↓ Skip to main content

Inferring defense-related gene families in Arabidopsis and wheat

Overview of attention for article published in BMC Genomics, December 2017
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (56th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

twitter
6 tweeters

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
24 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.
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.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Other 4 17%
Researcher 4 17%
Student > Master 4 17%
Student > Ph. D. Student 4 17%
Student > Postgraduate 2 8%
Other 4 17%
Unknown 2 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 54%
Biochemistry, Genetics and Molecular Biology 5 21%
Nursing and Health Professions 1 4%
Environmental Science 1 4%
Unknown 4 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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
#9,751,293
of 17,995,569 outputs
Outputs from BMC Genomics
#3,972
of 9,504 outputs
Outputs of similar age
#176,313
of 418,431 outputs
Outputs of similar age from BMC Genomics
#327
of 824 outputs
Altmetric has tracked 17,995,569 research outputs across all sources so far. This one is in the 45th percentile – i.e., 45% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,504 research outputs from this source. They receive a mean Attention Score of 4.4. This one has gotten more attention than average, scoring higher than 56% 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 418,431 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 56% of its contemporaries.
We're also able to compare this research output to 824 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 58% of its contemporaries.