A novel synthetic-genetic-array–based yeast one-hybrid system for high discovery rate and short processing time

  1. Ying-Chung Jimmy Lin2,3,5
  1. 1Genomics Research Center, Academia Sinica, Taipei 11529, Taiwan;
  2. 2State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin 150040, China;
  3. 3Department of Life Sciences and Institute of Plant Biology, College of Life Science, National Taiwan University, Taipei 10617, Taiwan;
  4. 4Institute of Biomedical Informatics and Center for Systems and Synthetic Biology, National Yang-Ming University, Taipei 11221, Taiwan;
  5. 5Forest Biotechnology Group, Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, North Carolina 27695, USA
  1. 6 These authors contributed equally to this work.

  • Corresponding authors: ycjimmylin{at}ntu.edu.tw, chang108{at}gate.sinica.edu.tw, weili2015{at}nefu.edu.cn, vchiang{at}ncsu.edu
  • Abstract

    Eukaryotic gene expression is often tightly regulated by interactions between transcription factors (TFs) and their DNA cis targets. Yeast one-hybrid (Y1H) is one of the most extensively used methods to discover these interactions. We developed a high-throughput meiosis-directed yeast one-hybrid system using the Magic Markers of the synthetic genetic array analysis. The system has a transcription factor–DNA interaction discovery rate twice as high as the conventional diploid-mating approach and a processing time nearly one-tenth of the haploid-transformation method. The system also offers the highest accuracy in identifying TF–DNA interactions that can be authenticated in vivo by chromatin immunoprecipitation. With these unique features, this meiosis-directed Y1H system is particularly suited for constructing novel and comprehensive genome-scale gene regulatory networks for various organisms.

    Footnotes

    • [Supplemental material is available for this article.]

    • Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.245951.118.

    • Freely available online through the Genome Research Open Access option.

    • Received November 2, 2018.
    • Accepted June 6, 2019.

    This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

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