Utilizing RNA-Seq data for de novo coexpression network inference

Ovidiu Iancu, Sunita Kawane, Daniel Bottomly, Robert Searles, Robert Hitzemann, Shannon McWeeney

    Research output: Contribution to journalArticlepeer-review

    101 Scopus citations


    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 scalefree, 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.

    Original languageEnglish (US)
    Article numberbts245
    Pages (from-to)1592-1597
    Number of pages6
    Issue number12
    StatePublished - Jun 2012

    ASJC Scopus subject areas

    • Statistics and Probability
    • Biochemistry
    • Molecular Biology
    • Computer Science Applications
    • Computational Theory and Mathematics
    • Computational Mathematics


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