Curated Database Analysis (Free Downloads)

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PubGene 2.6™ Database and Analysis Software

PubGene

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Description: PubGene 2.6 database and analysis software is designed to identify using Natural Language Processing (NLP) the relationships between genes and proteins, diseases, cell processes, functions, mutations, cellular components, and drugs based on their co-occurrence in the abstracts of scientific papers, on their sequence homology, and statistical probability of their co-occurrence with each other. In addition, PubGene allows researchers to analyze large gene lists coupled with the results of microarray experiments, survival tests, cluster genes and proteins and link the results directly to the literature knowledge domain, sequence homology domain, or function / process domain.

PubGene is offered for local installation on Unix / Linux /Mac / Windows platforms. Currently, PubGene contains data for 21 different organisms including H. sapiens, M. musculus, R. norvegicus, E.coli, A. thaliana, B. taurus and B. anthracis genes and proteins. Additionally, PubGene, Inc. is offering to integrate any proprietary database that a customer might have in-house for use with the local PubGene installation. PubGene package is also available in the form of PubGene On-Line commercial service.

Network Browser Toollets you mine text databases for genes or proteins that have been co-cited with the gene or protein of interest with a user-defined set of parameters. The search is based on the primary gene or protein symbol and all the synonym names of the gene or protein of interest are included in the search. You can now get gene and/or protein networks searching through the literature with all 21 PubGene organisms in mind. It is also possible to build networks for multiple terms(e.g., a protein list, or a gene cluster) to find out how several genes or proteins of interest are related to each other and, optionally, to other genes and proteins through a user-defined set of parameters in literature, sequence homology, or statistical probability domains. The user can add more complexity to the search by entering keywords (such as diseases, processes, or functions) to filter out only the genes or proteins relevant to a certain pathway or disease.

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