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Soybean (Glycine max) SWEET gene family: insights through comparative genomics, transcriptome profiling and whole genome re-sequence analysis

Overview of attention for article published in BMC Genomics, July 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

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1 news outlet
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6 X users
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1 patent

Citations

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168 Dimensions

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152 Mendeley
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Title
Soybean (Glycine max) SWEET gene family: insights through comparative genomics, transcriptome profiling and whole genome re-sequence analysis
Published in
BMC Genomics, July 2015
DOI 10.1186/s12864-015-1730-y
Pubmed ID
Authors

Gunvant Patil, Babu Valliyodan, Rupesh Deshmukh, Silvas Prince, Bjorn Nicander, Mingzhe Zhao, Humira Sonah, Li Song, Li Lin, Juhi Chaudhary, Yang Liu, Trupti Joshi, Dong Xu, Henry T. Nguyen

Abstract

SWEET (MtN3_saliva) domain proteins, a recently identified group of efflux transporters, play an indispensable role in sugar efflux, phloem loading, plant-pathogen interaction and reproductive tissue development. The SWEET gene family is predominantly studied in Arabidopsis and members of the family are being investigated in rice. To date, no transcriptome or genomics analysis of soybean SWEET genes has been reported. In the present investigation, we explored the evolutionary aspect of the SWEET gene family in diverse plant species including primitive single cell algae to angiosperms with a major emphasis on Glycine max. Evolutionary features showed expansion and duplication of the SWEET gene family in land plants. Homology searches with BLAST tools and Hidden Markov Model-directed sequence alignments identified 52 SWEET genes that were mapped to 15 chromosomes in the soybean genome as tandem duplication events. Soybean SWEET (GmSWEET) genes showed a wide range of expression profiles in different tissues and developmental stages. Analysis of public transcriptome data and expression profiling using quantitative real time PCR (qRT-PCR) showed that a majority of the GmSWEET genes were confined to reproductive tissue development. Several natural genetic variants (non-synonymous SNPs, premature stop codons and haplotype) were identified in the GmSWEET genes using whole genome re-sequencing data analysis of 106 soybean genotypes. A significant association was observed between SNP-haplogroup and seed sucrose content in three gene clusters on chromosome 6. Present investigation utilized comparative genomics, transcriptome profiling and whole genome re-sequencing approaches and provided a systematic description of soybean SWEET genes and identified putative candidates with probable roles in the reproductive tissue development. Gene expression profiling at different developmental stages and genomic variation data will aid as an important resource for the soybean research community and can be extremely valuable for understanding sink unloading and enhancing carbohydrate delivery to developing seeds for improving yield.

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

Geographical breakdown

Country Count As %
Spain 1 <1%
Netherlands 1 <1%
China 1 <1%
Brazil 1 <1%
Unknown 148 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 45 30%
Researcher 23 15%
Student > Master 13 9%
Student > Bachelor 10 7%
Professor 7 5%
Other 18 12%
Unknown 36 24%
Readers by discipline Count As %
Agricultural and Biological Sciences 67 44%
Biochemistry, Genetics and Molecular Biology 31 20%
Business, Management and Accounting 3 2%
Social Sciences 2 1%
Computer Science 2 1%
Other 2 1%
Unknown 45 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 13 May 2022.
All research outputs
#1,960,186
of 23,653,133 outputs
Outputs from BMC Genomics
#513
of 10,778 outputs
Outputs of similar age
#25,728
of 264,092 outputs
Outputs of similar age from BMC Genomics
#15
of 249 outputs
Altmetric has tracked 23,653,133 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,778 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 95% 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 264,092 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 249 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.