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Root Development

Overview of attention for book
Cover of 'Root Development'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Measuring Plant Root Traits Under Controlled and Field Conditions: Step-by-Step Procedures
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    Chapter 2 Phenotyping Crop Root Crowns: General Guidance and Specific Protocols for Maize, Wheat, and Soybean
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    Chapter 3 Developmental Analysis of Arabidopsis Root Meristem
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    Chapter 4 Genetic and Phenotypic Analysis of Lateral Root Development in Arabidopsis thaliana
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    Chapter 5 Adapting the Lateral Root-Inducible System to Medicago truncatula
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    Chapter 6 Characterization of Root Epidermal Cell Patterning and Differentiation in Arabidopsis
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    Chapter 7 In Vitro Assay for Induction of Adventitious Rooting on Intact Arabidopsis Hypocotyls
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    Chapter 8 Root Gravitropism: Quantification, Challenges, and Solutions
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    Chapter 9 Calcium Ion Dynamics in Roots: Imaging and Analysis
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    Chapter 10 Optimized Whole-Mount In Situ Immunolocalization for Arabidopsis thaliana Root Meristems and Lateral Root Primordia
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    Chapter 11 Light Sheet Fluorescence Microscopy Optimized for Long-Term Imaging of Arabidopsis Root Development
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    Chapter 12 Histological Profiling Over Time to Optimize Root Cell Type-Specific Reporter Lines for Cell Sorting
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    Chapter 13 Long-Term In Vivo Imaging of Luciferase-Based Reporter Gene Expression in Arabidopsis Roots
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    Chapter 14 Cortical Cell Length Analysis During Gravitropic Root Growth
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    Chapter 15 Growth Rate Normalization Method to Assess Gravitropic Root Growth
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    Chapter 16 Immunoprecipitation of Membrane Proteins from Arabidopsis thaliana Root Tissue
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    Chapter 17 Monitoring Transcriptomic Changes in Soil-Grown Roots and Shoots of Arabidopsis thaliana Subjected to a Progressive Drought Stress
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    Chapter 18 Chromatin Immunoprecipitation Sequencing (ChIP-Seq) for Transcription Factors and Chromatin Factors in Arabidopsis thaliana Roots: From Material Collection to Data Analysis
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    Chapter 19 μChIP-Seq for Genome-Wide Mapping of In Vivo TF-DNA Interactions in Arabidopsis Root Protoplasts
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    Chapter 20 Proteome Analysis of Arabidopsis Roots
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    Chapter 21 Step-by-Step Construction of Gene Co-expression Networks from High-Throughput Arabidopsis RNA Sequencing Data
  23. Altmetric Badge
    Chapter 22 GWA-Portal: Genome-Wide Association Studies Made Easy
Attention for Chapter 21: Step-by-Step Construction of Gene Co-expression Networks from High-Throughput Arabidopsis RNA Sequencing Data
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

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13 tweeters


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Chapter title
Step-by-Step Construction of Gene Co-expression Networks from High-Throughput Arabidopsis RNA Sequencing Data
Chapter number 21
Book title
Root Development
Published in
Methods in molecular biology, March 2018
DOI 10.1007/978-1-4939-7747-5_21
Pubmed ID
Book ISBNs
978-1-4939-7746-8, 978-1-4939-7747-5

Orlando Contreras-López, Tomás C. Moyano, Daniela C. Soto, Rodrigo A. Gutiérrez, Contreras-López, Orlando, Moyano, Tomás C., Soto, Daniela C., Gutiérrez, Rodrigo A.


The rapid increase in the availability of transcriptomics data generated by RNA sequencing represents both a challenge and an opportunity for biologists without bioinformatics training. The challenge is handling, integrating, and interpreting these data sets. The opportunity is to use this information to generate testable hypothesis to understand molecular mechanisms controlling gene expression and biological processes (Fig. 1). A successful strategy to generate tractable hypotheses from transcriptomics data has been to build undirected network graphs based on patterns of gene co-expression. Many examples of new hypothesis derived from network analyses can be found in the literature, spanning different organisms including plants and specific fields such as root developmental biology.In order to make the process of constructing a gene co-expression network more accessible to biologists, here we provide step-by-step instructions using published RNA-seq experimental data obtained from a public database. Similar strategies have been used in previous studies to advance root developmental biology. This guide includes basic instructions for the operation of widely used open source platforms such as Bio-Linux, R, and Cytoscape. Even though the data we used in this example was obtained from Arabidopsis thaliana, the workflow developed in this guide can be easily adapted to work with RNA-seq data from any organism.

Twitter Demographics

The data shown below were collected from the profiles of 13 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 131 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 131 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 19%
Student > Ph. D. Student 22 17%
Student > Bachelor 22 17%
Student > Master 15 11%
Other 8 6%
Other 15 11%
Unknown 24 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 47 36%
Biochemistry, Genetics and Molecular Biology 46 35%
Neuroscience 3 2%
Computer Science 2 2%
Immunology and Microbiology 1 <1%
Other 2 2%
Unknown 30 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 19 January 2019.
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Outputs of similar age from Methods in molecular biology
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Altmetric has tracked 21,742,867 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 12,489 research outputs from this source. They receive a mean Attention Score of 3.3. This one has done particularly well, scoring higher than 98% 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 298,169 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.