Evidence map›Paper›PMID 27540739›Full record

ArticleRice (New York, N.Y.)2016

Updated Rice Kinase Database RKD 2.0: enabling transcriptome and functional analysis of rice kinase genes.

Anil Kumar Nalini Chandran, Yo-Han Yoo, Peijian Cao, Rita Sharma, Manoj Sharma, Christopher Dardick, Pamela C Ronald, Ki-Hong Jung

Open access · goldAbstract read
In one paragraph

Article in Rice (New York, N.Y.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Pooled it
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  6. Fast Track to Discover Novel Promoters in Rice.Plants (Basel, Switzerland) · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 5 institutions in 4 countries.

Anil Kumar Nalini ChandranGraduate School of Biotechnology & Crop Biotech Institute, Kyung Hee University, Yongin, 446-701, Republic of Korea.
Yo-Han YooGraduate School of Biotechnology & Crop Biotech Institute, Kyung Hee University, Yongin, 446-701, Republic of Korea.
Peijian CaoChina Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute, Zhengzhou, 450001, China.
Rita SharmaSchool of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi, 110067, India.
Manoj SharmaSchool of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India.
Christopher DardickAppalachian Fruit Research Station, United States Department of Agriculture - Agricultural Research Service, 2217 Wiltshire Road, Kearneysville, WV, 25442, USA.
Pamela C RonaldDepartment of Plant Pathology and the Genome Center, University of California, Davis, CA, 95616, USA. pcronald@ucdavis.edu.
Ki-Hong JungGraduate School of Biotechnology & Crop Biotech Institute, Kyung Hee University, Yongin, 446-701, Republic of Korea. khjung2010@khu.ac.kr.
Kyung Hee University · KRJawaharlal Nehru University · INTobacco Research Institute · CNUnited States Department of Agriculture · USUniversity of California, Davis · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundProtein kinases catalyze the transfer of a phosphate moiety from a phosphate donor to the substrate molecule, thus playing critical roles in cell signaling and metabolism. Although plant genomes contain more than 1000 genes that encode kinases, knowledge is limited about the function of each of these kinases. A major obstacle that hinders progress towards kinase characterization is functional redundancy. To address this challenge, we previously developed the rice kinase database (RKD) that integrated omics-scale data within a phylogenetics context.

resultsAn updated version of rice kinase database (RKD) that contains metadata derived from NCBI GEO expression datasets has been developed. RKD 2.0 facilitates in-depth transcriptomic analyses of kinase-encoding genes in diverse rice tissues and in response to biotic and abiotic stresses and hormone treatments. We identified 261 kinases specifically expressed in particular tissues, 130 that are significantly up- regulated in response to biotic stress, 296 in response to abiotic stress, and 260 in response to hormones. Based on this update and Pearson correlation coefficient (PCC) analysis, we estimated that 19 out of 26 genes characterized through loss-of-function studies confer dominant functions. These were selected because they either had paralogous members with PCC values of <0.5 or had no paralog.

conclusionCompared with the previous version of RKD, RKD 2.0 enables more effective estimations of functional redundancy or dominance because it uses comprehensive expression profiles rather than individual profiles. The integrated analysis of RKD with PCC establishes a single platform for researchers to select rice kinases for functional analyses.

Indexed as

Functional redundancyMeta-analysisMutant analysisPearson correlation coefficientPhylogenomicsRice kinase databaseTranscriptome

Identifiers

PMID27540739
PMCPMC4991984
OpenAlexW2514256085

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.