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Plant-Pathogen Interactions

Overview of attention for book
Cover of 'Plant-Pathogen Interactions'

Table of Contents

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    Book Overview
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    Chapter 1 Galaxy as a platform for identifying candidate pathogen effectors.
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    Chapter 2 Bioinformatic analysis of expression data to identify effector candidates.
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    Chapter 3 Two-Dimensional Data Binning for the Analysis of Genome Architecture in Filamentous Plant Pathogens and Other Eukaryotes
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    Chapter 4 On the statistics of identifying candidate pathogen effectors.
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    Chapter 5 High-Throughput Imaging of Plant Immune Responses
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    Chapter 6 In Vivo Protein-Protein Interaction Studies with BiFC: Conditions, Cautions, and Caveats.
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    Chapter 7 Particle bombardment-mediated transient expression to identify localization signals in plant disease resistance proteins and target sites for the proteolytic activity of pathogen effectors.
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    Chapter 8 Purification of Fungal Haustoria from Infected Plant Tissue by Flow Cytometry
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    Chapter 9 Functional characterization of nematode effectors in plants.
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    Chapter 10 Silencing of Aphid Genes by Feeding on Stable Transgenic Arabidopsis thaliana.
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    Chapter 11 Leaf-Disc Assay Based on Transient Over-Expression in Nicotiana benthamiana to Allow Functional Screening of Candidate Effectors from Aphids.
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    Chapter 12 A Growth Quantification Assay for Hyaloperonospora arabidopsidis Isolates in Arabidopsis thaliana
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    Chapter 13 Simple Quantification of In Planta Fungal Biomass
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    Chapter 14 Virus-Induced Gene Silencing and Agrobacterium tumefaciens-Mediated Transient Expression in Nicotiana tabacum
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    Chapter 15 DIGE-ABPP by Click Chemistry: Pairwise Comparison of Serine Hydrolase Activities from the Apoplast of Infected Plants.
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    Chapter 16 A Simple and Fast Protocol for the Protein Complex Immunoprecipitation (Co-IP) of Effector: Host Protein Complexes
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    Chapter 17 An Arabidopsis and Tomato Mesophyll Protoplast System for Fast Identification of Early MAMP-Triggered Immunity-Suppressing Effectors
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    Chapter 18 Production of RXLR Effector Proteins for Structural Analysis by X-Ray Crystallography
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    Chapter 19 The Do's and Don'ts of Effectoromics.
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    Chapter 20 Protoplast Cell Death Assay to Study Magnaporthe oryzae AVR Gene Function in Rice
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    Chapter 21 A Bacterial Type III Secretion-Based Delivery System for Functional Assays of Fungal Effectors in Cereals
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    Chapter 22 Genomic DNA Library Preparation for Resistance Gene Enrichment and Sequencing (RenSeq) in Plants.
Attention for Chapter 3: Two-Dimensional Data Binning for the Analysis of Genome Architecture in Filamentous Plant Pathogens and Other Eukaryotes
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Chapter title
Two-Dimensional Data Binning for the Analysis of Genome Architecture in Filamentous Plant Pathogens and Other Eukaryotes
Chapter number 3
Book title
Plant-Pathogen Interactions
Published in
Methods in molecular biology, January 2014
DOI 10.1007/978-1-62703-986-4_3
Pubmed ID
Book ISBNs
978-1-62703-985-7, 978-1-62703-986-4
Authors

Diane G. O. Saunders, Joe Win, Sophien Kamoun, Sylvain Raffaele, Saunders, Diane G. O., Win, Joe, Kamoun, Sophien, Raffaele, Sylvain

Abstract

Genome architecture often reflects an organism's lifestyle and can therefore provide insights into gene function, regulation, and adaptation. In several lineages of plant pathogenic fungi and oomycetes, characteristic repeat-rich and gene-sparse regions harbor pathogenicity-related genes such as effectors. In these pathogens, analysis of genome architecture has assisted the mining for novel candidate effector genes and investigations into patterns of gene regulation and evolution at the whole genome level. Here we describe a two-dimensional data binning method in R with a heatmap-style graphical output to facilitate analysis and visualization of whole genome architecture. The method is flexible, combining whole genome architecture heatmaps with scatter plots of the genomic environment of selected gene sets. This enables analysis of specific values associated with genes such as gene expression and sequence polymorphisms, according to genome architecture. This method enables the investigation of whole genome architecture and reveals local properties of genomic neighborhoods in a clear and concise manner.

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X Demographics

The data shown below were collected from the profiles of 3 X users 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Australia 2 6%
Netherlands 1 3%
Brazil 1 3%
Unknown 27 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 19%
Student > Doctoral Student 5 16%
Student > Ph. D. Student 5 16%
Student > Bachelor 4 13%
Student > Master 2 6%
Other 3 10%
Unknown 6 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 58%
Biochemistry, Genetics and Molecular Biology 6 19%
Arts and Humanities 1 3%
Unknown 6 19%
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 10 April 2014.
All research outputs
#15,299,491
of 22,753,345 outputs
Outputs from Methods in molecular biology
#5,313
of 13,089 outputs
Outputs of similar age
#189,995
of 305,229 outputs
Outputs of similar age from Methods in molecular biology
#200
of 597 outputs
Altmetric has tracked 22,753,345 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% 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 45th percentile – i.e., 45% 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 305,229 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% 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 has gotten more attention than average, scoring higher than 56% of its contemporaries.