Utilizing RNA-Seq data for de novo coexpression network inference
Open accessAbstract
Motivation: RNA-Seq experiments have shown great potential for transcriptome profiling. While sequencing increases the level of biological detail, integrative data analysis is also important. One avenue is the construction of coexpression networks. Because the capacity of RNA-Seq data for network construction has not been previously evaluated, we constructed a coexpression network using striatal samples, derived its network properties and compared it with microarray-based networks. Results: The RNA-Seq coexpression network displayed scale-free, hierarchical network structure. We detected transcripts groups (modules) with correlated profiles; modules overlap distinct ontology categories. Neuroanatomical data from the Allen Brain Atlas reveal several modules with spatial colocalization. The network was compared with microarray-derived networks; correlations from RNA-Seq data were higher, likely because greater sensitivity and dynamic range. Higher correlations result in higher network connectivity, heterogeneity and centrality. For transcripts present across platforms, network structure appeared largely preserved. From this study, we present the first RNA-Seq data de novo network inference. Contact: iancuo@ohsu.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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The presumed novelty of RNA-seq co-expression networks has not been fully assessed.
“Very few analyses to date have been performed on co-expression networks from RNA-seq data (although see, e.g.Iancu et al., 2012;Sekhon et al., 2013), with fewer conclusions drawn about their utility, nor has their presumed novelty been fully assessed.”
From Guidance for RNA-seq co-expression network construction and analysis: safety in numbers (2015) · cites this paper as Iancu et al., 2012
Few conclusions have been drawn about the utility of RNA-seq co-expression networks.
“Very few analyses to date have been performed on co-expression networks from RNA-seq data (although see, e.g.Iancu et al., 2012;Sekhon et al., 2013), with fewer conclusions drawn about their utility, nor has their presumed novelty been fully assessed.”
From Guidance for RNA-seq co-expression network construction and analysis: safety in numbers (2015) · cites this paper as Iancu et al., 2012
Very few analyses of co-expression networks from RNA-seq data have been performed.
“Very few analyses to date have been performed on co-expression networks from RNA-seq data (although see, e.g.Iancu et al., 2012;Sekhon et al., 2013), with fewer conclusions drawn about their utility, nor has their presumed novelty been fully assessed.”
From Guidance for RNA-seq co-expression network construction and analysis: safety in numbers (2015) · cites this paper as Iancu et al., 2012
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Guidance for RNA-seq co-expression network construction and analysis: safety in numbers
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Self-citation
- Authors
- Iancu, O. D., Kawane, S., Bottomly, D., Searles, R., Hitzemann, R., & McWeeney, S.
- Affiliations
- Oregon Health & Science University (US)
- Corresponding author
- Iancu, Ovidiu D.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-05-03
- Volume
- 28
- Issue
- 12
- Pages
- 1592-1597
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC3493127
- OpenAlex
- W2101986518
- Cited by
- 114
- References
- 6
- Citations by year
- 2026: 2 · 2024: 2 · 2023: 4 · 2022: 5 · 2021: 10 · 2020: 6 · 2019: 8 · 2018: 11 · 2017: 16 · 2016: 9 · 2015: 11 · 2014: 14 · 2013: 15 · 2012: 1
- FWCI
- 5.28
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Veterans Affairs
- M.J. Murdock Charitable Trust
- National Institutes of Health (UL1TR000128)
- National Institute on Alcohol Abuse and Alcoholism (AA010760, R29AA011114, U01AA013484, P50AA010760, AA011114, R01AA011114, AA013484, P60AA010760)
- National Institute of Mental Health (MH051372, R01MH051372)
- National Institute on Drug Abuse (R01DA005228, DA005228)
- National Cancer Institute (P30CA069533)
- National Center for Research Resources (UL1RR024140)
- National Center for Advancing Translational Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- RNA-seq, co-expression network, gene expression profiling, network inference, striatum, scale-free networks, gene modules, Allen Brain Atlas, microarray comparison, network topology, gene expression correlation
- MeSH terms
- Animals, Male, Mice, Inbred C57BL, Mice, Inbred DBA, RNA, Sequence Analysis, RNA, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Mice, Gene Regulatory Networks
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