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. 2008 Jan;36(Database issue):D842-6.
doi: 10.1093/nar/gkm788. Epub 2007 Oct 11.

PubMeth: a cancer methylation database combining text-mining and expert annotation

Maté Ongenaert  1 , Leander Van Neste, Tim De Meyer, Gerben Menschaert, Sofie Bekaert, Wim Van Criekinge
Affiliations

PubMeth: a cancer methylation database combining text-mining and expert annotation

Maté Ongenaert et al. Nucleic Acids Res. 2008 Jan.

Abstract

Epigenetics, and more specifically DNA methylation is a fast evolving research area. In almost every cancer type, each month new publications confirm the differentiated regulation of specific genes due to methylation and mention the discovery of novel methylation markers. Therefore, it would be extremely useful to have an annotated, reviewed, sorted and summarized overview of all available data. PubMeth is a cancer methylation database that includes genes that are reported to be methylated in various cancer types. A query can be based either on genes (to check in which cancer types the genes are reported as being methylated) or on cancer types (which genes are reported to be methylated in the cancer (sub) types of interest). The database is freely accessible at http://www.pubmeth.org. PubMeth is based on text-mining of Medline/PubMed abstracts, combined with manual reading and annotation of preselected abstracts. The text-mining approach results in increased speed and selectivity (as for instance many different aliases of a gene are searched at once), while the manual screening significantly raises the specificity and quality of the database. The summarized overview of the results is very useful in case more genes or cancer types are searched at the same time.

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Figures

Figure 1.
Figure 1.
Scheme that illustrates the initial filling up of database using text-mining. Aliases of genes and different keyword lists (methylation, cancer and detection-related) are highlighted in the abstract. At the same time, different parameters are counted and stored in a MySQL relational database. Afterwards, the data is ranked and manually reviewed.
Figure 2.
Figure 2.
Summary page of a gene-centric query. The different colors represent the frequency of methylation of the gene in the different cancer types (what percentage of the samples showed methylation), while the numbers indicate the total number of primary samples tested for methylation.
See this image and copyright information in PMC

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