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Functional classification of rice flanking sequence tagged genes using MapMan terms and global understanding on metabolic and regulatory pathways affected by dxr mutant having defects in light…

Overview of attention for article published in Rice, April 2016
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  • Among the highest-scoring outputs from this source (#46 of 107)
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2 tweeters


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23 Mendeley
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Functional classification of rice flanking sequence tagged genes using MapMan terms and global understanding on metabolic and regulatory pathways affected by dxr mutant having defects in light response
Published in
Rice, April 2016
DOI 10.1186/s12284-016-0089-2
Pubmed ID

Anil Kumar Nalini Chandran, Gang-Seob Lee, Yo-Han Yoo, Ung-Han Yoon, Byung-Ohg Ahn, Doh-Won Yun, Jin-Hyun Kim, Hong-Kyu Choi, GynHeung An, Tae-Ho Kim, Ki-Hong Jung


Rice is one of the most important food crops for humans. To improve the agronomical traits of rice, the functions of more than 1,000 rice genes have been recently characterized and summarized. The completed, map-based sequence of the rice genome has significantly accelerated the functional characterization of rice genes, but progress remains limited in assigning functions to all predicted non-transposable element (non-TE) genes, estimated to number 37,000-41,000. The International Rice Functional Genomics Consortium (IRFGC) has generated a huge number of gene-indexed mutants by using mutagens such as T-DNA, Tos17 and Ds/dSpm. These mutants have been identified by 246,566 flanking sequence tags (FSTs) and cover 65 % (25,275 of 38,869) of the non-TE genes in rice, while the mutation ratio of TE genes is 25.7 %. In addition, almost 80 % of highly expressed non-TE genes have insertion mutations, indicating that highly expressed genes in rice chromosomes are more likely to have mutations by mutagens such as T-DNA, Ds, dSpm and Tos17. The functions of around 2.5 % of rice genes have been characterized, and studies have mainly focused on transcriptional and post-transcriptional regulation. Slow progress in characterizing the function of rice genes is mainly due to a lack of clues to guide functional studies or functional redundancy. These limitations can be partially solved by a well-categorized functional classification of FST genes. To create this classification, we used the diverse overviews installed in the MapMan toolkit. Gene Ontology (GO) assignment to FST genes supplemented the limitation of MapMan overviews. The functions of 863 of 1,022 known genes can be evaluated by current FST lines, indicating that FST genes are useful resources for functional genomic studies. We assigned 16,169 out of 29,624 FST genes to 34 MapMan classes, including major three categories such as DNA, RNA and protein. To demonstrate the MapMan application on FST genes, transcriptome analysis was done from a rice mutant of 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR) gene with FST. Mapping of 756 down-regulated genes in dxr mutants and their annotation in terms of various MapMan overviews revealed candidate genes downstream of DXR-mediating light signaling pathway in diverse functional classes such as the methyl-D-erythritol 4-phosphatepathway (MEP) pathway overview, photosynthesis, secondary metabolism and regulatory overview. This report provides a useful guide for systematic phenomics and further applications to enhance the key agronomic traits of rice.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 39%
Student > Master 4 17%
Researcher 3 13%
Student > Bachelor 1 4%
Librarian 1 4%
Other 2 9%
Unknown 3 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 52%
Biochemistry, Genetics and Molecular Biology 4 17%
Business, Management and Accounting 2 9%
Arts and Humanities 1 4%
Unknown 4 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 15 April 2016.
All research outputs
of 7,554,544 outputs
Outputs from Rice
of 107 outputs
Outputs of similar age
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Outputs of similar age from Rice
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Altmetric has tracked 7,554,544 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 107 research outputs from this source. They receive a mean Attention Score of 2.5. This one has gotten more attention than average, scoring higher than 50% 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 268,607 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.