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

Ongenaert M, Van Neste L, De Meyer T, Menschaert G, Bekaert S, Van Criekinge W - Nucleic Acids Res. (2007)

Bottom Line: Epigenetics, and more specifically DNA methylation is a fast evolving research area.Therefore, it would be extremely useful to have an annotated, reviewed, sorted and summarized overview of all available data.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Biotechnology, Faculty of Bioscience Engineering, Laboratory for Bioinformatics and Computational Genomics, Ghent University, B-9000 Ghent, Belgium. mate.ongenaert@ugent.be

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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Related in: MedlinePlus

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.
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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.

Mentions: Abstracts are then manually reviewed, taking into account the order after ranking, with the aid of highlighting the different keyword lists, aliases and sentences with alias and methylation-related keyword in different colors. Aliases are linked with gene information using hover-over effects generated with JavaScript and CSS. After manual reviewing, the information in the database only has to be minimally updated or corrected. A schematic overview of the complete process is given in Figure 1. This process is still in progress; due to the ranking system the most important publications are currently in the database. The remaining abstracts will be reviewed soon, and an accurate update strategy will be developed.Figure 1.


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

Ongenaert M, Van Neste L, De Meyer T, Menschaert G, Bekaert S, Van Criekinge W - Nucleic Acids Res. (2007)

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.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2238841&req=5

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.
Mentions: Abstracts are then manually reviewed, taking into account the order after ranking, with the aid of highlighting the different keyword lists, aliases and sentences with alias and methylation-related keyword in different colors. Aliases are linked with gene information using hover-over effects generated with JavaScript and CSS. After manual reviewing, the information in the database only has to be minimally updated or corrected. A schematic overview of the complete process is given in Figure 1. This process is still in progress; due to the ranking system the most important publications are currently in the database. The remaining abstracts will be reviewed soon, and an accurate update strategy will be developed.Figure 1.

Bottom Line: Epigenetics, and more specifically DNA methylation is a fast evolving research area.Therefore, it would be extremely useful to have an annotated, reviewed, sorted and summarized overview of all available data.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Biotechnology, Faculty of Bioscience Engineering, Laboratory for Bioinformatics and Computational Genomics, Ghent University, B-9000 Ghent, Belgium. mate.ongenaert@ugent.be

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.

Show MeSH
Related in: MedlinePlus