Logic programming to infer complex RNA expression patterns from RNA-seq data. (7th December 2016)

• Record Type: Journal Article
• Authors: Weirick, Tyler; Militello, Giuseppe; Ponomareva, Yuliya; John, David; Döring, Claudia; Dimmeler, Stefanie; Uchida, Shizuka
• Is Part Of: Briefings in bioinformatics. Volume 19:Number 2(2018:Mar.)
• Page Start: 199
• Page End: 209
• Publication Date: 2016-12-07
• Subjects: gene expression -- kidney -- lncRNA -- microarray -- RNA-seq; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbw117
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: 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. We 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, we introduce 'RenalDB' (http://renaldb.uni-frankfurt.de ), 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. We 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. We anticipate RenalDB will be used as a first step toward functional studies of lncRNAs in the kidney.