↓ Skip to main content

Wheat Rust Diseases

Overview of attention for book
Cover of 'Wheat Rust Diseases'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Wheat Rust Surveillance: Field Disease Scoring and Sample Collection for Phenotyping and Molecular Genotyping
  3. Altmetric Badge
    Chapter 2 Field Pathogenomics: An Advanced Tool for Wheat Rust Surveillance
  4. Altmetric Badge
    Chapter 3 Race Typing of Puccinia striiformis on Wheat
  5. Altmetric Badge
    Chapter 4 Assessment of Aggressiveness of Puccinia striiformis on Wheat
  6. Altmetric Badge
    Chapter 5 Extraction of High Molecular Weight DNA from Fungal Rust Spores for Long Read Sequencing
  7. Altmetric Badge
    Chapter 6 Microsatellite Genotyping of the Wheat Yellow Rust Pathogen Puccinia striiformis
  8. Altmetric Badge
    Chapter 7 Computational Methods for Predicting Effectors in Rust Pathogens
  9. Altmetric Badge
    Chapter 8 Protein–Protein Interaction Assays with Effector–GFP Fusions in Nicotiana benthamiana
  10. Altmetric Badge
    Chapter 9 Proteome Profiling by 2D–Liquid Chromatography Method for Wheat–Rust Interaction
  11. Altmetric Badge
    Chapter 10 Investigating Gene Function in Cereal Rust Fungi by Plant-Mediated Virus-Induced Gene Silencing
  12. Altmetric Badge
    Chapter 11 Apoplastic Sugar Extraction and Quantification from Wheat Leaves Infected with Biotrophic Fungi
  13. Altmetric Badge
    Chapter 12 Genetic Analysis of Resistance to Wheat Rusts
  14. Altmetric Badge
    Chapter 13 Advances in Identification and Mapping of Rust Resistance Genes in Wheat
  15. Altmetric Badge
    Chapter 14 Chromosome Engineering Techniques for Targeted Introgression of Rust Resistance from Wild Wheat Relatives
  16. Altmetric Badge
    Chapter 15 Applications of Genomic Selection in Breeding Wheat for Rust Resistance
  17. Altmetric Badge
    Chapter 16 Rapid Phenotyping Adult Plant Resistance to Stem Rust in Wheat Grown under Controlled Conditions
  18. Altmetric Badge
    Chapter 17 Generation of Loss-of-Function Mutants for Wheat Rust Disease Resistance Gene Cloning
  19. Altmetric Badge
    Chapter 18 Isolation of Wheat Genomic DNA for Gene Mapping and Cloning
  20. Altmetric Badge
    Chapter 19 MutRenSeq: A Method for Rapid Cloning of Plant Disease Resistance Genes
  21. Altmetric Badge
    Chapter 20 Rapid Gene Isolation Using MutChromSeq
  22. Altmetric Badge
    Chapter 21 Rapid Identification of Rust Resistance Genes Through Cultivar-Specific De Novo Chromosome Assemblies
  23. Altmetric Badge
    Chapter 22 BSMV-Induced Gene Silencing Assay for Functional Analysis of Wheat Rust Resistance
  24. Altmetric Badge
    Chapter 23 Yeast as a Heterologous System to Functionally Characterize a Multiple Rust Resistance Gene that Encodes a Hexose Transporter
  25. Altmetric Badge
    Chapter 24 Biocontrol Agents for Controlling Wheat Rust
Attention for Chapter 2: Field Pathogenomics: An Advanced Tool for Wheat Rust Surveillance
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

twitter
10 X users

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
9 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Chapter title
Field Pathogenomics: An Advanced Tool for Wheat Rust Surveillance
Chapter number 2
Book title
Wheat Rust Diseases
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-7249-4_2
Pubmed ID
Book ISBNs
978-1-4939-7248-7, 978-1-4939-7249-4
Authors

Vanessa Bueno-Sancho, Daniel C. E. Bunting, Luis J. Yanes, Kentaro Yoshida, Diane G. O. Saunders, Bueno-Sancho, Vanessa, Bunting, Daniel C. E., Yanes, Luis J., Yoshida, Kentaro, Saunders, Diane G. O.

Abstract

Traditionally, diagnostic tools for plant pathogens were limited to the analysis of purified pathogen isolates subjected to phenotypic characterization and/or PCR-based genotypic analysis. However, these approaches detect only already known pathogenic agents, may not always recognize novel races, and can introduce bias in the results. Recent advances in next-generation sequencing technologies have provided new opportunities to integrate high-resolution genotype data into pathogen surveillance programs. Here, we describe some of the key bioinformatics analysis used in the newly developed "Field Pathogenomics" pathogen surveillance technique. This technique is based on RNA-seq data generated directly form pathogen-infected plant leaf samples collected in the field, providing a unique opportunity to characterize the pathogen population and its host directly in their natural environment. We describe two main analyses: (1) a phylogenetic analysis of the pathogen isolates that have been collected to understand how they are related to each other, and (2) a population structure analysis to provide insight into the genetic substructure within the pathogen population. This provides a high-resolution representation of pathogen population dynamics directly in the field, providing new insights into pathogen biology, population structure, and pathogenesis.

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 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 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 44%
Other 1 11%
Student > Doctoral Student 1 11%
Student > Master 1 11%
Unknown 2 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 56%
Biochemistry, Genetics and Molecular Biology 2 22%
Unknown 2 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 07 August 2020.
All research outputs
#6,436,171
of 22,999,744 outputs
Outputs from Methods in molecular biology
#1,940
of 13,154 outputs
Outputs of similar age
#120,871
of 421,208 outputs
Outputs of similar age from Methods in molecular biology
#220
of 1,074 outputs
Altmetric has tracked 22,999,744 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 13,154 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 85% 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 421,208 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 1,074 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.