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RNA-seq-based genome annotation and identification of long-noncoding RNAs in the grapevine cultivar ‘Riesling’

Overview of attention for article published in BMC Genomics, December 2017
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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 (88th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

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Title
RNA-seq-based genome annotation and identification of long-noncoding RNAs in the grapevine cultivar ‘Riesling’
Published in
BMC Genomics, December 2017
DOI 10.1186/s12864-017-4346-6
Pubmed ID
Authors

Zachary N. Harris, Laszlo G. Kovacs, Jason P. Londo

Abstract

The technological advances of RNA-seq and de novo transcriptome assembly have enabled genome annotation and transcriptome profiling in highly heterozygous species such as grapevine (Vitis vinifera L.). This work is an attempt to utilize a de novo-assembled transcriptome of the V. vinifera cultivar 'Riesling' to improve annotation of the grapevine reference genome sequence. Here we show that the transcriptome assembly of a single V. vinifera cultivar is insufficient for a complete genome annotation of the grapevine reference genome constructed from V. vinifera PN40024. Further, we provide evidence that the gene models we identified cannot be completely anchored to the previously published V. vinifera PN40024 gene models. In addition to these findings, we present a computational pipeline for the de novo identification of lncRNAs. Our results demonstrate that, in grapevine, lncRNAs are significantly different from protein coding transcripts in such metrics as length, GC-content, minimum free energy, and length-corrected minimum free energy. In grapevine, high-level heterozygosity necessitates that transcriptome characterization be based on cultivar-specific reference genome sequences. Our results strengthen the hypothesis that lncRNAs have thermodynamically different properties than protein-coding RNAs. The analyses of both coding and non-coding RNAs will be instrumental in uncovering inter-cultivar variation in wild and cultivated grapevine species.

X Demographics

X Demographics

The data shown below were collected from the profiles of 11 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 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 66 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 16 24%
Researcher 10 15%
Student > Ph. D. Student 10 15%
Student > Bachelor 7 11%
Student > Doctoral Student 5 8%
Other 7 11%
Unknown 11 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 35 53%
Biochemistry, Genetics and Molecular Biology 13 20%
Computer Science 3 5%
Business, Management and Accounting 1 2%
Pharmacology, Toxicology and Pharmaceutical Science 1 2%
Other 1 2%
Unknown 12 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 18 December 2017.
All research outputs
#2,247,405
of 23,932,490 outputs
Outputs from BMC Genomics
#620
of 10,862 outputs
Outputs of similar age
#51,024
of 444,632 outputs
Outputs of similar age from BMC Genomics
#19
of 212 outputs
Altmetric has tracked 23,932,490 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,862 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 94% 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 444,632 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 212 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 91% of its contemporaries.