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Optimizing de novo transcriptome assembly from short-read RNA-Seq data: a comparative study

Overview of attention for article published in BMC Bioinformatics, January 2011
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)

Mentioned by

twitter
15 tweeters

Citations

dimensions_citation
367 Dimensions

Readers on

mendeley
561 Mendeley
citeulike
4 CiteULike
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Title
Optimizing de novo transcriptome assembly from short-read RNA-Seq data: a comparative study
Published in
BMC Bioinformatics, January 2011
DOI 10.1186/1471-2105-12-s14-s2
Pubmed ID
Authors

Qiong-Yi Zhao, Yi Wang, Yi-Meng Kong, Da Luo, Xuan Li, Pei Hao

Abstract

With the fast advances in nextgen sequencing technology, high-throughput RNA sequencing has emerged as a powerful and cost-effective way for transcriptome study. De novo assembly of transcripts provides an important solution to transcriptome analysis for organisms with no reference genome. However, there lacked understanding on how the different variables affected assembly outcomes, and there was no consensus on how to approach an optimal solution by selecting software tool and suitable strategy based on the properties of RNA-Seq data.

Twitter Demographics

The data shown below were collected from the profiles of 15 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 561 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 14 2%
Germany 10 2%
Brazil 4 <1%
Mexico 3 <1%
Spain 3 <1%
Australia 3 <1%
France 3 <1%
India 2 <1%
Italy 2 <1%
Other 24 4%
Unknown 493 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 157 28%
Researcher 131 23%
Student > Master 80 14%
Student > Bachelor 40 7%
Student > Doctoral Student 29 5%
Other 97 17%
Unknown 27 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 345 61%
Biochemistry, Genetics and Molecular Biology 94 17%
Computer Science 33 6%
Environmental Science 7 1%
Engineering 7 1%
Other 28 5%
Unknown 47 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 02 September 2013.
All research outputs
#1,905,964
of 16,528,868 outputs
Outputs from BMC Bioinformatics
#704
of 5,960 outputs
Outputs of similar age
#13,765
of 126,914 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 16,528,868 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,960 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done well, scoring higher than 88% 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 126,914 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 89% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them