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Plant Gene Regulatory Networks

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Cover of 'Plant Gene Regulatory Networks'

Table of Contents

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    Book Overview
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    Chapter 1 From Genes to Networks: Characterizing Gene-Regulatory Interactions in Plants
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    Chapter 2 Inducible Promoter Systems for Gene Perturbation Experiments in Arabidopsis
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    Chapter 3 Cell Type-Specific Gene Expression Profiling Using Fluorescence-Activated Nuclear Sorting
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    Chapter 4 Characterization of Cell-Type-Specific DNA Binding Sites of Plant Transcription Factors Using Chromatin Immunoprecipitation
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    Chapter 5 Yeast One- and Two-Hybrid High-Throughput Screenings Using Arrayed Libraries
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    Chapter 6 SELEX-Seq: A Method to Determine DNA Binding Specificities of Plant Transcription Factors
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    Chapter 7 Analysis of a Plant Transcriptional Regulatory Network Using Transient Expression Systems
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    Chapter 8 Analysis of In Vivo Chromatin and Protein Interactions of Arabidopsis Transcript Elongation Factors
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    Chapter 9 Characterization of Mediator Complex and its Associated Proteins from Rice
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    Chapter 10 DNase I SIM: A Simplified In-Nucleus Method for DNase I Hypersensitive Site Sequencing
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    Chapter 11 In Situ Hi-C Library Preparation for Plants to Study Their Three-Dimensional Chromatin Interactions on a Genome-Wide Scale
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    Chapter 12 Multiplexed Transcriptional Activation or Repression in Plants Using CRISPR-dCas9-Based Systems
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    Chapter 13 Generation of dTALEs and Libraries of Synthetic TALE-Activated Promoters for Engineering of Gene Regulatory Networks in Plants
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    Chapter 14 Design of Knowledge Bases for Plant Gene Regulatory Networks
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    Chapter 15 AraNet: A Network Biology Server for Arabidopsis thaliana and Other Non-Model Plant Species
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    Chapter 16 Integration of Genome-Wide TF Binding and Gene Expression Data to Characterize Gene Regulatory Networks in Plant Development
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    Chapter 17 Predicting Transcription Factor Binding Sites and Their Cognate Transcription Factors Using Gene Expression Data
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    Chapter 18 Computational Approaches to Study Gene Regulatory Networks
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    Chapter 19 Boolean Dynamic Modeling Approaches to Study Plant Gene Regulatory Networks: Integration, Validation, and Prediction
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    Chapter 20 ODE-Based Modeling of Complex Regulatory Circuits
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    Chapter 21 Inferring Gene Regulatory Networks in the Arabidopsis Root Using a Dynamic Bayesian Network Approach
Attention for Chapter 4: Characterization of Cell-Type-Specific DNA Binding Sites of Plant Transcription Factors Using Chromatin Immunoprecipitation
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Chapter title
Characterization of Cell-Type-Specific DNA Binding Sites of Plant Transcription Factors Using Chromatin Immunoprecipitation
Chapter number 4
Book title
Plant Gene Regulatory Networks
Published in
Methods in molecular biology, June 2017
DOI 10.1007/978-1-4939-7125-1_4
Pubmed ID
Book ISBNs
978-1-4939-7124-4, 978-1-4939-7125-1
Authors

Lau, On Sun, On Sun Lau

Editors

Kerstin Kaufmann, Bernd Mueller-Roeber

Abstract

The generation of diverse cell types in multicellular organisms often requires the activity of cell-type-specific transcription factors. Understanding where these transcription factors bind in controlling specific cellular programs is critical. However, probing these cell-type-specific factors in vivo with standard chromatin immunoprecipitation (ChIP) assays remains a challenge. We have developed an optimized ChIP assay termed Maximized Objects for Better Enrichment (MOBE)-ChIP, which improves ChIP sensitivity and allows the detection of cell-type-specific signals at a genome-wide scale. Here, I describe the procedure for implementing this method for the study of plant transcription factors. Besides being useful for cell-type-specific studies, MOBE-ChIP can also be employed as a general strategy for enhancing ChIP signals.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 50%
Student > Postgraduate 1 50%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 100%