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Plant Gene Regulatory Networks

Overview of attention for book
Cover of 'Plant Gene Regulatory Networks'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    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
  8. Altmetric Badge
    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
  19. Altmetric Badge
    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
  21. Altmetric Badge
    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 16: Integration of Genome-Wide TF Binding and Gene Expression Data to Characterize Gene Regulatory Networks in Plant Development
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  • High Attention Score compared to outputs of the same age and source (90th percentile)

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Chapter title
Integration of Genome-Wide TF Binding and Gene Expression Data to Characterize Gene Regulatory Networks in Plant Development
Chapter number 16
Book title
Plant Gene Regulatory Networks
Published in
Methods in molecular biology, June 2017
DOI 10.1007/978-1-4939-7125-1_16
Pubmed ID
Book ISBNs
978-1-4939-7124-4, 978-1-4939-7125-1
Authors

Chen, Dijun, Kaufmann, Kerstin, Dijun Chen, Kerstin Kaufmann

Editors

Kerstin Kaufmann, Bernd Mueller-Roeber

Abstract

Key transcription factors (TFs) controlling the morphogenesis of flowers and leaves have been identified in the model plant Arabidopsis thaliana. Recent genome-wide approaches based on chromatin immunoprecipitation (ChIP) followed by high-throughput DNA sequencing (ChIP-seq) enable systematic identification of genome-wide TF binding sites (TFBSs) of these regulators. Here, we describe a computational pipeline for analyzing ChIP-seq data to identify TFBSs and to characterize gene regulatory networks (GRNs) with applications to the regulatory studies of flower development. In particular, we provide step-by-step instructions on how to download, analyze, visualize, and integrate genome-wide data in order to construct GRNs for beginners of bioinformatics. The practical guide presented here is ready to apply to other similar ChIP-seq datasets to characterize GRNs of interest.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 8%
Unknown 12 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 31%
Researcher 4 31%
Professor 2 15%
Student > Master 2 15%
Student > Bachelor 2 15%
Other 0 0%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 38%
Agricultural and Biological Sciences 5 38%
Psychology 1 8%
Social Sciences 1 8%
Unknown 1 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 January 2018.
All research outputs
#6,395,749
of 22,981,247 outputs
Outputs from Methods in molecular biology
#1,928
of 13,149 outputs
Outputs of similar age
#102,807
of 316,926 outputs
Outputs of similar age from Methods in molecular biology
#25
of 280 outputs
Altmetric has tracked 22,981,247 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 13,149 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 85% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 316,926 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 280 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.