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Logic programming to infer complex RNA expression patterns from RNA-seq data.

Tyler Weirick1,2,3, Giuseppe Militello1,2,3, Yuliya Ponomareva1,2

  • 1Institute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, Germany.

Briefings in Bioinformatics
|December 25, 2016
PubMed
Summary
This summary is machine-generated.

A new database, RenalDB, offers detailed expression profiles for long noncoding RNAs (lncRNAs) in kidney cells and tissues. This resource utilizes logic programming to map complex anatomical and developmental data, aiding functional studies.

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Area of Science:

  • Genomics and Bioinformatics
  • Molecular Biology
  • Renal Science

Background:

  • Numerous long noncoding RNA (lncRNA) databases exist, but often lack detailed expression profiles across specific cell types and developmental stages.
  • Understanding lncRNA expression is crucial due to their cell-type and time-dependent activity.
  • Existing databases struggle to represent complex anatomical hierarchies and metadata.

Purpose of the Study:

  • To develop a novel strategy for organizing and presenting lncRNA expression data, accounting for complex biological contexts.
  • To introduce RenalDB, a specialized database for lncRNA expression profiles in kidney tissues and cells.
  • To facilitate functional studies of lncRNAs within the kidney.

Main Methods:

  • Employed logic programming to model intricate relationships between organs, tissues, cell types, gender, and developmental time points.
  • Developed RenalDB (http://renaldb.uni-frankfurt.de) to store and query expression profiles based on this logic programming framework.
  • Validated database content through biological experiments and functional characterization of specific lncRNAs.

Main Results:

  • RenalDB successfully integrates complex anatomical and sample metadata using logic programming.
  • Functional characterization revealed LOC440173's role in cell growth/survival and PAXIP1-AS1's role as a regulator of cell death.
  • The database provides detailed expression profiles for lncRNAs in major organs, with a focus on kidney-specific data.

Conclusions:

  • The logic programming approach effectively handles complex biological data organization for lncRNA expression.
  • RenalDB serves as a valuable resource for exploring lncRNA expression in the kidney.
  • The characterized lncRNAs, LOC440173 and PAXIP1-AS1, highlight the functional importance of kidney lncRNAs.