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Plant-Pathogen Interactions

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Cover of 'Plant-Pathogen Interactions'

Table of Contents

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    Book Overview
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    Chapter 1 Specific Detection and Quantification of Major Fusarium spp. Associated with Cereal and Pulse Crops
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    Chapter 2 Distinguishing Puccinia striiformis f. sp. tritici Isolates Using Genomic Sequencing: A Case Study
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    Chapter 3 DNA-Barcoding Identification of Plant Pathogens for Disease Diagnostics
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    Chapter 4 Real-Time Portable LAMP Assay for a Rapid Detection of Xylella fastidiosa In-Field
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    Chapter 5 Selective Quantification of Chemotropic Responses of Fusarium graminearum
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    Chapter 6 Live-Cell Visualization of Early Stages of Root Colonization by the Vascular Wilt Pathogen Fusarium oxysporum
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    Chapter 7 Transfection of Barley Leaf Protoplasts with a Fluorescently Tagged Fungal Effector for In Planta Localization Studies in Barley
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    Chapter 8 A Bioinformatic Guide to Identify Protein Effectors from Phytopathogens
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    Chapter 9 Unraveling Plant-Pathogen Interactions in Cereals Using RNA-seq.
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    Chapter 10 RNA-Seq Data Processing in Plant-Pathogen Interaction System: A Case Study
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    Chapter 11 Differential Expression Feature Extraction (DEFE): A Case Study in Wheat FHB RNA-Seq Data Analysis
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    Chapter 12 Proteomic Profiling of Host Response in the Cereal Crop Triticum aestivum to the Mycotoxin, 15-Acetyldeoxynivalenol, Produced by the Fungal Pathogen, Fusarium graminearum
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    Chapter 13 Quantitative Phosphoproteome Analysis of the Interaction Between Fusarium graminearum and Triticum aestivum
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    Chapter 14 Fatty Acid Profiling of Grapevine Extracellular Compartment
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    Chapter 15 Identifying Fungal Secondary Metabolites and Their Role in Plant Pathogenesis
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    Chapter 16 Eliciting Targeted Mutations in Medicago sativa Using CRISPR/Cas9-Mediated Genome Editing: A Potential Tool for the Improvement of Disease Resistance
Attention for Chapter 11: Differential Expression Feature Extraction (DEFE): A Case Study in Wheat FHB RNA-Seq Data Analysis
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Chapter title
Differential Expression Feature Extraction (DEFE): A Case Study in Wheat FHB RNA-Seq Data Analysis
Chapter number 11
Book title
Plant-Pathogen Interactions
Published by
Humana, New York, NY, May 2023
DOI 10.1007/978-1-0716-3159-1_11
Pubmed ID
Book ISBNs
978-1-07-163158-4, 978-1-07-163159-1
Authors

Youlian Pan, Anuradha Surendra, Ziying Liu, Thérèse Ouellet, Nora A. Foroud

Abstract

In differential gene expression data analysis, one objective is to identify groups of co-expressed genes from a large dataset in order to detect the association between such a group of genes and an experimental condition. This is often done through a clustering approach, such as k-means or bipartition hierarchical clustering, based on particular similarity measures in the grouping process. In such a dataset, the gene differential expression itself is an innate attribute that can be used in the feature extraction process. For example, in a dataset consisting of multiple treatments versus their controls, the expression of a gene in each treatment would have three possible behaviors, upregulated, downregulated, or unchanged. We present in this chapter, a differential expression feature extraction (DEFE) method by using a string consisting of three numerical values at each character to denote such behavior, i.e., 1 = up, 2 = down, and 0 = unchanged, which results in up to 3B differential expression patterns across all B comparisons. This approach has been successfully applied in many research projects, and among these, we demonstrate the strength of DEFE in a case study on RNA-sequencing (RNA-seq) data analysis of wheat challenged with the phytopathogenic fungus, Fusarium graminearum. Combinations of multiple schemes of DEFE patterns revealed groups of genes putatively associated with resistance or susceptibility to FHB.

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