Biopatternsg is a system that builds gene regulatory networks from a basic collection of biological objects and DNA data, gathering and processing PubMed abstracts, using Internet available informational resources like Mesh, GeneOntology, UniProt, PDB and others, and that uses logical engines, prolog and java code, to integrate and analyze them. To carry out the modeling of such networks, the scientific community uses and develops various computer services available through portals available on the Internet. Some of these portals are: GeneOntology, PDB, HGNC, Pathway Commons, UniProt, PubMed, among others. The mentioned sites provide services that make possible to access such services automatically and therefore, these can be used to organize knowledge bases that integrate their resources. Our team has developed a system that allows such integration and the analysis of the information obtained in different modalities. Services such as automatic identity modeling (e.g. receptor, enzyme, etc.), molecular functions, and biological processes for the objects in a network and their protein-protein interactions, have been implemented. Our work describes an ontological and logical alternative for the discovery of biological signaling pathways and regulatory subnetworks within an GRN. This document describes the system’s capabilities and modes of use, which we hope will facilitate the definition of adjustments and new requirements, aimed at improving the utility of the system we have called biopatternsg (biopatterns searching). For this purpose, an example of modeling and analysis is used, in which it is desired to explore possible links between the regulatory processes inherent in SARS-COV and SARS-COV-2. At the moment the example mentioned only illustrates the use of the system, therefore its results are for academic purposes only. This document includes an annex that allows observing the trace of the bioPatternsg execution and the knowledge bases it generates, as well as details regarding its downloading and installation.

Please, do not hesitate to contact us at this email address: biopatternsg@gmail.com, for any problem,comment or doubt.

How to Cite: Lopez J., Ramirez Y., Dávila J., Bastidas M. A logical and ontological framework for knowledge discovery on gene regulatory networks. Case study: Bile Acid and Xenobiotic System (BAXS). Journal of Bioinformatics and Genomics, [S.l.], n. 2 (14), dec. 2020. ISSN 2530-1381. Available at: http://journal-biogen.org/article/view/236. Date accessed: 19 dec. 2020. doi: http://dx.doi.org/10.18454/jbg.2020.2.14.1.

Please, see the wiki for details about the system's installation and the system's user's guide.