title: Comparative study of RNA-seq- and microarray-derived coexpression networks in Arabidopsis thaliana.
abstract: BACKGROUND
  Coexpression networks are data-derived representations of genes behaving in a similar way across tissues and experimental conditions. They have been used for hypothesis generation and guilt-by-association approaches for inferring functions of previously unknown genes. So far, the main platform for expression data has been DNA microarrays; however, the recent development of RNA-seq allows for higher accuracy and coverage of transcript populations. It is therefore important to assess the potential for biological investigation of coexpression networks derived from this novel technique in a condition-independent dataset.
  
  
  RESULTS
  We collected 65 publicly available Illumina RNA-seq high quality Arabidopsis thaliana samples and generated Pearson correlation coexpression networks. These networks were then compared with those derived from analogous microarray data. We show how Variance-Stabilizing Transformed (VST) RNA-seq data samples are the most similar to microarray ones, with respect to inter-sample variation, correlation coefficient distribution and network topological architecture. Microarray networks show a slightly higher score in biology-derived quality assessments such as overlap with the known protein-protein interaction network and edge ontological agreement. Different coexpression network centralities are investigated; in particular, we show how betweenness centrality is generally a positive marker for essential genes in A.thaliana, regardless of the platform originating the data. In the end, we focus on a specific gene network case, showing that although microarray data seem more suited for gene network reverse engineering, RNA-seq offers the great advantage of extending coexpression analyses to the entire transcriptome.
authors: Institute of Applied Genomics, Udine 33100, Italy. fgiorgi@appliedgenomics.org
authors: Federico M
authors: FM
authors: Giorgi
authors: Cristian
authors: C
authors: Del Fabbro
authors: Francesco
authors: F
authors: Licausi
mesh_terms: D017360:Arabidopsis / Q000235:genetics* / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods*; D018506:Gene Expression Regulation, Plant; D053263:Gene Regulatory Networks; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D017423:Sequence Analysis, RNA