Welcome to GenoMesh!

GenoMesh is a genome-wide analysis of gene-to-gene relationships and pathways based on the association between individual genes and MeSH terms obtained from the literature. The Medical Subject Headings (MeSH) system is a controlled vocabulary of medical and scientific terms for indexing articles in the MEDLINE and PubMed literature database. The 2013 MeSH contains over 26,800 MeSH descriptors organized in a hierarchal fashion based on 16 top-level categories. Over 210,000 MeSH entry terms also exist to assist in finding the most appropriate MeSH Headings. All the MeSH terms are assigned to individual PubMed articles manually by knowledgeable biomedical scientists. The terminology used in MeSH provides a unique and consistent approach to retrieve information that uses different terminologies to describe similar biological and/or medical concepts. GenoMesh uses MeSH terms associated with PubMed papers as signatures to characterize the genes associated with the same papers. The MeSH term-gene associations are used in our GenoMesh algorithm to identify existing and predict new gene-gene associations and interactions.

The web-based GenoMesh systems includes several tools generated based on our novel GenoMesh algorithm:

• GeneMesh: GeneMesh identifies all genes and MeSH terms that are associated to a queried gene with dissimilarity scores below a threshold value.
• GenePair: GenePair calculates the dissimilarities between two queried genes using the GenoMesh algorithm, and outputs ranks and P-values.
• GeneCluster: GeneCluster groups all genes within a specific genome by hierarchical clustering.
• GeneNet: GeneNet outputs a network among a list of input genes based on the GenoMesh algorithm.
• MeSHBrowse: MeSHBrowse displays the hierarchical tree structure of MeSH and assigns genes to individual MeSH terms.

This GenoMesh website also includes many other pages, such as statistics, downloads, Introduction, etc. Please check the left side navigation bar to identify specific web pages in which you are interested.

At present GenoMesh contains literature mining data for the well-studied model organism Escherichia coli and a much less-studied bacterium Brucella. We are aiming to include more microbial genomes in the further GenoMesh development. Meanwhile, the general GenoMesh algorithm is likely applicable to the study of eukaryotic systems (e.g., human and mouse) as well as the interactions between host and pathogens.

Your suggestions and comments are welcome and appreciated. Thank you!

 

CItation: Please cite the following reference for Genomesh:

Xiang Z, Qin T, Qin Z, He Y. A genome-wide MeSH-based literature mining system predicts implicit gene-to-gene relationships and networks. BMC Systems Biology. 2013, 7(Suppl 3):S9. doi:10.1186/1752-0509-7-S3-S9. (note: this paper was also presented in InCoB2013 and collected in F1000Posters; see the presentation slides)