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Indica rice genome assembly, annotation and mining of blast disease resistance genes

Overview of attention for article published in BMC Genomics, March 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

Mentioned by

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6 tweeters

Citations

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28 Dimensions

Readers on

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94 Mendeley
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Title
Indica rice genome assembly, annotation and mining of blast disease resistance genes
Published in
BMC Genomics, March 2016
DOI 10.1186/s12864-016-2523-7
Pubmed ID
Authors

H. B. Mahesh, Meghana Deepak Shirke, Siddarth Singh, Anantharamanan Rajamani, Shailaja Hittalmani, Guo-Liang Wang, Malali Gowda

Abstract

Rice is a major staple food crop in the world. Over 80 % of rice cultivation area is under indica rice. Currently, genomic resources are lacking for indica as compared to japonica rice. In this study, we generated deep-sequencing data (Illumina and Pacific Biosciences sequencing) for one of the indica rice cultivars, HR-12 from India. We assembled over 86 % (389 Mb) of rice genome and annotated 56,284 protein-coding genes from HR-12 genome using Illumina and PacBio sequencing. Comprehensive comparative analyses between indica and japonica subspecies genomes revealed a large number of indica specific variants including SSRs, SNPs and InDels. To mine disease resistance genes, we sequenced few indica rice cultivars that are reported to be highly resistant (Tetep and Tadukan) and susceptible (HR-12 and Co-39) against blast fungal isolates in many countries including India. Whole genome sequencing of rice genotypes revealed high rate of mutations in defense related genes (NB-ARC, LRR and PK domains) in resistant cultivars as compared to susceptible. This study has identified R-genes Pi-ta and Pi54 from durable indica resistant cultivars; Tetep and Tadukan, which can be used in marker assisted selection in rice breeding program. This is the first report of whole genome sequencing approach to characterize Indian rice germplasm. The genomic resources from our work will have a greater impact in understanding global rice diversity, genetics and molecular breeding.

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 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 3 3%
China 1 1%
United States 1 1%
Unknown 89 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 23%
Student > Ph. D. Student 20 21%
Student > Master 15 16%
Student > Doctoral Student 7 7%
Student > Bachelor 6 6%
Other 13 14%
Unknown 11 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 54 57%
Biochemistry, Genetics and Molecular Biology 18 19%
Computer Science 6 6%
Engineering 2 2%
Social Sciences 1 1%
Other 1 1%
Unknown 12 13%

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 06 December 2017.
All research outputs
#6,346,756
of 12,259,388 outputs
Outputs from BMC Genomics
#3,010
of 7,187 outputs
Outputs of similar age
#117,850
of 311,244 outputs
Outputs of similar age from BMC Genomics
#88
of 213 outputs
Altmetric has tracked 12,259,388 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,187 research outputs from this source. They receive a mean Attention Score of 4.3. 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 311,244 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 61% of its contemporaries.
We're also able to compare this research output to 213 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 54% of its contemporaries.