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Inferring defense-related gene families in Arabidopsis and wheat

Overview of attention for article published in BMC Genomics, December 2017
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Title
Inferring defense-related gene families in Arabidopsis and wheat
Published in
BMC Genomics, December 2017
DOI 10.1186/s12864-017-4381-3
Pubmed ID
Authors

Rong-Cai Yang, Fred Y. Peng, Zhiqiu Hu

Abstract

A large number of disease resistance genes or QTLs in crop plants are identified through conventional genetics and genomic tools, but their functional or molecular characterization remains costly, labor-intensive and inaccurate largely due to the lack of deep sequencing of large and complex genomes of many important crops such as allohexaploid wheat (Triticum aestivum L.). On the other hand, gene annotation and relevant genomic resources for disease resistance and other defense-related traits are more abundant in model plant Arabidopsis (Arabidopsis thaliana). The objectives of this study are (i) to infer homology of defense-related genes in Arabidopsis and wheat and (ii) to classify these homologous genes into different gene families. We employed three bioinformatics and genomics approaches to identifying candidate genes known to affect plant defense and to classifying these protein-coding genes into different gene families in Arabidopsis. These approaches predicted up to 1790 candidate genes in 11 gene families for Arabidopsis defense to biotic stresses. The 11 gene families included ABC, NLR and START, the three families that are already known to confer rust resistance in wheat, and eight new families. The distributions of predicted SNPs for individual rust resistance genes were highly skewed towards specific gene families, including eight one-to-one uniquely matched pairs: Lr21-NLR, Lr34-ABC, Lr37-START, Sr2-Cupin, Yr24-Transcription factor, Yr26-Transporter, Yr36-Kinase and Yr53-Kinase. Two of these pairs, Lr21-NLR and Lr34-ABC, are expected because Lr21 and Lr34 are well known to confer race-specific and race-nonspecific resistance to leaf rust (Puccinia triticina) and they encode NLR and ABC proteins. Our inference of 11 known and new gene families enhances current understanding of functional diversity with defense-related genes in genomes of model plant Arabidopsis and cereal crop wheat. Our comparative genomic analysis of Arabidopsis and wheat genomes is complementary to the conventional map-based or marker-based approaches for identification of genes or QTLs for rust resistance genes in wheat and other cereals. Race-specific and race-nonspecific candidate genes predicted by our study may be further tested and combined in breeding for durable resistance to wheat rusts and other pathogens.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 17%
Other 4 14%
Researcher 4 14%
Student > Master 4 14%
Professor > Associate Professor 2 7%
Other 3 10%
Unknown 7 24%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 45%
Biochemistry, Genetics and Molecular Biology 5 17%
Nursing and Health Professions 1 3%
Environmental Science 1 3%
Unknown 9 31%
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 30 June 2018.
All research outputs
#15,687,628
of 23,312,088 outputs
Outputs from BMC Genomics
#6,752
of 10,742 outputs
Outputs of similar age
#269,827
of 441,968 outputs
Outputs of similar age from BMC Genomics
#136
of 225 outputs
Altmetric has tracked 23,312,088 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 10,742 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 28th percentile – i.e., 28% 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 441,968 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 225 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.