Comparative study of RNA-seq- and Microarray-derived coexpression networks in Arabidopsis thaliana
Autor: | Cristian Del Fabbro, Federico M. Giorgi, Francesco Licausi |
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Přispěvatelé: | Giorgi, Federico M., Del Fabbro, Cristian, Licausi, Francesco |
Jazyk: | angličtina |
Rok vydání: | 2013 |
Předmět: |
Statistics and Probability
Microarray Arabidopsis Gene regulatory network RNA-Seq Computational biology Biology Biochemistry Transcriptome Betweenness centrality Gene Expression Regulation Plant Interaction network ngs Computational Theory and Mathematic rna-seq microarray Gene Regulatory Networks Molecular Biology Oligonucleotide Array Sequence Analysis Genetics Sequence Analysis RNA Microarray analysis techniques Gene Expression Profiling Medicine (all) Computer Science Applications1707 Computer Vision and Pattern Recognition Computer Science Applications Computational Mathematics Computational Theory and Mathematics Computational Mathematic DNA microarray |
Popis: | Motivation: 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. Contact: fgiorgi@appliedgenomics.org Supplementary information: Supplementary data are available at Bioinformatics online. |
Databáze: | OpenAIRE |
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