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Research ArticleLARGE-SCALE BIOLOGY ARTICLE
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Inference of Transcriptional Networks in Arabidopsis through Conserved Noncoding Sequence Analysis

Jan Van de Velde, Ken S. Heyndrickx, Klaas Vandepoele
Jan Van de Velde
Department of Plant Systems Biology, VIB, B-9052 Ghent, BelgiumDepartment of Plant Biotechnology and Bioinformatics, Ghent University, B-9052 Ghent, Belgium
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  • ORCID record for Jan Van de Velde
Ken S. Heyndrickx
Department of Plant Systems Biology, VIB, B-9052 Ghent, BelgiumDepartment of Plant Biotechnology and Bioinformatics, Ghent University, B-9052 Ghent, Belgium
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Klaas Vandepoele
Department of Plant Systems Biology, VIB, B-9052 Ghent, BelgiumDepartment of Plant Biotechnology and Bioinformatics, Ghent University, B-9052 Ghent, Belgium
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Published July 2014. DOI: https://doi.org/10.1105/tpc.114.127001

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  • © 2014 American Society of Plant Biologists. All rights reserved.

Abstract

Transcriptional regulation plays an important role in establishing gene expression profiles during development or in response to (a)biotic stimuli. Transcription factor binding sites (TFBSs) are the functional elements that determine transcriptional activity, and the identification of individual TFBS in genome sequences is a major goal to inferring regulatory networks. We have developed a phylogenetic footprinting approach for the identification of conserved noncoding sequences (CNSs) across 12 dicot plants. Whereas both alignment and non-alignment-based techniques were applied to identify functional motifs in a multispecies context, our method accounts for incomplete motif conservation as well as high sequence divergence between related species. We identified 69,361 footprints associated with 17,895 genes. Through the integration of known TFBS obtained from the literature and experimental studies, we used the CNSs to compile a gene regulatory network in Arabidopsis thaliana containing 40,758 interactions, of which two-thirds act through binding events located in DNase I hypersensitive sites. This network shows significant enrichment toward in vivo targets of known regulators, and its overall quality was confirmed using five different biological validation metrics. Finally, through the integration of detailed expression and function information, we demonstrate how static CNSs can be converted into condition-dependent regulatory networks, offering opportunities for regulatory gene annotation.

  • Glossary

    TF
    transcription factor
    miRNA
    microRNA
    ChIP
    chromatin immunoprecipitation
    DH
    DNase I hypersensitive
    CNS
    conserved noncoding sequence
    TFBS
    transcription factor binding site
    CMM
    Comparative Motif Mapping
    FDR
    false discovery rate
    UTR
    untranslated region
    BHIF
    best-hit and in-paralogous families
    TPR
    true positive rate
    GRN
    gene regulatory network
    GO
    Gene Ontology
    SCW
    secondary cell wall
    • Received April 23, 2014.
    • Revised June 11, 2014.
    • Accepted June 16, 2014.
    • Published July 2, 2014.
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    Inference of Transcriptional Networks in Arabidopsis through Conserved Noncoding Sequence Analysis
    Jan Van de Velde, Ken S. Heyndrickx, Klaas Vandepoele
    The Plant Cell Jul 2014, 26 (7) 2729-2745; DOI: 10.1105/tpc.114.127001

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    Inference of Transcriptional Networks in Arabidopsis through Conserved Noncoding Sequence Analysis
    Jan Van de Velde, Ken S. Heyndrickx, Klaas Vandepoele
    The Plant Cell Jul 2014, 26 (7) 2729-2745; DOI: 10.1105/tpc.114.127001
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    More in this TOC Section

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