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Plant Pathology

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Cover of 'Plant Pathology'

Table of Contents

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    Book Overview
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    Chapter 1 Plant Pathology
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    Chapter 2 Detection and Identification of Phoma Pathogens of Potato
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    Chapter 3 Plant Pathology
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    Chapter 4 A real-time multiplex PCR assay used in the identification of closely related fungal pathogens at the species level.
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    Chapter 5 Diagnostics of Tree Diseases Caused by Phytophthora austrocedri Species
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    Chapter 6 Real-Time LAMP for Chalara fraxinea Diagnosis
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    Chapter 7 Plant Pathology
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    Chapter 8 Loop-Mediated Isothermal Amplification (LAMP) for Detection of Phytoplasmas in the Field
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    Chapter 9 Diagnosis of Phytoplasmas by Real-Time PCR Using Locked Nucleic Acid (LNA) Probes
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    Chapter 10 Q-Bank Phytoplasma: A DNA Barcoding Tool for Phytoplasma Identification
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    Chapter 11 Plant Pathology
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    Chapter 12 Plant Pathology
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    Chapter 13 Plant Pathology
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    Chapter 14 Plant Pathology
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    Chapter 15 SNaPshot and CE-SSCP: Two Simple and Cost-Effective Methods to Reveal Genetic Variability Within a Virus Species
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    Chapter 16 Detection and Characterization of Viral Species/Subspecies Using Isothermal Recombinase Polymerase Amplification (RPA) Assays.
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    Chapter 17 Virus Testing by PCR and RT-PCR Amplification in Berry Fruit
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    Chapter 18 Metagenomics Approaches Based on Virion-Associated Nucleic Acids (VANA): An Innovative Tool for Assessing Without A Priori Viral Diversity of Plants.
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    Chapter 19 Plant Pathology
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    Chapter 20 Microarray Platform for the Detection of a Range of Plant Viruses and Viroids
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    Chapter 21 Plant Pathology
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    Chapter 22 Next-Generation Sequencing of Elite Berry Germplasm and Data Analysis Using a Bioinformatics Pipeline for Virus Detection and Discovery
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    Chapter 23 Metagenomic next-generation sequencing of viruses infecting grapevines.
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    Chapter 24 Droplet Digital PCR for Absolute Quantification of Pathogens
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    Chapter 25 Erratum to: Diagnostics of Tree Diseases Caused by Phytophthora austrocedri Species
Attention for Chapter 18: Metagenomics Approaches Based on Virion-Associated Nucleic Acids (VANA): An Innovative Tool for Assessing Without A Priori Viral Diversity of Plants.
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Chapter title
Metagenomics Approaches Based on Virion-Associated Nucleic Acids (VANA): An Innovative Tool for Assessing Without A Priori Viral Diversity of Plants.
Chapter number 18
Book title
Plant Pathology
Published in
Methods in molecular biology, January 2015
DOI 10.1007/978-1-4939-2620-6_18
Pubmed ID
Book ISBNs
978-1-4939-2619-0, 978-1-4939-2620-6
Authors

Filloux, Denis, Dallot, Sylvie, Delaunay, Agnès, Galzi, Serge, Jacquot, Emmanuel, Roumagnac, Philippe, Denis Filloux, Sylvie Dallot, Agnès Delaunay, Serge Galzi, Emmanuel Jacquot, Philippe Roumagnac

Abstract

This chapter describes an efficient approach that combines quality and yield extraction of viral nucleic acids from plants containing high levels of secondary metabolites and a sequence-independent amplification procedure for both the inventory of known plant viruses and the discovery of unknown ones. This approach turns out to be a useful tool for assessing the virome (the genome of all the viruses that inhabit a particular organism) of plants of interest. We here show that this approach enables the identification of a novel Potyvirus member within a single plant already known to be infected by two other Potyvirus species.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 6%
Unknown 32 94%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 21%
Researcher 6 18%
Student > Doctoral Student 4 12%
Student > Ph. D. Student 4 12%
Student > Bachelor 3 9%
Other 5 15%
Unknown 5 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 47%
Biochemistry, Genetics and Molecular Biology 7 21%
Unspecified 1 3%
Immunology and Microbiology 1 3%
Medicine and Dentistry 1 3%
Other 1 3%
Unknown 7 21%