file can be used to load any LDAS annotation server or it can be uploaded to the Ensembl ContigView. CONCLUSION: The eL-DASionator is an on-line tool that is intended for life-science researchers with little bioinformatics background. It conveniently generates LDAS upload files, and makes it possible to generate annotations in a standard format that permits comfortable sharing of this data

Data analysis and creation of epigenetics database

Indiana University-Purdue University Indianapolis (IUPUI)This thesis is aimed at creating a pipeline for analyzing DNA methylation epigenetics data and creating a data model structured well enough to store the analysis results of the pipeline. In addition to storing the results, the model is also designed to hold information which will help researchers to decipher a meaningful epigenetics sense from the results made available. Current major epigenetics resources such as PubMeth, MethyCancer, MethDB and NCBI’s Epigenomics database fail to provide holistic view of epigenetics. They provide datasets produced from different analysis techniques which raises an important issue of data integration. The resources also fail to include numerous factors defining the epigenetic nature of a gene. Some of the resources are also struggling to keep the data stored in their databases up-to-date. This has diminished their validity and coverage of epigenetics data. In this thesis we have tackled a major branch of epigenetics: DNA methylation. As a case study to prove the effectiveness of our pipeline, we have used stage-wise DNA methylation and expression raw data for Lung adenocarcinoma (LUAD) from TCGA data repository. The pipeline helped us to identify progressive methylation patterns across different stages of LUAD. It also identified some key targets which have a potential for being a drug target. Along with the results from methylation data analysis pipeline we combined data from various online data reserves such as KEGG database, GO database, UCSC database and BioGRID database which helped us to overcome the shortcomings of existing data collections and present a resource as complete solution for studying DNA methylation epigenetics data

Content evaluation and tool development for knowledge management system

Genetic and epigenetic mechanisms play vital roles in the initiation and progression of cancer. The motivation of the work reported here is thus to support research in this area, by investigating genetic and epigenetic mechanisms and the inter-relationships between them through provision of a platform (in-house biomedical resource (StatEpigen)) for data collation and analysis. StatEpigen is targeted initially to collating information on colon cancer and the basic aim of this project is to enhance, evaluate and ensure robustness of this resource. Elements involved in building towards a more comprehensive 'picture of needs' to date include: a comparative study of available epigenetic/epigenomic biomedical resources, manual augmentation of StatEpigen database resource and an in-depth analysis of a set of germline-mutated colon cancer genes from the phylogenetic perspective, to link resource provision to the experimental base and address key bioinformatics questions. Comparative study has confirmed the current importance of epigenetic studies and provided information on resources that may offer integration potential for StatEpigen. Manual data augmentatrion (15% contribution to the current datasource, URL: http://statepigen.sci-sym.dcu.ie/) permitted assessment of the data curation process itself, and also motivation and planning for some degree of future automation. The in-depth genetic analysis addresses a specific-research question relating to the suitability ofthe murine model as a reference organism for colon cancer in humans. Analysis of the mouse parallel (following 180MY of independent evolution) revealed that some genes can not be used as suitable cancer model for humans. This finding provided stimulus for developed analyses (e.g. through StatEpigen) of related epigenetic characteristics and genetic-epigenetic interactions that are influential in the initiation and progression of the disease. A future focus for StatEpigen includes exploitation ofthe data already gathered, as well as tool development for automation of the data augmentation process