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Plant Chemical Genomics

Overview of attention for book
Cover of 'Plant Chemical Genomics'

Table of Contents

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    Book Overview
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    Chapter 1 Fully Automated Compound Screening in Arabidopsis thaliana Seedlings
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    Chapter 2 Time-profiling fluorescent reporters in the Arabidopsis root.
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    Chapter 3 Screening for Bioactive Small Molecules by In Vivo Monitoring of Luciferase-Based Reporter Gene Expression in Arabidopsis thaliana
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    Chapter 4 Application of Yeast-Two Hybrid Assay to Chemical Genomic Screens: A High-Throughput System to Identify Novel Molecules Modulating Plant Hormone Receptor Complexes
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    Chapter 5 High-Throughput Screening of Small-Molecule Libraries for Inducers of Plant Defense Responses
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    Chapter 6 Using a Reverse Genetics Approach to Investigate Small-Molecule Activity
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    Chapter 7 Investigating the Phytohormone Ethylene Response Pathway by Chemical Genetics
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    Chapter 8 Screening for Inhibitors of Chloroplast Galactolipid Synthesis Acting in Membrano and in Planta
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    Chapter 9 Forward Chemical Screening of Small RNA Pathways
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    Chapter 10 Identification and use of fluorescent dyes for plant cell wall imaging using high-throughput screening.
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    Chapter 11 High-Throughput Identification of Chemical Endomembrane Cycling Disruptors Utilizing Tobacco Pollen
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    Chapter 12 Plant chemical genomics: gravity sensing and response.
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    Chapter 13 Screening Chemical Libraries for Compounds That Affect Protein Sorting to the Yeast Vacuole
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    Chapter 14 The Use of Multidrug Approach to Uncover New Players of the Endomembrane System Trafficking Machinery
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    Chapter 15 Cheminformatic Analysis of High-Throughput Compound Screens
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    Chapter 16 Endomembrane Dissection Using Chemically Induced Bioactive Clusters
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    Chapter 17 Statistical Molecular Design: A Tool to Follow Up Hits from Small-Molecule Screening
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    Chapter 18 Early Stage Hit Triage for Plant Chemical Genetic Screens and Target Site Identification
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    Chapter 19 Screening for Gene Function Using the FOX ( F ull-Length cDNA O vere X pressor Gene) Hunting System
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    Chapter 20 Quantification of Stable Isotope Label in Metabolites via Mass Spectrometry
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    Chapter 21 1H NMR-Based Metabolomics Methods for Chemical Genomics Experiments
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    Chapter 22 Determination of Auxin Transport Parameters on the Cellular Level
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    Chapter 23 Analyzing the In Vivo Status of Exogenously Applied Auxins: A HPLC-Based Method to Characterize the Intracellularly Localized Auxin Transporters
Attention for Chapter 21: 1H NMR-Based Metabolomics Methods for Chemical Genomics Experiments
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Chapter title
1H NMR-Based Metabolomics Methods for Chemical Genomics Experiments
Chapter number 21
Book title
Plant Chemical Genomics
Published in
Methods in molecular biology, January 2014
DOI 10.1007/978-1-62703-592-7_21
Pubmed ID
Book ISBNs
978-1-62703-591-0, 978-1-62703-592-7
Authors

Daniel J. Orr, Gregory A. Barding Jr., Christiana E. Tolley, Glenn R. Hicks, Natasha V. Raikhel, Cynthia K. Larive, Gregory A. Barding, Orr, Daniel J., Barding, Gregory A., Tolley, Christiana E., Hicks, Glenn R., Raikhel, Natasha V., Larive, Cynthia K.

Abstract

Metabolomics and chemical genomics studies can each provide unique insights into plant biology. Although a variety of analytical techniques can be used for the interrogation of plant systems, nuclear magnetic resonance (NMR) provides unbiased characterization of abundant metabolites. An example methodology is provided for probing the metabolism of Arabidopsis thaliana in a chemical genomics experiment including methods for tissue treatment, tissue collection, metabolite extraction, and methods to minimize variance in biological and technical sample replicates. Additionally, considerations and methods for data analysis, including multivariate statistics, univariate statistics, and data interpretation are included. The process is illustrated by examining the metabolic effects of chemical treatment of Arabidopsis with Sortin 1, also known as vacuolar protein sorting inhibitor 1. Sortin 1 was applied to Arabidopsis seedlings to examine metabolic effects in a chemical genomics experiment and to demonstrate the utility of metabolomics in conjunction with other "omics" techniques.

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X Demographics

The data shown below were collected from the profile of 1 X user 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 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 29%
Student > Bachelor 1 14%
Student > Doctoral Student 1 14%
Student > Ph. D. Student 1 14%
Professor > Associate Professor 1 14%
Other 0 0%
Unknown 1 14%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 2 29%
Agricultural and Biological Sciences 2 29%
Chemistry 1 14%
Medicine and Dentistry 1 14%
Unknown 1 14%
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 28 July 2014.
All research outputs
#18,371,959
of 22,755,127 outputs
Outputs from Methods in molecular biology
#7,865
of 13,089 outputs
Outputs of similar age
#229,360
of 305,249 outputs
Outputs of similar age from Methods in molecular biology
#294
of 597 outputs
Altmetric has tracked 22,755,127 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,089 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
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 305,249 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 597 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.