Chapter title |
Identification of Protein–DNA Interactions Using Enhanced Yeast One-Hybrid Assays and a Semiautomated Approach
|
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Chapter number | 13 |
Book title |
Plant Genomics
|
Published in |
Methods in molecular biology, April 2017
|
DOI | 10.1007/978-1-4939-7003-2_13 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7001-8, 978-1-4939-7003-2
|
Authors |
Gaudinier, Allison, Tang, Michelle, Bågman, Anne-Maarit, Brady, Siobhan M., Allison Gaudinier, Michelle Tang, Anne-Maarit Bågman, Siobhan M. Brady |
Editors |
Wolfgang Busch |
Abstract |
Yeast one-hybrid assays are an in vitro gene-centered approach to map transcription factor-DNA interactions. Here we describe this method and adaptations to screen for interactions between plant transcriptional regulators and their targets. Of particular note, the use of yeast one-hybrid assays fills in an important gap in available methodologies. When one is interested in a specific biological process of interest, the yeast one-hybrid assay is the only method that allows researchers to identify upstream regulators of the biological process of interest. This technique can be also used to further validate physical protein-DNA interactions or as a hypothesis-generating tool. In this method, promoters or DNA regions of interest are cloned and transformed into yeast and tested for interaction against a collection of transcription factors (TFs). Yeast one-hybrid screens are adaptable to the question the researcher is asking and the tools and components available. In this chapter we will describe large-scale and high-throughput Y1H screening; however, this can easily be scaled down for smaller studies. |
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France | 1 | 50% |
United States | 1 | 50% |
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Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 32 | 100% |
Demographic breakdown
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Student > Ph. D. Student | 11 | 34% |
Researcher | 8 | 25% |
Student > Bachelor | 2 | 6% |
Lecturer > Senior Lecturer | 2 | 6% |
Professor | 1 | 3% |
Other | 3 | 9% |
Unknown | 5 | 16% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 15 | 47% |
Biochemistry, Genetics and Molecular Biology | 9 | 28% |
Computer Science | 1 | 3% |
Unknown | 7 | 22% |