PubMeth

Category Cross-Omics>Data/Text Mining Systems/Tools and Cross-Omics>Knowledge Bases/Databases/Tools

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

PubMeth is based on text-mining of Medline/PubMed abstracts, combined with manual reading and annotation of pre-selected 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.

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].

DNA methylation analysis --

DNA methylation in cancer research has evolved to a mainstream research topic. Methylation profiles are successfully used in early detection and personalized treatment. However, more and more data is available, especially with the availability of large-scale screening techniques.

All the information taken together determines the knowledge of the ‘cancer methylome’. Ultimately, the epigenome of all cancer tissues, including those of different stage and grade, could be mapped out.

Epigenetic states differ widely among tissues, and changes are far more varied and much more frequent per tumor than DNA mutations. Each differentiated cell has a different epigenome.

In this perspective, it is very useful to extract which genes are already reported in which cancer types from literature.

This information might be used as positive controls, to check the same genes in other (related) cancer types, to screen for markers that could be used as early diagnostic utility or in the context of personalized medicine and to deepen the knowledge of the mechanisms of methylation.

PubMeth tries to contain and summarize as many available literature data and presents them in an easy to use graphical interface.

It speeds up the process of searching relevant literature, many aliases and keywords are searched at the same time and the results are reliable as they are manually reviewed as one would do when performing a manual literature search.

PubMeth - Querying the Database --

A record in the database contains information about the source of publication, the gene, and the cancer type and subtypes if specified.

It includes the number of primary cancer samples where methylation is analyzed in, as well as the number of analyzed cell lines and the number of normal tissues.

For all these three (3) categories the methylation frequency (the percentage of the samples that show methylation) is also available.

Other information includes the detection technologies used and an ‘evidence sentence’ where most of the information in this record came from.

PubMeth can be queried using the web-interface at the manufacturer’s web-site in two (2) ways, depending on the researcher’s focus:

1) Gene-related: in which cancer types (and subtypes) the genes of interest are reported to be methylated; and

2) Cancer-related: which genes are reported to be methylated in the cancer types/subtypes.

Gene-centric query -

A query is created in two (2) easy steps.

In the first step, the user provides a list of genes (different identifiers are accepted: gene symbol or name, RefSeq, Ensembl ID, etc.).

The query is analyzed using local symbol/alias lists, generated using GeneCards, and suggestions are presented to the user.

In the second step, the user reviews the selections made (most likely, the genes selected due to intelligent sorting in the background are correct) and submits his/her choices.

At this point, the results will be generated and the main result page is presented to the user. This main result page ranks the genes, based on the number of references to the gene in the database.

A graphical summary representation of the number of references, the number of primary samples and the mean methylation frequency within different cancer types is also given.

On the Summary page of a gene-centric query (Not shown here…), 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.

Note: The summary is very useful if multiple genes are searched at once; this feature is what distinguishes this database from previous efforts.

One practical usage example would be that, using a pharmacologic demethylation approach in cell lines, 50 candidate genes are selected. The question then is to sub-select genes to verify in primary cancer samples, often based on time-consuming literature searches. This selection is facilitated by the summarization view of PubMeth.

From this main page, one can go to the detailed pages, focusing on a selected gene in a certain cancer type. On such a detailed page, graphical representations of the number of references in the database, the total number of samples and the mean methylation frequency are displayed for the different cancer types and their subtypes.

The complete individual records, linked with their original PubMed record, are shown.

Users can also choose to browse a precomputed gene-list. The advantage here is that the user can browse all genes in PubMeth without having to query the database, which is significantly faster. However, the summary view is Not available.

Cancer-centric query --

A cancer-centric query is executed in one easy step: the user selects cancer types (and/or subtypes up to three levels -- e.g. lymphoma, non-Hodgkin lymphoma, b-cell lymphoma and diffuse large B-cell).

An overview (in the same style as the gene-centric searching approach - see above...) of the genes that are most commonly described as methylated in the selected cancer types, as well as the total number of samples and the mean methylation frequency is returned.

From this summary page, navigating to the detailed pages is intuitive.

This type of search is meant to give a quick overview of the genes that are reported in the methylation context in the cancer (sub) types of interest and in which frequency, to explore methylation in the cancer types of interest, to compare experimental results with or to perform, in a next step, a gene-centric search on these genes for full details in all cancer (sub) types.

Note: A screencast (tutorial) that dynamically shows how to query PubMeth is available on the PubMeth website.

System Requirements

Contact manufacturer.

Manufacturer

Manufacturer Web Site PubMeth

Price Contact manufacturer.

G6G Abstract Number 20760

G6G Manufacturer Number 104340