Recent research has provided fascinating indications and evidence that the host health is linked to its microbial inhabitants. Due to the development of high-throughput sequencing technologies, more and more data covering microbial composition changes in different disease types are emerging. However, this information is dispersed over a wide variety of medical and biomedical disciplines. Disbiome is a database which collects and presents published microbiota-disease information in a standardized way. The diseases are classified using the MedDRA classification system and the micro-organisms are linked to their NCBI and SILVA taxonomy. Finally, each study included in the Disbiome database is assessed for its reporting quality using a standardized questionnaire. Disbiome is the first database giving a clear, concise and up-to-date overview of microbial composition differences in diseases, together with the relevant information of the studies published. The strength of this database lies within the combination of the presence of references to other databases, which enables both specific and diverse search strategies within the Disbiome database, and the human annotation which ensures a simple and structured presentation of the available data.

Quorum-sensing (QS) peptides are biologically attractive molecules, with a wide diversity of structures and prone to modifications altering or presenting new functionalities. Therefore, the Quorumpeps database (http://quorumpeps.ugent.be) is developed to give a structured overview of the QS oligopeptides, describing their microbial origin (species), functionality (method, result and receptor), peptide links and chemical characteristics (3D-structure-derived physicochemical properties). The chemical diversity observed within this group of QS signalling molecules can be used to develop new synthetic bio-active compounds.

Making data-querying and representation easier and more human consistent is an important research topic. In this context, fuzzy logic with its capability to model linguistic expressions provides an interesting framework, which has been adopted by many researchers. However, there are still some aspects that have not been adequately covered. In particular, it becomes widely advocated that while communicating, humans give both positive and negative information to state what they desire and what they reject. Because positive and negative statements do not necessarily mirror each other, this results in so-called heterogeneous bipolar information. Traditional fuzzy approaches do not adequately support the handling of heterogeneous bipolar information in information systems. Therefore, there is a need for more advanced techniques. In this paper, how bipolarity can be dealt with in the formulation and evaluation of selection conditions in fuzzy querying within a possibilistic, relational database framework is presented. Three novel query-evaluation techniques based on interval-valued fuzzy sets, Atanassov fuzzy sets, and twofold fuzzy sets are presented and compared with each other. Possibility theory is used to deal with uncertainty. Special attention is paid to the description of the semantics, use, benefits, and drawbacks of each formalism.