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To date, research on inflammatory bowel disease (IBD, encompassing Crohn’s disease and ulcerative colitis), a chronic complex disorder, has generated a large amount of data scattered across published literature (1 06 333) listed in PubMed on 14 October 2020, and no dedicated database currently exists that catalogues information on genes associated with IBD. We aimed to manually curate 289 genes that are experimentally validated to be linked with IBD and its known phenotypes. Furthermore, we have developed an integrated platform providing information about different aspects of these genes by incorporating several resources and an extensive text-mined knowledgebase. The curated IBD database (IBDDB) allows the selective display of collated 34 subject-specific concepts (listed as columns) exportable through a user-friendly IBDDB portal. The information embedded in concepts was acquired via text-mining of PubMed (manually cleaned and curated), accompanied by data-mining from varied resources. The user can also explore different biomedical entities and their co-occurrence with other entities (about one million) from 11 curated dictionaries in the indexed PubMed records. This functionality permits the user to generate and cross-examine a new hypothesis that is otherwise not easy to comprehend by just reading the published abstracts and papers. Users can download required information using various file formats and can display information in the form of networks. To our knowledge, no curated database of IBD-related genes is available so far. IBDDB is free for academic users and can be accessed at https://www.cbrc.kaust.edu.sa/ibd/.

Microorganisms produce an enormous variety of chemical compounds. It is of general interest for microbiology and biotechnology researchers to have means to explore information about molecular and genetic basis of functioning of different microorganisms and their ability for bioproduction. To enable such exploration, we compiled 45 topic-specific knowledgebases (KBs) accessible through DESM portal (www.cbrc.kaust.edu.sa/desm). The KBs contain information derived through text-mining of PubMed information and complemented by information data-mined from various other resources (e.g. ChEBI, Entrez Gene, GO, KOBAS, KEGG, UniPathways, BioGrid). All PubMed records were indexed using 4,538,278 concepts from 29 dictionaries, with 1 638 986 records utilized in KBs. Concepts used are normalized whenever possible. Most of the KBs focus on a particular type of microbial activity, such as production of biocatalysts or nutraceuticals. Others are focused on specific categories of microorganisms, e.g. streptomyces or cyanobacteria. KBs are all structured in a uniform manner and have a standardized user interface. Information exploration is enabled through various searches. Users can explore statistically most significant concepts or pairs of concepts, generate hypotheses, create interactive networks of associated concepts and export results. We believe DESM will be a useful complement to the existing resources to benefit microbiology and biotechnology research.

Ovarian cancer (OC) is becoming the most common gynecological cancer in developed countries and the most lethal gynecological malignancy. It is also the fifth leading cause of all cancer-related deaths in women. The identification of diagnostic biomarkers and development of early detection techniques for OC largely depends on the understanding of the complex functionality and regulation of genes involved in this disease. Unfortunately, information about these OC genes is scattered throughout the literature and various databases making extraction of relevant functional information a complex task. To reduce this problem, we have developed a database dedicated to OC genes to support exploration of functional characterization and analysis of biological processes related to OC. The database contains general information about OC genes, enriched with the results of transcription regulation sequence analysis and with relevant text mining to provide insights into associations of the OC genes with other genes, metabolites, pathways and nuclear proteins. Overall, it enables exploration of relevant information for OC genes from multiple angles, making it a unique resource for OC and will serve as a useful complement to the existing public resources for those interested in OC genetics. Access is free for academic and non-profit users and database can be accessed at http://apps.sanbi.ac.za/ddoc/.

Enhancers are cis-acting DNA regulatory regions that play a key role in distal control of transcriptional activities. Identification of enhancers, coupled with a comprehensive functional analysis of their properties, could improve our understanding of complex gene transcription mechanisms and gene regulation processes in general. We developed DENdb, a centralized on-line repository of predicted enhancers derived from multiple human cell-lines. DENdb integrates enhancers predicted by five different methods generating an enriched catalogue of putative enhancers for each of the analysed cell-lines. DENdb provides information about the overlap of enhancers with DNase I hypersensitive regions, ChIP-seq regions of a number of transcription factors and transcription factor binding motifs, means to explore enhancer interactions with DNA using several chromatin interaction assays and enhancer neighbouring genes. DENdb is designed as a relational database that facilitates fast and efficient searching, browsing and visualization of information. Database URL: http://www.cbrc.kaust.edu.sa/dendb/.