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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 (53rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

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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.

X Demographics

X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 112 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 107 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 27 24%
Student > Ph. D. Student 20 18%
Student > Master 14 13%
Student > Doctoral Student 8 7%
Student > Bachelor 6 5%
Other 17 15%
Unknown 20 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 59 53%
Biochemistry, Genetics and Molecular Biology 19 17%
Computer Science 5 4%
Engineering 2 2%
Pharmacology, Toxicology and Pharmaceutical Science 1 <1%
Other 4 4%
Unknown 22 20%
Attention Score in Context

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
#12,950,089
of 22,856,968 outputs
Outputs from BMC Genomics
#4,570
of 10,661 outputs
Outputs of similar age
#136,629
of 300,005 outputs
Outputs of similar age from BMC Genomics
#91
of 217 outputs
Altmetric has tracked 22,856,968 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,661 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 55% 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 300,005 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 53% of its contemporaries.
We're also able to compare this research output to 217 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 56% of its contemporaries.