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@article{e2998f49853c408b990a87ee3e8f2014,
title = "Review of combinations of experimental and computational techniques to identifyand understand genes involved in innate immunity and effector-triggered defence",
keywords = "Journal Article, Review",
author = "Stotz, {Henrik U} and {de Oliveira Almeida}, Rodrigo and Neil Davey and Volker Steuber and Valente, {Guilherme T}",
note = "Copyright © 2017. Published by Elsevier Inc.",
year = "2017",
month = "8",
doi = "10.1016/j.ymeth.2017.08.019",
journal = "Plant Methods",
issn = "1746-4811",
publisher = "BioMed Central",

}

RIS

TY - JOUR

T1 - Review of combinations of experimental and computational techniques to identifyand understand genes involved in innate immunity and effector-triggered defence

AU - Stotz,Henrik U

AU - de Oliveira Almeida,Rodrigo

AU - Davey,Neil

AU - Steuber,Volker

AU - Valente,Guilherme T

N1 - Copyright © 2017. Published by Elsevier Inc.

PY - 2017/8/31

Y1 - 2017/8/31

N2 - The innate immune system includes a first layer of defence that recognises conserved pathogen-associated molecular patterns that are essential for microbial fitness. Resistance (R) gene-based recognition of pathogen effectors, which function in modulation or avoidance of host immunity, activates a second layer of plant defence. In this review, experimental and computational techniques are considered to improve understanding of the plant immune system. Biocomputation contributes to discovery of the molecular genetic basis of host resistance against pathogens. Sequenced genomes have been used to identify R genes in plants. Resistance gene enrichment sequencing based on conserved protein domains has increased the number of R genes with nucleotide-binding site and leucine-rich repeat domains. Network analysis will contribute to an improved understanding of the innate immune system and identify novel genes for partial disease resistance. Machine learning algorithms are expected to become important in defining aspects of the immune system that are less well characterised, including identification of R genes that lack conserved protein domains.

AB - The innate immune system includes a first layer of defence that recognises conserved pathogen-associated molecular patterns that are essential for microbial fitness. Resistance (R) gene-based recognition of pathogen effectors, which function in modulation or avoidance of host immunity, activates a second layer of plant defence. In this review, experimental and computational techniques are considered to improve understanding of the plant immune system. Biocomputation contributes to discovery of the molecular genetic basis of host resistance against pathogens. Sequenced genomes have been used to identify R genes in plants. Resistance gene enrichment sequencing based on conserved protein domains has increased the number of R genes with nucleotide-binding site and leucine-rich repeat domains. Network analysis will contribute to an improved understanding of the innate immune system and identify novel genes for partial disease resistance. Machine learning algorithms are expected to become important in defining aspects of the immune system that are less well characterised, including identification of R genes that lack conserved protein domains.

KW - Journal Article

KW - Review

U2 - 10.1016/j.ymeth.2017.08.019

DO - 10.1016/j.ymeth.2017.08.019

M3 - Article

JO - Plant Methods

T2 - Plant Methods

JF - Plant Methods

SN - 1746-4811

ER -