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Re-annotation of the woodland strawberry (Fragaria vesca) genome

Overview of attention for article published in BMC Genomics, January 2015
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  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

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Title
Re-annotation of the woodland strawberry (Fragaria vesca) genome
Published in
BMC Genomics, January 2015
DOI 10.1186/s12864-015-1221-1
Pubmed ID
Authors

Omar Darwish, Rachel Shahan, Zhongchi Liu, Janet P Slovin, Nadim W Alkharouf

Abstract

Background Fragaria vesca is a low-growing, small-fruited diploid strawberry species commonly called woodland strawberry. It is native to temperate regions of Eurasia and North America and while it produces edible fruits, it is most highly useful as an experimental perennial plant system that can serve as a model for the agriculturally important Rosaceae family. A draft of the F. vesca genome sequence was published in 2011 [Nat Genet 43:223,2011]. The first generation annotation (version 1.1) were developed using GeneMark-ES+[Nuc Acids Res 33:6494,2005]which is a self-training gene prediction tool that relies primarily on the combination of ab initio predictions with mapping high confidence ESTs in addition to mapping gene deserts from transposable elements. Based on over 25 different tissue transcriptomes, we have revised the F. vesca genome annotation, thereby providing several improvements over version 1.1.ResultsThe new annotation, which was achieved using Maker, describes many more predicted protein coding genes compared to the GeneMark generated annotation that is currently hosted at the Genome Database for Rosaceae (http://www.rosaceae.org/). Our new annotation also results in an increase in the overall total coding length, and the number of coding regions found. The total number of gene predictions that do not overlap with the previous annotations is 2286, most of which were found to be homologous to other plant genes. We have experimentally verified one of the new gene model predictions to validate our results.ConclusionsUsing the RNA-Seq transcriptome sequences from 25 diverse tissue types, the re-annotation pipeline improved existing annotations by increasing the annotation accuracy based on extensive transcriptome data. It uncovered new genes, added exons to current genes, and extended or merged exons. This complete genome re-annotation will significantly benefit functional genomic studies of the strawberry and other members of the Rosaceae.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Uruguay 1 1%
China 1 1%
France 1 1%
Unknown 84 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 26%
Student > Ph. D. Student 16 18%
Student > Master 13 15%
Student > Doctoral Student 5 6%
Student > Bachelor 5 6%
Other 12 13%
Unknown 15 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 51 57%
Biochemistry, Genetics and Molecular Biology 10 11%
Computer Science 4 4%
Unspecified 2 2%
Engineering 2 2%
Other 6 7%
Unknown 14 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 09 February 2015.
All research outputs
#6,448,853
of 23,881,329 outputs
Outputs from BMC Genomics
#2,669
of 10,793 outputs
Outputs of similar age
#85,632
of 357,931 outputs
Outputs of similar age from BMC Genomics
#69
of 259 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 10,793 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done well, scoring higher than 75% 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 357,931 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 75% of its contemporaries.
We're also able to compare this research output to 259 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 73% of its contemporaries.