To meet the increasing demand in the field, numerous long noncoding RNA (lncRNA) databases are available. Given many lncRNAs are specifically expressed in certain cell types and/or time-dependent manners, most lncRNA databases fall short of providing such profiles. Researchers at the University of Louisville have developed a strategy using logic programming to handle the complex organization of organs, their tissues and cell types as well as gender and developmental time points. To showcase this strategy, they introduce ‘RenalDB’, a database providing expression profiles of RNAs in major organs focusing on kidney tissues and cells. RenalDB uses logic programming to describe complex anatomy, sample metadata and logical relationships defining expression, enrichment or specificity. The researchers validated the content of RenalDB with biological experiments and functionally characterized two long intergenic noncoding RNAs: LOC440173 is important for cell growth or cell survival, whereas PAXIP1-AS1 is a regulator of cell death. They anticipate RenalDB will be used as a first step toward functional studies of lncRNAs in the kidney.
(A) The database schema for the relational database portion of RenalDB. (B–D) Examples of the knowledge bases used in RenalDB. Knowledge bases are separated based on the type of information they contain. (B) An example of the facts describing kidney anatomy. This series of facts describes the relationships (e.g. contains, develops from) among anatomical objects (e.g. organism, tissue, cell). (C) An example of the facts describing the experiments included in RenalDB. (D) An example of the rules describing how expression, enrichment and specificity are defined. These high-level logical statements then used as queries on the anatomical and experimental databases to determine whether the gene/transcript is expressed, enriched or specific to various anatomical objects.
Availability – RenalDB is available at: http://renaldb.uni-frankfurt.de