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Bioinformatic landscapes for plant transcription factor system research


Main conclusion

Diverse bioinformatic resources have been developed for plant transcription factor (TF) research. This review presents the bioinformatic resources and methodologies for the elucidation of plant TF-mediated biological events. Such information is helpful to dissect the transcriptional regulatory systems in the three reference plants Arabidopsis , rice, and maize and translation to other plants.

Transcription factors (TFs) orchestrate diverse biological programs by the modulation of spatiotemporal patterns of gene expression via binding cis-regulatory elements. Advanced sequencing platforms accompanied by emerging bioinformatic tools revolutionize the scope and extent of TF research. The system-level integration of bioinformatic resources is beneficial to the decoding of TF-involved networks. Herein, we first briefly introduce general and specialized databases for TF research in three reference plants Arabidopsis, rice, and maize. Then, as proof of concept, we identified and characterized heat shock transcription factor (HSF) members through the TF databases. Finally, we present how the integration of bioinformatic resources at -omics layers can aid the dissection of TF-mediated pathways. We also suggest ways forward to improve the bioinformatic resources of plant TFs. Leveraging these bioinformatic resources and methodologies opens new avenues for the elucidation of transcriptional regulatory systems in the three model systems and translation to other plants.

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We apologize for not being able to cite many relevant original papers owing to space limitations. This work was supported by the National Natural Science Foundation of China (31201213 and 31571671), the Priority Academic Program Development of Jiangsu Higher Education Institutions, and the Open Project Program of Shanghai Key Laboratory of Bio-Energy Crops, Shanghai University (201302).

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Correspondence to Yijun Wang.

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Wang, Y., Lu, W. & Deng, D. Bioinformatic landscapes for plant transcription factor system research. Planta 243, 297–304 (2016). https://doi.org/10.1007/s00425-015-2453-7

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  • Plant transcription factors
  • Bioinformatics
  • -omics
  • Systems biology