title: Network-based bioinformatics analysis of spatio-temporal RNA-Seq data reveals transcriptional programs underpinning normal and aberrant retinal development.
abstract: The retina as a model system with extensive information on genes involved in development/maintenance is of great value for investigations employing deep sequencing to capture transcriptome change over time. This in turn could enable us to find patterns in gene expression across time to reveal transition in biological processes.
  
  
  
  We developed a bioinformatics pipeline to categorize genes based on their differential expression and their alternative splicing status across time by binning genes based on their transcriptional kinetics. Genes within same bins were then leveraged to query gene annotation databases to discover molecular programs employed by the developing retina.
  
  
  
  Using our pipeline on RNA-Seq data obtained from fractionated (nucleus/cytoplasm) developing retina at embryonic day (E) 16 and postnatal day (P) 0, we captured high-resolution as in the difference between the cytoplasm and the nucleus at the same developmental time. We found de novo transcription of genes whose transcripts were exclusively found in the nuclear transcriptome at P0. Further analysis showed that these genes enriched for functions that are known to be executed during postnatal development, thus showing that the P0 nuclear transcriptome is temporally ahead of that of its cytoplasm. We extended our strategy to perform temporal analysis comparing P0 data to either P21-Nrl-wildtype (WT) or P21-Nrl-knockout (KO) retinae, which predicted that the KO retina would have compromised vasculature. Indeed, histological manifestation of vasodilation has been reported at a later time point (P60).
  
  
  
  Thus, our approach was predictive of a phenotype before it presented histologically. Our strategy can be extended to investigating the development and/or disease progression of other tissue types.
authors: Department of Physiology and Neurobiology, University of Connecticut, Storrs, CT, 06269, USA.
authors: Devi Krishna Priya
authors: DK
authors: Karunakaran
authors: Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, 06269, USA.
authors: Sahar
authors: S
authors: Al Seesi
authors: Department of Physiology and Neurobiology, University of Connecticut, Storrs, CT, 06269, USA.
authors: Abdul Rouf
authors: AR
authors: Banday
authors: Department of Physiology and Neurobiology, University of Connecticut, Storrs, CT, 06269, USA.
authors: Marybeth
authors: M
authors: Baumgartner
authors: Department of Physiology and Neurobiology, University of Connecticut, Storrs, CT, 06269, USA.|Utrecht University, 3508 TC, Utrecht, The Netherlands.
authors: Anouk
authors: A
authors: Olthof
authors: Department of Physiology and Neurobiology, University of Connecticut, Storrs, CT, 06269, USA.
authors: Christopher
authors: C
authors: Lemoine
authors: Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, 06269, USA.
authors: Ion I
authors: II
authors: Măndoiu
authors: Department of Physiology and Neurobiology, University of Connecticut, Storrs, CT, 06269, USA. rahul.kanadia@uconn.edu.
authors: Rahul N
authors: RN
authors: Kanadia
mesh_terms: D017398:Alternative Splicing; D000818:Animals; D019295:Computational Biology; D018450:Disease Progression; D020869:Gene Expression Profiling; D007700:Kinetics; D051379:Mice; D018345:Mice, Knockout; D012160:Retina / Q000002:abnormalities / Q000196:embryology / Q000378:metabolism*; D017423:Sequence Analysis, RNA; D062211:Spatio-Temporal Analysis; D059467:Transcriptome