Chapter title |
Identifying Gene Regulatory Networks in Arabidopsis by In Silico Prediction, Yeast-1-Hybrid, and Inducible Gene Profiling Assays.
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Chapter number | 3 |
Book title |
Plant Cell Division
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Published in |
Methods in molecular biology, January 2016
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DOI | 10.1007/978-1-4939-3142-2_3 |
Pubmed ID | |
Book ISBNs |
978-1-4939-3141-5, 978-1-4939-3142-2
|
Authors |
Sparks, Erin E, Benfey, Philip N, Sparks, Erin E., Benfey, Philip N., Erin E. Sparks, Philip N. Benfey |
Abstract |
A system-wide understanding of gene regulation will provide deep insights into plant development and physiology. In this chapter we describe a threefold approach to identify the gene regulatory networks in Arabidopsis thaliana that function in a specific tissue or biological process. Since no single method is sufficient to establish comprehensive and high-confidence gene regulatory networks, we focus on the integration of three approaches. First, we describe an in silico prediction method of transcription factor-DNA binding, then an in vivo assay of transcription factor-DNA binding by yeast-1-hybrid and lastly the identification of co-expression clusters by transcription factor induction in planta. Each of these methods provides a unique tool to advance our understanding of gene regulation, and together provide a robust model for the generation of gene regulatory networks. |
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Demographic breakdown
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
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Chile | 2 | 17% |
Unknown | 10 | 83% |
Demographic breakdown
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Student > Bachelor | 4 | 33% |
Student > Master | 3 | 25% |
Student > Doctoral Student | 1 | 8% |
Student > Ph. D. Student | 1 | 8% |
Researcher | 1 | 8% |
Other | 0 | 0% |
Unknown | 2 | 17% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 3 | 25% |
Nursing and Health Professions | 1 | 8% |
Mathematics | 1 | 8% |
Unknown | 2 | 17% |