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Plant Functional Genomics

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Cover of 'Plant Functional Genomics'

Table of Contents

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    Book Overview
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    Chapter 1 Epigenome Profiling of Specific Plant Cell Types Using a Streamlined INTACT Protocol and ChIP-seq.
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    Chapter 2 Whole-genome DNA methylation profiling with nucleotide resolution.
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    Chapter 3 High-Throughput Nuclease-Mediated Probing of RNA Secondary Structure in Plant Transcriptomes
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    Chapter 4 Genome-Wide Mapping of DNase I Hypersensitive Sites in Plants.
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    Chapter 5 Characterization of In Vivo DNA-Binding Events of Plant Transcription Factors by ChIP-seq: Experimental Protocol and Computational Analysis.
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    Chapter 6 Identification of Direct Targets of Plant Transcription Factors Using the GR Fusion Technique.
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    Chapter 7 Ribosome profiling: a tool for quantitative evaluation of dynamics in mRNA translation.
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    Chapter 8 Tissue-Specific Gene Expression Profiling by Cell Sorting
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    Chapter 9 Translating Ribosome Affinity Purification (TRAP) Followed by RNA Sequencing Technology (TRAP-SEQ) for Quantitative Assessment of Plant Translatomes.
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    Chapter 10 Rapid Immunopurification of Ribonucleoprotein Complexes of Plants
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    Chapter 11 Metabolomic profiling of plant tissues.
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    Chapter 12 Targeted Plant Genome Editing via the CRISPR/Cas9 Technology.
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    Chapter 13 QTL Mapping Using High-Throughput Sequencing
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    Chapter 14 Quantitating Plant MicroRNA-Mediated Target Repression Using a Dual-Luciferase Transient Expression System.
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    Chapter 15 Persistent virus-induced gene silencing in asymptomatic accessions of Arabidopsis.
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    Chapter 16 A User’s Guide to the Arabidopsis T-DNA Insertion Mutant Collections
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    Chapter 17 Genome-Wide Association Mapping in Plants Exemplified for Root Growth in Arabidopsis thaliana
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    Chapter 18 Tilling by sequencing.
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    Chapter 19 SHOREmap v3.0: Fast and Accurate Identification of Causal Mutations from Forward Genetic Screens
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    Chapter 20 Software-Assisted Stacking of Gene Modules Using GoldenBraid 2.0 DNA-Assembly Framework.
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    Chapter 21 Ligation-Independent Cloning for Plant Research
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    Chapter 22 Gene Functional Analysis Using Protoplast Transient Assays
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    Chapter 23 Descriptive vs. Mechanistic Network Models in Plant Development in the Post-Genomic Era.
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    Chapter 24 Analysis and visualization of RNA-Seq expression data using RStudio, Bioconductor, and Integrated Genome Browser
  26. Altmetric Badge
    Chapter 25 Constructing simple biological networks for understanding complex high-throughput data in plants.
Attention for Chapter 5: Characterization of In Vivo DNA-Binding Events of Plant Transcription Factors by ChIP-seq: Experimental Protocol and Computational Analysis.
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Chapter title
Characterization of In Vivo DNA-Binding Events of Plant Transcription Factors by ChIP-seq: Experimental Protocol and Computational Analysis.
Chapter number 5
Book title
Plant Functional Genomics
Published in
Methods in molecular biology, January 2015
DOI 10.1007/978-1-4939-2444-8_5
Pubmed ID
Book ISBNs
978-1-4939-2443-1, 978-1-4939-2444-8
Authors

Hilda van Mourik, Jose M Muiño, Alice Pajoro, Gerco C Angenent, Kerstin Kaufmann, Jose M. Muiño, Gerco C. Angenent, Mourik, Hilda van, Muiño, Jose M., Pajoro, Alice, Angenent, Gerco C., Kaufmann, Kerstin

Abstract

Chromatin immunoprecipitation followed by next-generation sequencing (ChIP-seq) is a powerful technique for genome-wide identification of in vivo binding sites of DNA-binding proteins. The technique had been used to study many DNA-binding proteins in a broad variety of species. The basis of the ChIP-seq technique is the ability to covalently cross-link DNA and proteins that are located in very close proximity. This allows the use of an antibody against the (tagged) protein of interest to specifically enrich DNA-fragments bound by this protein. ChIP-seq can be performed using antibodies against the native protein or against tagged proteins. Using a specific antibody against a tag to immunoprecipitate tagged proteins eliminates the need for a specific antibody against the native protein and allows more experimental flexibility. In this chapter we present a complete workflow for experimental procedure and bioinformatic analysis that allows wet-lab biologists to perform and analyze ChIP-seq experiments.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 37%
Researcher 7 23%
Student > Bachelor 3 10%
Student > Master 3 10%
Professor > Associate Professor 2 7%
Other 1 3%
Unknown 3 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 43%
Biochemistry, Genetics and Molecular Biology 8 27%
Computer Science 2 7%
Unknown 7 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 12 March 2015.
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#20,264,045
of 22,794,367 outputs
Outputs from Methods in molecular biology
#9,900
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Outputs of similar age
#295,740
of 353,053 outputs
Outputs of similar age from Methods in molecular biology
#635
of 996 outputs
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