Cited by 103Bioinformatics and Genomic NetworksGenomics and Phylogenetic StudiesGene expression and cancer classification

Comparative study of RNA-seq- and Microarray-derived coexpression networks in Arabidopsis thaliana

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Giorgi, Federico M., Del Fabbro, Cristian, Licausi, Francesco·2013·Bioinformatics

Abstract

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.

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Crossref · 10.1093/bioinformatics/btt053

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Authors
Giorgi, F. M., Del Fabbro, C., & Licausi, F.
Affiliations
  • University of Udine (IT)
  • 1 Institute of Applied Genomics, Udine 33100, 2Scuola Superiore Sant’Anna, Pisa 56124 and 3Department of Agriculture and Environmental Sciences, University of Udine, Udine 33100, Italy
Venue
Bioinformatics
Published
2013-02-01
Volume
29
Issue
6
Pages
717-724
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
OpenAlex
W2111591809
Cited by
103
References
80
Citations by year
2026: 1 · 2024: 1 · 2023: 3 · 2022: 7 · 2021: 10 · 2020: 8 · 2019: 7 · 2018: 11 · 2017: 16 · 2016: 14 · 2015: 7 · 2014: 11 · 2013: 7
FWCI
5.82
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Bioinformatics (publishedVersion)
  • PubMed (publishedVersion)
  • Institutional Research Information System (University of Udine) (submittedVersion, cc-by-nc-nd)
  • CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
  • Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
  • CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies) (submittedVersion)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords
co-expression network, Arabidopsis thaliana, RNA-seq, DNA microarrays, platform comparison, network topology, betweenness centrality, essential genes, protein-protein interaction network, transcriptome, gene network inference
MeSH terms
Arabidopsis, Sequence Analysis, RNA, Gene Expression Regulation, Plant, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Gene Regulatory Networks

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