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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 24: Droplet Digital PCR for Absolute Quantification of Pathogens
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Chapter title
Droplet Digital PCR for Absolute Quantification of Pathogens
Chapter number 24
Book title
Plant Pathology
Published in
Methods in molecular biology, January 2015
DOI 10.1007/978-1-4939-2620-6_24
Pubmed ID
Book ISBNs
978-1-4939-2619-0, 978-1-4939-2620-6
Authors

Ion Gutiérrez-Aguirre, Nejc Rački, Tanja Dreo, Maja Ravnikar

Abstract

The recent advent of different digital PCR (dPCR) platforms is enabling the expansion of this technology for research and diagnostic applications worldwide. The main principle of dPCR, as in other PCR-based methods including quantitative PCR (qPCR), is the specific amplification of a nucleic acid target. The distinctive feature of dPCR is the separation of the reaction mixture into thousands to millions of partitions which is followed by a real time or end point detection of the amplification. The distribution of target sequences into partitions is described by the Poisson distribution, thus allowing accurate and absolute quantification of the target from the ratio of positive against all partitions at the end of the reaction. This omits the need to use reference materials with known target concentrations and increases the accuracy of quantification at low target concentrations compared to qPCR. dPCR has also shown higher resilience to inhibitors in a number of different types of samples. In this chapter we describe the droplet digital PCR (ddPCR) workflow for the detection and quantification of pathogens using the droplet digital Bio-Rad platform QX100. We present as an example the quantification of the quarantine plant pathogenic bacterium, Erwinia amylovora.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Ireland 1 <1%
Unknown 109 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 19%
Researcher 18 16%
Student > Master 13 12%
Student > Doctoral Student 5 5%
Student > Bachelor 5 5%
Other 11 10%
Unknown 37 34%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 27%
Biochemistry, Genetics and Molecular Biology 20 18%
Engineering 4 4%
Immunology and Microbiology 4 4%
Veterinary Science and Veterinary Medicine 3 3%
Other 9 8%
Unknown 40 36%